The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input but also how each neuron causally contributes to behaviour. This study demonstrates a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that is introduced is 'knockout training', which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. This approach was applied to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels, and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour. This model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, this framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain into the model (Cowley, 2024).

This study develop knockout training, a novel solution to identify a one-to-one mapping between internal units in a deep neural network (DNN) and real neurons in the brain of a fly. The model makes predictions about how neurons respond to sensory stimuli and drive behaviour. Although silencing each LC neuron type (LC neuron types receive input from the lobula and lobula plate in the optic lobe and send axons to optic glomeruli in the central brain) )on its own may have a small to medium effect on behaviour, the 1-to-1 network infers how the LC types work together as a population to drive the courtship behaviour of the male. The model extends beyond findings from direct recordings of LC neuron, even in behaving flies. The 1-to-1 network provides information on LC visual responses in freely behaving flies (not head-fixed, as is required for recordings) engaging in natural social interactions and can generate LC responses to any arbitrary visual stimulus. In fact, it was demonstrate that the 1-to-1 network predicts actual responses to stimuli that the model had not seen during training. The model also makes testable predictions about which combinations of LC types are both necessary and sufficient for specific courtship behaviours. A major new finding of this work is which and to what extent LC neuron types contribute to song production, an integral part of courtship guided by visual feedback. Given that the same visual stimulus sequence can drive multiple LC types, this neuron-to-behaviour relationship is not readily inferred from LC recordings alone. The 1-to-1 network is the first large-scale hypothesis of how the LC types work together to encode stimuli and contribute to behaviour; this model and code is shared with the community to inspire future experiments and models (Cowley, 2024).

The main conclusion of this study is that the complex courtship behaviour of the male relies on combinations of visual projections neurons-including those also involved in non-social behaviours. However, the extent to which other behaviours beyond those observed during courtship also rely on a population code is not yet known. Knockout training on the LC types could easily be applied to other visuomotor behaviours (for example, escape responses or flight) to make direct comparisons. Given the extent of interconnectivity between LC types and convergence of LC types onto common downstream cell types, it is posited that population coding for behaviour, particularly in natural contexts, might be the norm. By contrast, for behaviours that rely on quick and robust processing, such as escape from a predator, the arrangement of LC types into optic glomeruli may facilitate the fast readout of specific channels. One issue raised by the use of a multiplexed code is how the fly brain produces the correct behaviour at the correct time. For example, LPLC2 neurons synapse onto the giant fibre neuron to drive an escape take-off, but the 1-to-1 network predicts that this same cell type encodes female size and contributes to the forward velocity of the male during courtship; recent work has also found LPLC2 contributes to evasive flight turns. Future experiments are needed to understand how the same LC cell type can contribute to different behaviours in different contexts (Cowley, 2024).

This modelling approach comes with limitations. For example, if silencing an LC type does not lead to a noticeable change in behaviour, the 1-to-1 network cannot infer the tuning of that LC type. In addition, many silenced LC types resulted in stronger-not weaker-courtship, suggesting that these LC neurons may act partially as distractors to prevent relentless pursuit of the female. This approach also found some mismatches between real LC responses and the responses of the 1-to-1 network; although this may be owing to differences in internal state between freely moving males during natural courtship (training data for the model) versus head-fixed males passively viewing stimuli (neural recordings), training on neural data and behavioural data together may help to improve both neural and behavioural prediction. An experimental limitation of using natural behaviour arises because the statistics of the visual experience cannot be matched between LC-silenced and control males (for example, an LC9-silenced male spends much less time near the female); future experiments can use virtual reality or robotic females to present identical stimulus sequences to control and silenced males (Cowley, 2024).

Following recent studies using Deep Neural Networks (DNNs) to predict responses of visual neurons, this study used DNNs in the 1-to-1 network that are highly expressive function approximators but lack biological realism. The model-agnostic knockout training procedure can be used to train more biologically inspired models that incorporate constraints from the FlyWire connectome and emerging male brain wiring diagrams to include recurrent connections, lateral connections between LC types and delays. An intriguing future direction is to apply this framework to other bottlenecks within the Drosophila brain, such as the descending and ascending neurons that link the brain and nerve cord. and in more complex systems for which genetic control over cell types is available. This work shows that constraining models with causal perturbations of neurons during complex behaviour is an important ingredient in revealing the relationships between stimulus, neurons and behaviour (Cowley, 2024).

How neurons form appropriately sized dendritic fields to encounter their presynaptic partners is poorly understood. The Drosophila medulla is organized in layers and columns and innervated by medulla neuron dendrites and photoreceptor axons. This study shows that three types of medulla projection (Tm) neurons extend their dendrites in stereotyped directions and to distinct layers within a single column for processing retinotopic information. In contrast, the Dm8 amacrine neurons form a wide dendritic field to receive approximately 16 R7 photoreceptor inputs. R7- and R8-derived Activin selectively restricts the dendritic fields of their respective postsynaptic partners, Dm8 and Tm20, to the size appropriate for their functions. Canonical Activin signaling promotes dendritic termination without affecting dendritic routing direction or layer. Tm20 neurons lacking Activin signaling expand their dendritic fields and aberrantly synapse with neighboring photoreceptors. It is suggested that afferent-derived Activin regulates the dendritic field size of their postsynaptic partners to ensure appropriate synaptic partnership (Ting, 2014).

Studies of the past decade have revealed the molecular mechanisms by which photoreceptor and lamina axons target to specific medulla layers and restricting their axonal terminals to their retinotopic columns. However, obtaining comparable information on their synaptic partners, the dendrites of the medulla neurons, has been stymied by the morphological complexity of the dendrites and the lack of genetic and analytical tools. This study reports new techniques for visualizing and analyzing the dendritic structures of the medulla neurons. The dual-imaging technique reported in this study requires only regular confocal microscopes to generate 3D images of dendrites, and the registration method exploits the regular array structure of the medulla to permit the standardization of dendritic patterns for analyses. In addition, recent studies have begun to unravel the transcription programs that govern the development of diverse medulla cell types and serial EM reconstruction studies have illustrated their complex synaptic circuits. These advances are paving the way to studies on dendrite patterning in three-dimensional space and the establishment of synaptic partnerships between axons and dendrites in the Drosophila medulla (Ting, 2014).

Morphometric analyses of Tm neurons revealed dendritic properties distinct from those of the da sensory neurons in the PNS. Different classes of da neurons have distinguishable morphometric parameters, such as branching geometry, and their distinguishing dendritic morphological features are correlated with specific branching orders. In contrast, the three Tm neuron types have similar dendritic branching and terminating frequencies, and share similar dendritic geometrical properties. As such, the standard morphometrics are ineffective in differentiating Tm types. Instead, the type-specific dendritic attributes of Tm neurons, revealed by registration, are directly related to the layer and column structure of the medulla. These distinctions likely reflect the different functions of the dendrites of da and Tm neurons serve: da dendrites are free-ending sensory branches that tile the two-dimensional body wall, whereas Tm dendrites receive synaptic inputs from retinotopically directed afferents organized in layers and columns (Ting, 2014).

Examination of the dendritic attributes of Tm neurons provides insights into how dendritic morphology is achieved in these neurons. The axons of Tm9 and Tm2 were localized to the posterior side of their cognate columns, and their dendrites extended anteriorly to innervate their cognate columns, while Tm20 axons were at the anterior side of the column and their dendrites projected posteriorly. These type-specific axonal localizations are probably a consequence of their ontogeny: Tm9 and Tm2 axons are fasciculated, and these neurons are presumably derived from one or closely related cell lineages. Layer-specific termination of dendrites, the most prominent type-specific attribute of medulla neurons, matches the layer-specific termination of their presynaptic partners. Previous developmental studies have showed that columns and layers form as the different types of afferents sequentially innervate the medulla during development. The observation that type-specific dendritic attributes are directly related to medulla layers and columns thus led to the hypothesis that different types of medulla neurons respond differentially to various afferent-derived cues to pattern dendrites (Ting, 2014).

This study has revealed that Activin signaling regulates the dendritic field size of the columnar neuron Tm20 and the wide-field amacrine neuron Dm8. TGF-β family morphogens have been shown to regulate a broad range of neurodevelopmental processes, including proliferation and cell-fate determination during early stage of development as well as the promotion of axonal development through a non-canonical pathway involving Lim kinase. This study has found that Activin signals through the canonical pathway to regulate dendritic patterning in Tm20 and Dm8 neurons: the transcription factor dSmad2 was functionally required for Activin-dependent dendritic patterning, and the lack of Activin signaling caused a global dendritic phenotype (Ting, 2014).

The specificity of Activin signaling in regulating dendritic patterning is underscored by the distinct dendritic patterning defects, as well as the absence of cell-fate transformation and axonal targeting defects, observed in mutant neurons. Activin-signaling-deficient Tm20 neurons still expressed the appropriate transcription factors and markers, and projected their axons to the correct target layer in the lobula; mutant Dm8 elaborated their dendrites in the appropriate medulla layer M6. Instead, lack of Activin signaling reduced dendritic terminating frequency and expanded dendritic fields while the planar projection orientations and layer-specific terminations were largely unaffected. It is speculated that Activin signaling leads to the transcription regulation of as-yet-unidentified factor(s) that facilitate the termination of dendritic arbors (Ting, 2014).

Several lines of evidence suggest that Activin for Tm20 and Dm8 is, at least in part, provided by their respective presynaptic partners, R8 and R7. R7 and R8, but not R1-R6, expressed Activin during pupal stages, and RNAi-mediated knockdown of Activin in photoreceptors resulted in expanded dendritic fields in the Dm8 and Tm20 neurons. The cell-specific requirement for Activin is underscored by the observations that the genetic ablation of R7s phenocopied babo in Dm8 but not in Tm20 neurons, and that stalling R8 axons in the superficial M1 layer (thereby preventing the delivery of R8-derived Activin to the M3 layer) and Activin knock-down in R8s, disrupt the normal dendritic patterning of Tm20. It is noted that either Activin-RNAi or R8 stalling caused milder Tm20 dendritic phenotypes than did removing Baboon but that their combination did not significantly enhance Tm20 dendritic phenotypes. Thus, there is likely a secondary and partially redundant Activin source, for example, other medulla neurons, for Tm20. Nonetheless, the specific requirement of afferents indicates that R8-derived Activin cannot substitute for that from R7s, and vice-versa, even though these terminals reside only a few micrometers apart. It is therefore suggested that R7- and R8-derived Activin functions, in a short-range or layer-restricted fashion, to regulate the dendritic patterning of Dm8 and Tm20, respectively (Ting, 2014).

Photoreceptor-derived morphogens, such as Hedgehog, EGF, and Jelly Belly, have been shown to act, in an anterograde fashion early in development, to regulate the proliferation and differentiation of their target area. The current finding, that photoreceptor-derived Activin regulates the dendrite development of their synaptic targets, further suggests anterograde signaling as an effective mechanism for coordinating afferent-target development, even late in development. The wide-field neuron Dm8, which mediates innate UV preference, pools approximately 16 R7 inputs and relays the information to a few Tm5c neurons in the center of their dendritic field, while the Tm20 neurons form synapses in one-to-one correspondence with R8s, and process retinotopic information. Conceivably, the formation of appropriate dendritic field sizes and the correct synaptic partnership is critical for the functions of Tm20 and Dm8 neurons. It is reasoned that the dendritic tiling mediated by mutual repulsion, an important regulatory feature of the da neurons, alone is not suitable for restricting Tm20s' and Dm8s' dendritic fields because Tm20's dendrites arborized in three-dimensional space, and Dm8's dendrites overlap extensively with their neighbors'. The use of afferent-derived Activin to regulate dendritic patterning provides an adaptable and self-compensating mechanism for afferents to control the dendritic field sizes of their synaptic targets (Ting, 2014).

Establishing appropriate sizes and shapes of dendritic arbors is critical for proper wiring of the central nervous system. This study reports that Insulin-like Peptide 2 (DILP2) locally activates transiently expressed insulin receptors in the central dendrites of Drosophila Dm8 amacrine neurons to positively regulate dendritic field elaboration. DILP2 is expressed in L5 lamina neurons, which have axonal terminals abutting Dm8 dendrites. Proper Dm8 dendrite morphogenesis and synapse formation required insulin signaling through TOR (target of rapamycin) and SREBP (sterol regulatory element-binding protein), acting in parallel with previously identified negative regulation by Activin signaling to provide robust control of Dm8 dendrite elaboration. A simulation of dendritic growth revealed trade-offs between dendritic field size and robustness when branching and terminating kinetic parameters were constant, but dynamic modulation of the parameters could mitigate these trade-offs. It is suggested that antagonistic DILP2 and Activin signals from different afferents appropriately size Dm8 dendritic fields (Luo, 2020).

This study has shown that insulin/TOR/SREBP signaling positively regulates dendritic development. Insulin-like peptide DILP2 from L5 axons signals through insulin receptors on Dm8 dendrites to promote dendritic expansion via the canonical TOR pathway and SREBP. The roles of the insulin/TOR signaling pathway in cell growth and differentiation have been very well established. However, this is the first study to definitively demonstrate that insulin/TOR/SREBP signaling regulates dendritic field sizes during development. In Xenopus tadpoles, insulin receptor is expressed in retina and optic tectum, where it regulates synapse density and visual circuit function. However, the source of insulin or insulin-like growth factor (IGF) and the signaling pathway have not been described in developing Xenopus tectum. In vitro studies in cultured cells suggest that insulin/IGF signaling can not only regulate dendrite spine morphology and neurite outgrowth, but also function as a synaptic component at the postsynaptic density (PSD). It would be interesting to determine whether afferent-derived insulin/IGF is also utilized for dendritic development in vertebrate nervous systems (Luo, 2020).

The PI3K/AKT/TOR signaling pathway is known to mediate a broad spectrum of functions through two major complexes, TORC1 and TORC2, which act through different and varied effectors. In cultured hippocampal neurons, the PI3K-TOR pathway regulates dendrite size and branching patterns, presumably by interacting with the microtubule plus-end-tracking protein CLIP-170 to enhance crosstalk between CLIP-170 and the actin-binding protein IQGAP1 (Swiech, 2011). Dm8 dendritic development appears to involve the TORC1 complex and one of its downstream targets, SREBP. Removing the TORC2 component, Rictor, or TORC1 targets other than SREBP had no significant effect on Dm8 dendritic field size. In contrast, SREBP mutant Dm8 neurons had a reduced dendritic field size, and overexpression of SREBP rescued the dendritic phenotype of Tor mutants. While the possibility cannot be ruled out that other TOR targets play an additional or minor role, the results suggest that TOR works primarily through SREBP to regulate Dm8 dendritic development. Recent studies showed that SREBP enhances the transcription of cholesterol and fatty acid synthesis enzymes to regulate dendritic development of Da neurons in the peripheral nervous system. Thus, mounting evidence suggests that lipid synthesis may be a key regulatory point for controlling dendritic development in both central and peripheral nervous systems (Luo, 2020).

Using a novel receptor-based activity-dependent GRASP (R-synGRASP) technique, this study found synaptic defects between R7 photoreceptors and mutant Dm8 neurons that lack Insulin/TOR pathway signaling components. While the R-GRASP method revealed that Tor or chico mutant Dm8 dendrites formed membrane contacts with R7 axonal terminals in numbers that correlated with the dendritic field sizes, the results from the R-synGRASP method suggested these contacts do not constitute active synapses. Notably, Pten mutant Dm8 neurons appear to form more active synapses with peripheral R7 photoreceptors than central R7 photoreceptors, in contrast to wild types. At this stage of inquiry, the nature of such defects remains unclear. The presynaptic structures of R7 photoreceptors appear to be unaltered, as judged by the localization of the active zone marker, Bruchpilot. The level of histamine receptors on dendrites is significantly reduced in Tor- but not chico mutant Dm8 neurons, suggesting that reduced receptor levels might only partially account for the lack of R-synGRASP signal. It has been shown that insulin signaling regulates protein trafficking in various neuronal models, and mutations in SREBP and its upstream regulator easily shocked (eas) have been associated with excitability defects. Further studies will be required to resolve the nature of the synaptic defects in Tor and chico mutant Dm8 neurons (Luo, 2020).

Antagonistic regulation of Dm8 dendritic expansion by afferent-derived factors ensures robust size control A previous study showed that R7-derived Activin negatively regulates the expansion of Dm8 dendritic fields (Ting, 2014). This action is countered by positive regulation by L5-derived DILP2 revealed in this study. Despite the antagonistic effects on Dm8 dendritic expansion, both Activin and DILP2 are derived from afferents, delivered to the growth cones, and likely act on dendrites at close range. Both L3 and L5 lamina neurons express DILP2, but RNAi-mediated knockdown of dlip2 in L5 neurons alone is sufficient to reduce Dm8 dendritic expansion. This result suggests that L3-derived DILP2 is not necessary for normal development of Dm8 dendrites, even though the axonal terminals of L3 and L5 are only few micrometers apart. Similarly, Activin derived from R7 and R8 photoreceptors acts respectively on Dm8 and Tm20 neurons, even though the R7 and R8 axonal terminals are also only separated by a few micrometers (Ting, 2014). Furthermore, both Activin and DILP2 appear to have multiple functions in the developing optic lobe. In addition to its function on Dm8 dendritic development, R7-derived Activin functions in an autocrine fashion to control R7 axonal tiling. Glia-derived DILPs regulate lamina neuron differentiation, while L3 and L5 neurons express DILP2, which also affects photoreceptor axonal tiling. It is thus tempting to speculate that afferent-derived factors tend to function at short range on multiple targets during distinct developmental stages (Luo, 2020).

The results from genetic interaction experiments further argue that Activin and DILP2 signal in developing Dm8 dendrites through parallel pathways. Interestingly, the antagonistic regulation of Dm8 dendritic expansion by two afferent-derived factors is required for elaboration of appropriately large and robust Dm8 dendritic fields - removing both signaling events rendered highly variable sizes of Dm8 dendritic fields. While antagonistic regulation has been shown to control a wide range of biological processes, including gene and protein expression, this work provides the first example of antagonistic regulation for robust control of dendritic field sizes (Luo, 2020).

How does antagonistic regulation by insulin and Activin signaling ensure appropriate and robust sizes of Dm8 dendritic fields? Several lines of evidence suggest the involvement of spatiotemporal restriction of insulin signaling. Strong insulin receptor expression was detected in Dm8 neurons at early pupal stages (20-40 hr APF), when the cells had just begun to expand their dendritic arbors, but expression disappeared by late pupal stages (60 hr APF), before the dendrites expanded to adult sizes. Furthermore, the insulin receptors in developing Dm8 neurons were largely restricted to the central dendritic field, corresponding to one single column. Thus, insulin signaling likely occurs only over a short developmental period in response to insulin signal from one or a few L5 neurons to transiently promote Dm8 dendritic expansion during the early pupal stages (Luo, 2020).

To further understand how antagonistic regulation might control dendritic field sizes, simulations were carried out of Dm8 dendritic development using a kinetic Monte Carlo method. The variance of dendritic field sizes correlated with the difference between two key dendritic development parameters, branching and terminating rates: high branching and low terminating rates favor dendritic field expansion at the cost of high variability, while low branching and high terminating rates lead to small but robust dendritic field sizes. This study found that elaboration of large and consistent dendritic fields could be achieved by spatial and/or temporal segregation of growing and terminating phases. A previous study using in vivo imaging of dendritic dynamics showed that dendrites are elaborated at different rates according to the developmental stage. Thus, it is speculated that spatiotemporal restriction of insulin signaling, coupled with Activin-mediated negative regulation, allows Dm8 neurons to elaborate a large and consistent dendritic field. The large and robust sizes of Dm8 dendritic fields not only ensure a consistent receptive field for approximately 14 R7 photoreceptor inputs, but also can receive negative feedback signals to generate a center-surround signal structure (Luo, 2020).

Detecting motion is a feature of all advanced visual systems, nowhere more so than in flying animals, like insects. In flies, an influential autocorrelation model for motion detection, the elementary motion detector circuit (EMD), compares visual signals from neighboring photoreceptors to derive information on motion direction and velocity. This information is fed by two types of interneuron, L1 and L2, in the first optic neuropile, or lamina, to downstream local motion detectors in columns of the second neuropile, the medulla. Despite receiving carefully matched photoreceptor inputs, L1 and L2 drive distinct, separable pathways responding preferentially to moving 'on' and 'off' edges, respectively. Serial electron microscopy (EM) identifies two types of transmedulla (Tm) target neurons, Tm1 and Tm2, that receive apparently matched synaptic inputs from L2. Tm2 neurons also receive inputs from two retinotopically posterior neighboring columns via L4, a third type of lamina neuron. Light microscopy reveals that the connections in these L2/L4/Tm2 circuits are highly determinate. Single-cell transcript profiling suggests that nicotinic acetylcholine receptors mediate transmission within the L2/L4/Tm2 circuits, whereas L1 is apparently glutamatergic. It is proposed that Tm2 integrates sign-conserving inputs from neighboring columns to mediate the detection of front-to-back motion generated during forward motion (Takemura, 2011).

Given that both L2 and L4 express Choline acetyltransferase (Cha) and are thus genotypically qualified to synthesize acetylcholine and provide cholinergic input to Tm2, the expression of acetylcholine receptors in Tm2 was profiled. This proved more complex than for L2 and L4. In addition to Dα7 and Dβ1 nAcR shared with L2 and L4, Tm2 also expressed Dα1/2 and Dβ2 nAcR. The exclusive expression of nicotinic rather than muscarinic receptors (nAcR not mAcR) in Tm2 suggests that both L2 and L4 provide fast excitatory inputs to Tm2. It was also found that Tm2 expressed Cha but not VGlut, indicating that, like L2 and L4, Tm2 is also genotypically cholinergic. In summary, these data predict that both synaptic connections in the L2/L4/Tm2 network are mediated by excitatory acetylcholine systems, and therefore sign-conserving (Takemura, 2011).

While either the L1 or L2 channel alone can mediate rudimentary motion detection, each also responds differentially in walking flies, and in head-yaw assays the L2 pathway is preferentially tuned to front-to-back motion. Although the connections between L4 and L2 along the anteroposterior direction might account for this front-to-back preference, these connections are reciprocal so that information also flows from posterior to anterior, while L2's activity fails to reveal asymmetrical responses. Between L2's two targets, only Tm2 receives two additional L4 inputs from neighboring posterior columns; Tm1 does not. These L2/L4/Tm2 connections are highly determinate, underscoring a critical role in connecting neighboring L2 channels along the AP direction, in what is arguably the most important motion direction for flies since it occurs during forward flight. Interestingly, other flies have a Tm neuron closely resembling Drosophila's Tm2 morphologically, for example Tm1 in the calliphorid Phaenicia. This is proposed to receive L2 inputs, suggesting that an L2/L4/Tm2 network might be conserved in higher Diptera (Takemura, 2011).

Tm2 could conceivably serve as half of the EMD's multiplier stage, comparing the temporally delayed input from collateral L4s with the cognate signal from L2. However, electrophysiological investigations on calliphorid 'Tm1' neurons, which resemble morphologically Drosophila's Tm2, have yet to provide strong evidence for this role. An alternative interpretation is that the L2/L4/Tm2 network serves instead as a prefilter in the preprocessing stage while Tm2's output feeds into the multiplier stage. The topology and sign-conserving nature of L4/Tm2 connections suggest the spatial summation of neighboring visual signals, which could increase light sensitivity at the expense of spatial acuity. It has been suggested that under low luminance conditions, neighboring visual signals are pooled prior to their interaction at the multiplier stage, while at higher luminance levels nearest-neighbor interactions dominate motion detection. Alternatively, the L4/Tm2 connections could convert visual signals sampled from the hexagonal ommatidial array into an orthogonal coordinate upon which motion signals can be derived. Differentiating between these possibilities must await future investigations that combine genetic and electrophysiological approaches (Takemura, 2011).

Visual objects can be discriminated by static spatial features such as luminance or dynamic features such as relative movement. Flies track a solid dark vertical bar moving on a bright background, a behavioral reaction so strong that for a rigidly tethered fly, the steering trajectory is phase advanced relative to the moving bar, apparently in anticipation of its future position. By contrast, flickering bars that generate no coherent motion, or whose surface texture moves in the direction opposite to the bar generate steering responses that lag behind the stimulus. It remains unclear how the spatial properties of a bar influence behavioral response dynamics. A dark bar defined by its luminance contrast to the uniform background drives a co-directional steering response that is phase-advanced relative to the response to a textured bar defined only by its motion relative to a stationary textured background. The textured bar drives an initial contra-directional turn and phase-locked tracking. The qualitatively distinct response dynamics could indicate parallel visual processing of a luminance versus motion-defined object. Calcium imaging shows that T4/T5 motion detecting neurons are more responsive to a solid dark bar than a motion defined bar. Genetically blocking T4/T5 neurons eliminates the phase-advanced co-directional response to the luminance-defined bar, leaving the orientation response largely intact. It is concluded that T4/T5 neurons mediate a co-directional optomotor response to a luminance defined bar, thereby driving phase-advanced wing kinematics, whereas separate unknown visual pathways elicit the contra-directional orientation response (Keles, 2018).

Direction-selective neurons respond to visual motion in a preferred direction. They are direction-opponent if they are also inhibited by motion in the opposite direction. In flies and vertebrates, direction opponency has been observed in second-order direction-selective neurons, which achieve this opponency by subtracting signals from first-order direction-selective cells with opposite directional tunings. This study reports direction opponency in Drosophila that emerges in first-order direction-selective neurons, the elementary motion detectors T4 and T5. This opponency persists when synaptic output from these cells is blocked, suggesting that it arises from feedforward, not feedback, computations. These observations exclude a broad class of linear-nonlinear models that have been proposed to describe direction-selective computations. However, they are consistent with models that include dynamic nonlinearities. Simulations of opponent models suggest that direction opponency in first-order motion detectors improves motion discriminability by suppressing noise generated by the local structure of natural scenes (Badwan, 2019).

Nonlinear, multiplication-like operations carried out by individual nerve cells greatly enhance the computational power of a neural system, but understanding of their biophysical implementation is scant. This study pursued this problem in the Drosophila melanogaster ON motion vision circuit, in which the membrane potentials of direction-selective T4 neurons and of their columnar input elements were recorded in response to visual and pharmacological stimuli in vivo. Electrophysiological measurements and conductance-based simulations provide evidence for a passive supralinear interaction between two distinct types of synapse on T4 dendrites. This multiplication-like nonlinearity arises from the coincidence of cholinergic excitation and release from glutamatergic inhibition. The latter depends on the expression of the glutamate-gated chloride channel GluClα in T4 neurons, which sharpens the directional tuning of the cells and shapes the optomotor behaviour of the animals. Interacting pairs of shunting inhibitory and excitatory synapses have long been postulated as an analogue approximation of a multiplication, which is integral to theories of motion detection, sound localization and sensorimotor control (Groschner, 2022).

Nervous systems rely on nonlinearities to process information. A multiplication-like operation-possibly the simplest form of nonlinearity-is implicated in the transformation of eye-centric into head-centric coordinates, the localization of sound, the combination of multisensory signals and the detection of visual motion. The biophysical underpinnings of such an operation in a single neuron are by and large unclear. One exception is the looming detector of locusts, in which-just like on a slide rule-the sum of two logarithmically scaled signals is exponentially transformed into spike rates. Other multiplicative synaptic interactions involve NMDA receptors. Both mechanisms are contingent on threshold-like nonlinearities in the current-voltage relationships of ion channels: the gating of tetrodotoxin-sensitive sodium channels in the former and the magnesium block of NMDA receptors in the latter case. This study describes a multiplication-like nonlinearity that is independent of thresholds (Groschner, 2022).

Using the visual circuit of the fruit fly as an example, this study took advantage of the neurons' compact sizes, their known connectivity and an ability to manipulate them genetically to study the biophysical basis of the multiplication step in a Hassenstein-Reichardt detector. The membrane potentials of ON motion-sensitive T4 neurons and of their columnar input elements were measured in response to a defined set of visual stimuli. Measurements of both pre- and postsynaptic voltages obviated the need for assumptions regarding the temporal dynamics of input signals when modelling the detector's output. The voltage responses of T4 neurons were reproduced rather faithfully by a passive conductance-based model. Discrepancies between simulation and reality could be due to selective synaptic delays or the 15% of dendritic inputs from wide-field TmY15 and CT1 neurons, which were not taken into account. In the model, as in the data, the supralinearity arises from the coincidence of excitation and release from shunting inhibition. Such 'multiplicative disinhibition' constitutes the inverse operation of divisive inhibition. It is free from the voltage dependence that often limits threshold-based systems and less sensitive to changing signal amplitudes. More broadly, theory invokes multiplication as a strategy to gate information flow. The passive biophysical mechanism that is proposed could lend itself to other systems, such as the logical conjunction of chemosensory signals or the gating of cortical afferent. Motion vision in flies may provide one of many cases of multiplicative disinhibition (Groschner, 2022).

A neuron is conventionally regarded as a single processing unit. It receives input from one or several presynaptic cells, transforms these signals, and transmits one output signal to its postsynaptic partners. Exceptions exist: amacrine cells in the mammalian retina or interneurons in the locust mesothoracic ganglion are thought to represent many electrically isolated microcircuits within one neuron. An extreme case of such an amacrine cell has recently been described in the Drosophila visual system. This cell, called CT1, reaches into two neuropils of the optic lobe, where it visits each of 700 repetitive columns, thereby covering the whole visual field. Due to its unusual morphology, CT1 has been suspected to perform local computations, but this has never been proven. Using 2-photon calcium imaging and visual stimulation, this study found highly compartmentalized retinotopic response properties in neighboring terminals of CT1, with each terminal acting as an independent functional unit. Model simulations demonstrate that this extreme case of compartmentalization is at the biophysical limit of neural computation (Meier, 2019).

The visual system of the fruit fly comprises the eye with about 700 facets and the optic lobe. The latter consists of four layers of retinotopically organized neuropils called lamina, medulla, lobula, and lobula plate. Within each column of both medulla and lobula, CT1 receives input from a number of local interneurons and makes output synapses onto elementary motion-detecting T4 and T5 cells. T4 cells are sensitive to bright moving edges and have their dendrites in the most proximal layer of the medulla. T5 cells respond preferentially to dark moving edges and extend their dendrites into the posterior layer of the lobula. Both come in four subtypes, one for each of the four cardinal directions of visual motion. For each subtype, the synapses of CT1 are located precisely on the null direction side of T4 and T5 dendrites. Therefore, CT1 has been speculated to be of functional relevance for creating direction selectivity within T4 and T5, in particular because it receives excitatory, cholinergic input and releases the typically inhibitory neurotransmitter GABA, which may introduce a sign inversion. To support motion detection, however, the columnar processes of CT1 must act as spatially independent, local processing units. Curiously, these structures are connected to major branches via ultra-thin, 0.1-µ-wide processes that form loops and, thus, are about 10 times longer than needed to bridge the distance between neighboring columns (Meier, 2019).

To measure the receptive fields of individual processes in CT1, calcium levels were used - as a proxy for neural activity - recorded under a 2-photon microscope during visual stimulation with a white-noise bar pattern. By correlating the fluorescence signal of a given neural process with the stimulus intensity at each location in space and time, a spatiotemporal receptive field was obtained. As shown in a representative experiment, each compartment of CT1 has its own confined receptive field with a small half-width, comparable to the size of the interommatidial angle of the fly eye. Furthermore, receptive field centers are displaced with respect to neighboring compartments. Processes within the medulla are ON-sensitive and the ones in the lobula are OFF-sensitive, just like their postsynaptic partner cells, T4 and T5. To assess the spatiotemporal response properties more closely, such measurements were taken from many compartments of CT1 cellsm and their receptive fields were averaged after alignment to their center. The spatial profile of the processes in the medulla reveal an ON-center with a full-width at half maximum of 9.1° and temporal band-pass characteristics. The spatial profile in the lobula reveals an OFF-center with a half-width of 7.2°. Receptive field sizes determined at different locations in visual space were rather similar. To test the selectivity of the compartments to contrast polarity as well as their potential direction selectivity, bright and dark edges moving in 12 directions were used. CT1 compartments respond selectively to either moving ON (medulla) or OFF (lobula) edges, irrespective of the direction of motion (Meier, 2019).

This extreme compartmentalization requires strong electrical isolation of neural processes within neighboring columns. Given the minute absolute distances in the fruit fly brain, it was wondered whether this is at all possible. A realistic, electrically passive compartmental model of CT1 was built, based on electron microscopy reconstructions. It consists of 11 terminals in the medulla that are connected via thin, looping branches to several thicker parental branches. Using biophysically plausible membrane parameters and direct current (DC) injections into one columnar compartment at a time, the distribution of membrane voltage across the whole model cell was investigated. Irrespective of the stimulated column, the voltage drops steeply to a level of around 20% in all remaining compartments, indicating strong electrical isolation of each CT1 terminal from its neighbors. As a control, the same current injection into the tip of the thick main branch was calculated. As expected, the voltage spreads more uniformly under these conditions and drops to only about 50% of the peak voltage was examined. Next, how the compartmentalization of the model cell depends on the choice of parameters for the axial (Ra) and transmembrane (Rm) resistance. To this end, current injections into one compartment were simulated, and the average relative membrane potential was measured in all other terminals while varying the values for Ra and Rm. The model only retains a high degree of compartmentalization if Ra is rather high or if Rm is low. How this translates into receptive field properties of an isolated CT1 terminal, as determined in imaging experiments, was examined. To test this, synaptic input into one compartment was simulated, assuming three Gaussian input signals of 7° half-width, separated by 5°. Testing the receptive field of the central of three columnar CT1 compartments, a spatial receptive field half-width was found of 9.6°. Hence, the receptive field is largely determined by the spatial sensitivity of the input and closely replicates the experimental findings. In order to detect visual movement, local motion detectors must receive input signals that are separated in space and in time. To probe whether CT1 terminals fulfill this requirement, the model cell was presented with a sine grating. When the stimulus was stationary, it was observed that the membrane potential in neighboring terminals changed according to the phase of the grating, further corroborating the results from the receptive field analysis. Once the grating was moved in one direction, voltage responses of individual terminals were modulated robustly at the temporal frequency of the stimulus. Critically for motion detection, phase delays between signals were preserved. This illustrates that CT1 terminals retain information about the spatial as well as the temporal characteristics of the input signal and further demonstrates their usefulness in the motion detection pathway (Meier, 2019).

Using functional 2-photon calcium imaging and anatomically realistic compartmental modeling, strong evidence is provided for an extreme case of electrically isolated sub-compartments in a large-field amacrine cell, which functions as many hundred independent processing units. This has important implications for the potential role of CT1 in motion processing in the visual system of Drosophila. Recently, several studies have demonstrated that the computation of direction selectivity in the fly brain is based on both preferred direction enhancement and null direction suppression. As a result, presynaptic elements to the elementary motion detectors T4 and T5 must contribute both excitation (on the preferred side) and inhibition (on the null side). Although evidence for inhibitory, columnar inputs to T4 has been shown before, the only cell type in the OFF pathway potentially providing null direction suppression is the GABAergic wide-field neuron CT1. In this study, it was demonstrated that CT1 can indeed perform local computations and is therefore a promising candidate for the direction selectivity circuit, whose exact role has to be further investigated in the future (Meier, 2019).

In these experiments, changes in calcium concentration were measured, whereas in the modeling section, membrane voltage distributions were calculated. Potential discrepancies between these two readouts of neural activity need to be kept in mind. It is possible that the transformation from underlying membrane polarization to voltage-gated calcium dynamics either enhances or reduces compartmentalization between terminals through compressive or expansive channel activation functions. Any model of this transformation would require knowledge of channel properties and distribution that is currently not available. However, a recent study has shown that, if anything, calcium signaling tends to be more compartmentalized than voltage in the fly visual system. Finally, because synaptic output depends on calcium concentration at release sites, the strong compartmentalization that was observe in the calcium measurements should be of immediate functional relevance (Meier, 2019).

In summary, these results show that, in order to ensure an electrical decoupling of the different processes of an amacrine cell in the small brain of the fly, connecting branches have to be long and thin. Additionally, either specific axial resistance has to be unusually high or membrane resistance must be at the low end of the spectrum. This demonstrates that such an extreme case of compartmentalization is at the biophysical limit of neural computation (Meier, 2019).

Neurotransmitter receptors and ion channels shape the biophysical properties of neurons, from the sign of the response mediated by neurotransmitter receptors to the dynamics shaped by voltage-gated ion channels. Therefore, knowing the localizations and types of receptors and channels present in neurons is fundamental to understanding of neural computation. This study developed two approaches to visualize the subcellular localization of specific proteins in Drosophila: the flippase-dependent expression of GFP-tagged receptor subunits in single neurons and 'FlpTag', a versatile new tool for the conditional labelling of endogenous proteins. Using these methods, the subcellular distribution of the receptors GluClα, Rdl, and Dα7 and the ion channels Para and Ih in motion-sensing T4/T5 neurons of the Drosophila visual system was investigated. A strictly segregated subcellular distribution of these proteins a sequential spatial arrangement of glutamate, acetylcholine, and GABA receptors was discovered along the dendrite that matched the previously reported EM-reconstructed synapse distributions (Fendl, 2020).

How neural circuits implement certain computations in order to process sensory information is a central question in systems neuroscience. In the visual system of Drosophila, much progress has been made in this direction: numerous studies examined the response properties of different cell-types in the fly brain and electron microscopy studies revealed the neuronal wiring between them. However, one element crucial to understanding is still missing; these are the neurotransmitter receptors used by cells at the postsynaptic site. This knowledge is essential since neurotransmitters and corresponding receptors define the sign and the time-course of a connection, that is whether a synapse is inhibitory or excitatory and whether the signal transduction is fast or slow. The same neurotransmitter can act on different receptors with widely differing effects for the postsynaptic neuron. Glutamate for instance is mainly excitatory, however, in invertebrates it can also have inhibitory effects when it acts on a glutamate-gated chloride channel, known as GluClα. Recently, it has also been shown that acetylcholine, usually excitatory, might also be inhibitory in Drosophila, if it binds to the muscarinic mAChR-A receptor. Hence, knowledge inferring the type of transmitter receptor at a synapse is essential for understanding of the way neural circuits process information (Fendl, 2020).

Moreover, voltage-gated ion channels shape synaptic transmission and the integration of synaptic inputs by defining the membrane properties of every neural cell type. The voltage-gated calcium channel cacophony, for instance, mediates influx of calcium ions that drives synaptic vesicle fusion at presynaptic sites. Voltage-gated sodium channels like Paralytic (Para) are important for the cell's excitability and the generation of sodium-dependent action potentials. The voltage-gated channel Ih influences the integration and kinetics of excitatory postsynaptic potentials. However, only little is known about how these channels are distributed in neurons and how this shapes the neural response properties (Fendl, 2020).

One of the most extensively studied neural circuits in Drosophila is the motion vision pathway in the optic lobe and the underlying computation for direction-selectivity. The optic lobe comprises four neuropils: lamina, medulla, lobula, and lobula plate. As in the vertebrate retina, the fly optic lobe processes information in parallel ON and OFF pathways. Along the visual processing chain, T4/T5 neurons are the first neurons that respond to visual motion in a direction selective way. T4 dendrites reside in layer 10 of the medulla and compute the direction of moving bright edges (ON-pathway). T5 dendrites arborize in layer 1 of the lobula and compute the direction of moving dark edges (OFF-pathway). The four subtypes of T4/T5 neurons (a, b, c, d), project axon terminals to one of the four layers in the lobula plate, each responding only to movement in one of the four cardinal directions, their preferred direction (Fendl, 2020).

How do T4/T5 neurons become direction-selective? Both T4 and T5 dendrites span around eight columns collecting signals from several presynaptic input neurons, each of which samples information from visual space in a retinotopic manner. The functional response properties of the presynaptic partners of T4/T5 have been described in great detail along with their neurotransmitter phenotypes. T4 dendrites receive glutamatergic, GABAergic and cholinergic input, whereas T5 dendrites receive GABAergic and cholinergic input only. These input synapses are arranged in a specific spatial order along T4/T5 dendrites (Fendl, 2020).

Which receptors receive this repertoire of different neurotransmitters at the level of T4/T5 dendrites? Recently, several RNA-sequencing studies described the gene expression pattern of nearly all cell-types in the optic lobe of the fruit fly including T4/T5 neurons. T4/T5 neurons were found to express numerous receptor subunits of different transmitter classes and voltage-gated ion channels at various expression strengths. However, RNA-sequencing studies do not unambiguously answer the above question for two reasons: mRNA and protein levels are regulated in complex ways via post-transcriptional, translational, and protein degradation mechanisms making it difficult to assign protein levels to RNA levels. Secondly, standard RNA-sequencing techniques cannot provide spatial information about receptor localizations, hence, they are not sufficient to conclude which transmitter receptors receive which input signal. Both shortcomings could in principle be overcome by antibody staining since immunohistochemical techniques detect neurotransmitter receptors at the protein level and preserve spatial information. However, high-quality antibodies are not available for every protein of interest and may have variable affinity due to epitope recognition. Furthermore, labeling ion channels via antibodies and ascribing expression of a given channel to a cell-type in dense neuronal tissue remains challenging. The disadvantages of the above techniques highlight the need for new strategies for labeling neurotransmitter receptors in cell types of interest (Fendl, 2020).

This study employed existing and generated new genetic methods to label and visualize ion channels in Drosophila. For endogenous, cell-type-specific labeling of proteins, a generalizable method called FlpTag was developed that expresses a GFP-tag conditionally. Using these tools, the subcellular distribution was determined of the glutamate receptor subunit GluClα, the acetylcholine receptor subunit Dα7, and the GABA receptor subunit Rdl in motion-sensing T4/T5 neurons. These receptor subunits were differentially localized between dendrites and axon terminals. Along the dendrites of individual T4/T5 cells, the receptor subunits GluClα, Rdl, and Dα7 reveal a distinct distribution profile that can be assigned to specific input neurons forming synapses in this area. Furthermore, it was demonstrated the generalizability of the FlpTag approach by generating lines for the metabotropic GABA receptor subunit Gaba-b-r1 and the voltage-gated ion channels para and Ih. The strategies described in this study can be applied to other cells as well as other proteins to reveal the full inventory and spatial distribution of the various ion channels within individual neurons (Fendl, 2020).

Neurotransmitter receptors are essential neuronal elements that define the sign and temporal dynamics of synaptic connections. For understanding of complex neural circuits, it is indispensable to examine which transmitter receptor types are used by the participating neurons and to which compartment they localize. This study developed FlpTag, a generalizable method for endogenous, cell-type-specific labeling of proteins. Alongside several GFP-tagged UAS-lines, the newly developed FlpTag lines were developed to explore the distribution of receptor subunits GluClα, Rdl, Dα7, Gaba-b-r1 and voltage-gated ion channels Para and Ih in motion-sensing T4/T5 neurons of the visual system of Drosophila. These ion channels were found to be localized to either the dendrite, the axonal fiber or the axon terminal. Even at the level of individual dendrites, GluClα, Rdl and Dα7 were differentially distributed precisely matching the locations where T4 and T5 neurons sample signals from their glutamatergic, cholinergic, or GABAergic input neurons, respectively (Fendl, 2020).

Working with Drosophila as model organism bears some unrivaled advantages when it comes to genetic tools. The MiMIC and FlyFos libraries, for instance, are large-scale approaches of enormous value for the fly community as they provide GFP-tagged protein lines for thousands of Drosophila genes including several neurotransmitter receptors and voltage-gated ion channels. Recently, Kondo expanded these existing libraries with T2A-Gal4 insertions in 75 neurotransmitter receptor genes that can also be exchanged by the fluorescent protein tag Venus (Kondo, 2020). While all these approaches tag genes at their endogenous locus, none of them are conditional, for example they cannot be applied in a cell-type-specific manner. Hence, ascribing the expression of the pan-neuronally tagged proteins to cell-types of interest are challenging in dense neuronal tissue (Fendl, 2020).

To overcome these difficulties, two conditional strategies were used for the investigation of membrane protein localizations in the cell types of interest, T4 and T5 neurons. First, GFP-tagged UAS-lines were developed for GluClα and Rdl, and an existing UAS-Dα7::GFP line was tested. As stated above, aberrant localization of overexpressed proteins can occur, however, this is not always the case. Overexpression of UAS-GluClα::GFP shows a similar receptor localization pattern as both MiMIC and FlpTag endogenous lines, thus, validating the use of UAS-GluClα::GFP for studying receptor distribution. Additionally, previous studies reported that the UAS-Dα7::GFP line showed proper localization of the acetylcholine receptor to endogenous synapses when compared to antibody stainings or endogenous Bruchpilot (Brp) puncta. This study confirmed confirmed this finding and further showed that Dα7::GFP presumably localizes only to cholinergic synapses. Overexpressing Dα7::GFP in a medulla neuron that is devoid of endogenous Dα7 demonstrated that Dα7::GFP localized to apparent cholinergic synapses. Hence, the UAS-Dα7::GFP line can be used to study the distribution of cholinergic synapses, but not the exact composition of cholinergic receptor subunits. A recent study showed that quantitatively the levels of the postsynaptic density protein PSD95 change when overexpressed, but qualitatively the localization is not altered. Altogether, this suggests that tagged overexpression lines can be used for studying protein localizations, but they have to be controlled carefully and drawn conclusions might be different for every line (Fendl, 2020).

Ideally, a tool for protein tagging should be both endogenous and conditional. This can be achieved by introducing an FRT-flanked STOP cassette upstream of the gene of interest which was engineered with an epitope tag or fluorescent protein. Only upon cell-type specific expression of Flp, the tagged protein will be expressed in a cell-type specific manner. This genetic strategy was utilized by two independent studies to label the presynaptic protein Brp, the histamine channel Ort and the vesicular acetylcholine transporter VAChT. Recently, a new approach based on the split-GFP system was utilized for endogenous, conditional labeling of proteins in two independent studies. However, all these aforementioned approaches are not readily generalizable and easily applicable to any gene of interest (Fendl, 2020).

The FlpTag strategy presented in this study overcomes these caveats by allowing for endogenous, conditional tagging of proteins and by offering a generalizable toolbox for targeting many genes of interest. Similar to the conditional knock-out tools FlpStop and FlipFlop, FlpTag utilizes a FLEx switch to conditionally control expression of a reporter gene, in this case GFP. Likewise, FlpTag also easily integrates using the readily available intronic MiMIC insertions. This study attempted to generate FlpTag lines for six genes, GluClα, Rdl, Dα7, Gaba-b-r1, para and Ih. Four out of these six lines yielded conditional GFP-tagged protein lines (GluClα, Gaba-b-r1, para, Ih). The FlpTag cassette was injected in MI02620 for Rdl and MI12545 for Dα7, but no GFP expression was detected across the brain. The MiMiC insertion sites used for Rdl and Dα7 seem to be in a suboptimal location for tagging the protein (Fendl, 2020).

As of now, there are MiMIC insertions in coding introns for more than 2800 genes available, which covers approximately 24% of neuronal genes. Additionally, the attP insertion sites generated in the study by Kondo provide possible landing sites for the FlpTag cassette for 75 neurotransmitter receptor genes (Kondo, 2020). Transmembrane proteins such as neurotransmitter receptors form complex 3D structures making fluorescent tagging especially difficult. Neither the MiMIC insertion sites, nor the target sites of the Kondo study at the C-terminus of several transmitter receptor genes, ensure a working GFP-tagged protein line. For genes of interest lacking a suitable MiMIC insertion site a homology directed repair (HDR) cassette was generated that utilizes CRISPR/Cas9-mediated gene editing to integrate the FlpTag cassette in any desired gene locus. The plasmid consists of the FlpTag cassette flanked by multiple cloning sites for the insertion of homology arms (HA). Through HDR the FlpTag cassette can be knocked-in into any desired locus. Taken together, the FlpTag cassette is a generalizable tool that can be integrated in any available attP-site in genes of interest or inserted by CRISPR-HDR into genes lacking attP landing sites. This allows for the investigation of the endogenous spatial distributions of proteins, as well as the correct temporal dynamics of protein expression (Fendl, 2020).

Further, the FlyFos project demonstrated that most fly lines with an extra copy of GFP-tagged protein-coding genes worked normally and GFP-tagged proteins could be imaged in living fly embryos and pupae. In principle, live-imaging of the GFP-tagged lines that were created could be performed during different developmental stages of the fruit fly. In general, the tools generated in this study can be used as specific postsynaptic markers, visualizing glutamatergic, GABAergic, and cholinergic synapses with standard confocal light microscopy. This extends the existing toolbox of Drosophila postsynaptic markers for studying the localization and development of various types of synapses (Fendl, 2020).

T4/T5 neurons combine spatiotemporal input from their presynaptic partners, leading to selective responses to one of the four cardinal directions. Numerous studies investigated the mechanisms underlying direction-selective responses in T4/T5 neurons, yet the computation is still not fully understood. At an algorithmic level, a three-arm detector model is sufficient to describe how direction-selective responses in T4/T5 neurons arise. This model relies on the comparison of signals originating from three neighboring points in space via a delay-and-compare mechanism. The central arm provides fast excitation to the neuron. While one flanking arm amplifies the central signal for stimuli moving along the preferred direction, the other inhibits the central signal for stimuli moving along the null direction of the neuron. Exploring the neurotransmitter receptors and their distribution on T4/T5 dendrites allows defining the sign as well as the temporal dynamics of some of the input synapses to T4/T5 (Fendl, 2020).

According to the algorithmic model, an excitatory, amplifying input signal on the distal side of T4/T5 dendrites was expected. This study found that T4 cells receive an inhibitory, glutamatergic input from Mi9 via GluClα, which, at first sight, seems to contradict expectation. However, since Mi9 has an OFF-center receptive field, this glutamatergic synapse will invert the polarity from Mi9-OFF to T4-ON. Theoretically, in darkness, Mi9 inhibits T4 via glutamate and GluClα, and this inhibition is released upon an ON-edge moving into its receptive field. The concomitant closure of chloride channels and subsequent increased input resistance in T4 cells results in an amplification of a subsequent excitatory input signal from Mi1 and Tm3. As shown by a recent modeling study, this biophysical mechanism can indeed account for preferred direction enhancement in T4 cells (Borst, 2018). Some studies failed to detect preferred direction enhancement in T4/T5 neurons and they proposed that the enhanced signal in PD seen in GCaMP recordings could be a result from a non-linear calcium-to-voltage transformation. If this was really the case, the role of Mi9 and GluClα must be reconsidered and future functional experiments will shed light onto this topic (Fendl, 2020).

Nevertheless, Strother (2017) showed that the RNAi- knock-down of GluClα in T4/T5 neurons leads to enhanced turning responses on the ball set-up for faster speeds of repeating ON and OFF edges (Strother, 2017). Although this observation cannot answer the question about preferred direction enhancement in T4 cells, it indicates that both T4 and T5 receive inhibitory input and that removal of such create enhanced turning responses at the behavioral level. In line with these observations, the glutamate receptor GluClα was also found in T4/T5 axon terminals. A possible functional role of these inhibitory receptors in the axon terminals could be a cross-inhibition of T4/T5 cells with opposite preferred directions via lobula plate intrinsic neurons (LPis). Glutamatergic LPi neurons are known to receive a cholinergic, excitatory signal from T4/T5 neurons within one layer and to inhibit lobula plate tangential cells, the downstream postsynaptic partners of T4/T5 neurons, via GluClα in the adjacent oppositely tuned layer. This mechanism induces a motion opponent response in lobula plate tangential cells and increases their flow-field selectivity. In addition, LPi neurons could also inhibit T4/T5 neurons presynaptically at their axon terminals via GluClα in order to further sharpen the flow-field selectivity of lobula plate tangential cells. Taken together, exploring the subcellular distribution of GluClα in T4/T5 neurons highlights its differential functional roles in different parts of these cell types (Fendl, 2020).

Secondly, the Dα7 signal in the center of T4/T5 dendrites discovered in this study, corresponds to ionotropic, cholinergic input from Mi1 and Tm3 for T4, and Tm1, Tm2 and Tm4 for T5. These signals correspond to the central, fast, excitatory arm of the motion detector model. As T4 and T5 express a variety of different ACh receptor subunits, the exact subunit composition and underlying biophysics of every cholinergic synapse on T4/T5 dendrites still awaits further investigations (Fendl, 2020).

Third, inhibition via GABA plays an essential role in creating direction-selective responses in both T4 and T5 neurons by providing null direction suppression. Computer simulations showed that direction selectivity decreases in T4/T5 motion detector models without this inhibitory input on the null side of the dendrite. This study shows that T4 and T5 neurons possess the inhibitory GABA receptor subunit Rdl mainly on the proximal base on the null side of their dendrites, providing the synaptic basis for null direction suppression. The metabotropic GABA receptor subunit Gaba-b-r1 was not detected in T4/T5 neurons using the newly generated FlpTag Gaba-b-r1 line. Finally, all of the receptor subunits GluClα, Rdl and Dα7 investigated in this study are ionotropic, fast receptors, which presumably do not add a temporal delay at the synaptic level. In the detector model described above, the two outer arms provide a slow and sustained signal, and such properties are already intrinsic properties of these input neurons. However, it cannot be excluded that slow, metabotropic receptor subunits for acetylcholine or GABA (e.g. Gaba-br2) which are also present in T4/T5 and could induce additional delays at the synaptic level (Fendl, 2020).

Furthermore, the subcellular distribution was investigated of the voltage-gated ion channels Para and Ih in T4/T5 neurons. Para, a voltage-gated sodium channel, was found to be distributed along the axonal fibers of both T4 and T5 neurons. As Para is important for the generation of sodium-dependent action potentials, it will be interesting for future functional studies to investigate, if T4/T5 really fire action potentials and how this shapes their direction-selective response. Further, Ih, a voltage-gated ion channel permeable for several types of ions, was detected in T4/T5 dendrites using the FlpTag strategy. Ih channels are activated at negative potentials below -50 mV and as they are permeable to sodium and potassium ions, they can cause a depolarization of the cell after hyperpolarization. Loss-of-function studies will unravel the functional role of the Ih channel for direction-selective responses in T4/T5 neurons (Fendl, 2020).

Since the ability to combine synaptic inputs from different neurotransmitters at different spatial sites is common to all neurons, the approaches described in this study represent an important future perspective for other circuits. The tools can be used to study the ion channels GluClα, Rdl, Dα7, Gaba-b-r1, para and Ih in any given Drosophila cell-type and circuit. Furthermore, the FlpTag tool box can be used to target many genes of interest and thereby foster molecular questions across fields (Fendl, 2020).

The techniques described in this study can be transferred to other model organisms as well, to study the distribution of different transmitter receptors. For instance, in the mouse retina - similar to motion-sensing T4/T5 neurons in the fruit fly - so-called On-Off direction-selective ganglion cells receive asymmetric inhibitory GABAergic inputs from presynaptic starburst amacrine cells during null-direction motion. A previous study investigated the spatial distribution of GABA receptors of these direction-selective ganglion cells using super-resolution imaging and antibody staining. Additionally, starburst amacrine cells also release ACh onto ganglion cells which contributes to the direction-selective responses of ganglion cells. Thus, mapping the distribution of ACh receptors on direction-selective ganglion cells will be the next important step to further investigate cholinergic transmission in this network (Fendl, 2020).

Overall, this study has demonstrated the importance of exploring the distributions of neurotransmitter receptors and ion channels for systems neuroscience. The distinct distributions in T4/T5 neurons discovered in this study and the resulting functional consequences expand knowledge of the molecular basis of motion vision. Although powerful, recent RNAseq studies lacked information about spatial distributions of transmitter receptors which can change the whole logic of wiring patterns and underlying synaptic signs. Future studies can use this knowledge to target these receptors and directly probe their role in functional experiments or incorporate the gained insights into model simulations. However, this study is only highlighting some examples of important neural circuit components: expanding the approaches described in this study to other transmitter receptors and ion channels, as well as gap junction proteins will reveal the full inventory and the spatial distributions of these decisive determinants of neural function within an individual neuron (Fendl, 2020).

Diverse sensory systems, from audition to thermosensation, feature a separation of inputs into ON (increments) and OFF (decrements) signals. In the Drosophila visual system, separate ON and OFF pathways compute the direction of motion, yet anatomical and functional studies have identified some crosstalk between these channels. This well-studied circuit was used to ask whether the motion computation depends on ON-OFF pathway crosstalk. Using whole-cell electrophysiology, visual responses of T4 (ON) and T5 (OFF) cells were recorded and their composite ON-OFF receptive fields were mapped: it was found that they share a similar spatiotemporal structure. A biophysical model was fit to these receptive fields that accurately predicts directionally selective T4 and T5 responses to both ON and OFF moving stimuli. This model also provides a detailed mechanistic explanation for the directional preference inversion in response to the prominent reverse-phi illusion. Finally, the steering responses of tethered flying flies was used to validate the model's predicted effects of varying stimulus parameters on the behavioral turning inversion (Gruntman, 2021).

Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract a mechanistic understanding of sensory computations. This is evident in the highly constrained, canonical Drosophila motion detection circuit, where the core computation underlying direction selectivity is still debated despite extensive studies. This study measured the filtering properties of neural inputs to the OFF motion-detecting T5 cell in Drosophila. Stimulus-dependent changes in the shape of these signals, which become more biphasic under specific conditions. Summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that these shapes are sufficient to explain T5 responses to various motion stimuli. Thus, these stimulus- and state-dependent measurements reconcile motion computation with the anatomy of the circuit. These findings provide a clear example of how a basic circuit supports flexible sensory computation (Kohn, 2021).

Self-motion generates visual patterns on the eye that are important for navigation. These optic flow patterns are encoded by the population of local direction-selective cells in the mouse retina, whereas in flies, local direction-selective T4/T5 cells are thought to be uniformly tuned. How complex global motion patterns can be computed downstream is unclear. This study shows that the population of T4/T5 cells in Drosophila encodes global motion patterns. Whereas the mouse retina encodes four types of optic flow, the fly visual system encodes six. This matches the larger number of degrees of freedom and the increased complexity of translational and rotational motion patterns during flight. The four uniformly tuned T4/T5 subtypes described previously represent a local subset of the population. Thus, a population code for global motion patterns appears to be a general coding principle of visual systems that matches local motion responses to modes of the animal's movement (Henning, 2022).

Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate. This study reports the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, it is estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. These neurons were matched, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, the integration of optic flow patterns by neurons downstream of the LPTs was analyzed and most central brain neurons were found to establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, it was found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors (Zhao, 2023).

Animals have evolved sophisticated visual circuits to solve a vital inference problem: detecting whether or not a visual signal corresponds to an object on a collision course. Such events are detected by specific circuits sensitive to visual looming, or objects increasing in size. Various computational models have been developed for these circuits, but how the collision-detection inference problem itself shapes the computational structures of these circuits remains unknown. Inspired by the distinctive structures of LPLC2 neurons (ultra-selective looming detecting neuron, lobula plate/lobula columnar, type II (LPLC2) in Drosophila) in the visual system of Drosophila, this study built anatomically-constrained shallow neural network models and trained them to identify visual signals that correspond to impending collisions. Surprisingly, the optimization arrives at two distinct, opposing solutions, only one of which matches the actual dendritic weighting of LPLC2 neurons. Both solutions can solve the inference problem with high accuracy when the population size is large enough. The LPLC2-like solutions reproduces experimentally observed LPLC2 neuron responses for many stimuli, and reproduces canonical tuning of loom sensitive neurons, even though the models are never trained on neural data. Thus, LPLC2 neuron properties and tuning are predicted by optimizing an anatomically-constrained neural network to detect impending collisions. More generally, these results illustrate how optimizing inference tasks that are important for an animal's perceptual goals can reveal and explain computational properties of specific sensory neurons (Zhou, 2022).

Many sighted animals solve this inference problem with high precision, thanks to robust loom-selective neural circuits evolved over hundreds of millions of years. The neuronal mechanisms for response to looming stimuli have been studied in a wide range of vertebrates, from cats and mice to zebrafish, as well as in humans. In invertebrates, detailed anatomical, neurophysiological, behavioral, and modeling studies have investigated loom detection, especially in locusts and flies. An influential mathematical model of loom detection was derived by studying the responses of the giant descending neurons of locusts. This model established a relationship between the timing of the neurons' peak responses and an angular size threshold for the looming object. Similar models have been applied to analyze neuronal responses to looming signals in flies, where genetic tools make it possible to precisely dissect neural circuits, revealing various neuron types that are sensitive to looming signals (Zhou, 2022).

However, these computational studies did not directly investigate the relationship between the structure of the loom-sensitive neural circuits and the inference problem they appear to solve. On the one hand, the properties of many sensory circuits appear specifically tuned to the tasks that they are executing. In particular, by taking into account relevant behaviors mediated by specific sensory neurons, experiments can provide insight into their tuning properties. On the other hand, computational studies that have trained artificial neural networks to solve specific visual and cognitive tasks, such as object recognition or motion estimation, have revealed response patterns similar to the corresponding biological circuits or even individual neurons. Thus, in this study it is asked whether the properties associated with neural loom detection can be reproduced simply by optimizing shallow neural networks for collision detection (Zhou, 2022).

The starting point for the computational model of loom detection is the known neuroanatomy of the visual system of the fly. In particular, the loom-sensitive neuron LPLC2 (lobula plate/lobula columnar, type 2) has been studied in detail (Wu, 2016). These neurons tile visual space, sending their axons to descending neurons called the giant fibers (GFs), which trigger the fly's jumping and take-off behaviors. Each LPLC2 neuron has four dendritic branches that receive inputs at the four layers of the lobula plate (LP). The retinotopic LP layers host the axon terminals of motion detection neurons, and each layer uniquely receives motion information in one of the four cardinal directions. Moreover, the physical extensions of the LPLC2 dendrites align with the preferred motion directions in the corresponding LP layers. These dendrites form an outward radial structure, which matches the moving edges of a looming object that expands from the receptive field center. Common stimuli such as the wide-field motion generated by movement of the insect only match part of the radial structure, and strong inhibition for inward-directed motion suppresses responses to such stimuli. Thus, the structure of the LPLC2 dendrites favors responses to visual stimuli with edges moving radially outwards, corresponding to objects approaching the receptive field center (Zhou, 2022).

The focus of this paper is to investigate how loom detection in LPLC2 can be seen as the solution to a computational inference problem. Can the structure of the LPLC2 neurons be explained in terms of optimization-carried out during the course of evolution-for the task of predicting which trajectories will result in collisions? How does coordination among the population of more than 200 LPLC2 neurons tiling a fly's visual system affect this optimization? To answer these questions, simple anatomically-constrained neural network models were built that receive motion signals in the four cardinal directions. The model using artificial stimuli to detect visual objects on a collision course with the observer. Surprisingly, optimization finds two distinct types of solutions, with one resembling the LPLC2 neurons and the other having a very different configuration. How each of these solutions detects looming events and where they show distinct individual and population behaviors were analyzed. When tested on visual stimuli not in the training data, the optimized solutions with filters that resemble LPLC2 neurons exhibit response curves that are similar to those of LPLC2 neurons measured experimentally. Importantly, although it only receives motion signals, the optimized model shows characteristics of an angular size encoder, which is consistent with many biological loom detectors, including LPLC2. These results show that optimizing a neural network to detect looming events can give rise to the properties and tuning of LPLC2 neurons (Zhou, 2022).

The radially structured dendrites of the LPLC2 neuron in the LP can account for its response to motion radiating outward from the receptive field center. The results show that the logic of this computation can be understood in terms of inferential loom detection by the population of units. In particular, for an individual detector unit, an inward structure can make a better loom detector than an outward structure, since it is sensitive to colliding objects originating from a wider array of incoming angles. As the number of units across visual space increases, the performance of the outward-sensitive receptive field structure comes to match the performance of the inward solutions. As the number of units increases, the inhibitory component of the outward solutions also becomes broader as the population size becomes larger, which is crucial for reproducing key experimental observations, such as peripheral inhibition. The optimized solutions depend on the number of detectors, and this is likely related to the increasing overlap in receptive fields as the population grows. This result is consistent with prior work showing that populations of neurons often exhibit different and improved coding strategies compared to individual neurons. Thus, understanding anatomical, physiological, and algorithmic properties of individual neurons can require considering the population response. The solutions that were found to the loom inference problem suggest that individual LPLC2 responses should be interpreted in light of the population of LPLC2 responses (Zhou, 2022).

Our results shed light on discussions of η-like (encoding angular size) and ρ-like (encoding angular velocity) looming sensitive neurons in the literature. In particular, these optimized models clarify an interesting but puzzling fact: LPLC2 neurons transform their inputs of direction-selective motion signals to computations of angular size (Ache, 2019). Consistent with this tuning, the model also shows a linear relationship between the peak time relative to collision and the R/η ratio, which should be followed by loom sensitive neurons that encode angular size. In both cases, these properties appear to be the simple result of training the constrained model to reliably detect looming stimuli (Zhou, 2022).

The units of the outward solution exhibit sparsity in their responses to looming stimuli, in contrast to the denser representations in the inward solution. During a looming event, in an outward solution, most of the units are quiet and only a few adjacent units have very large activities, reminiscent of sparse codes that seem to be favored, for instance, in cortical encoding of visual scenes. Since the readout of the model is a summation of the activities of the units, sparsity does not directly affect the performance of the model, but is an attribute of the favored solution. For a model with a different loss function or with noise, the degree of sparsity might be crucial. For instance, the sparse code of the outward model might make it easier to localize a hit stimulus, or might make the population response more robust to noise (Zhou, 2022).

Experiments have shown that inhibitory circuits play an important role for the selectivity of LPLC2 neurons. For example, motion signals at the periphery of the receptive field of an LPLC2 neuron inhibit its activity. This peripheral inhibition causes various interesting response patterns of the LPLC2 neurons to different types of stimuli. However, the structure of this inhibitory field is not fully understood, and the model provides a tool to investigate how the inhibitory inputs to LPLC2 neurons affect circuit performance on loom detection tasks. The strong inhibition on the periphery of the receptive field arises naturally in the outward solutions after optimization. The extent of the inhibitory components increases as more units are added to models. The broad inhibition appears in the model to suppress responses to the non-hit stimuli, and as in the data, the inhibition is broader than one might expect if the neuron were simply being inhibited by inward motion. These larger inhibitory fields are also consistent with the larger spatial pooling likely to be supplied by inhibitory LPi inputs (Zhou, 2022).

The synthetic stimuli used to train models in this study were unnatural in two ways. The first way was in the proportion of hits and non-hits. Training was carried out with 25% of the training data representing hits. The true fraction of hits among all stimuli encountered by a fly is undoubtedly much less, and this affects how the loss function weights different types of errors. It is also clear that a false-positive hit (in which a fly might jump to escape an object not on collision course) is much less penalized during evolution than a false-negative (in which a fly doesn't jump and an object collides, presumably to the detriment of the fly). It remains unclear how to choose these weights in the training data or in the loss function, but they affect the receptive field weights optimized by the model (Zhou, 2022).

The second issue with the stimuli is that they were caricatures of stimulus types, but did not incorporate the richness of natural stimuli. This richness could include natural textures and spatial statistics, which seem to impact motion detection algorithms. This richness could also include more natural trajectories for approaching objects. Another way to enrich the stimuli would be to add noise, either in inputs to the model or in the model's units themselves. This was explored briefly by adding self-rotation-generated background motion; under those conditions, both solutions were present but optimized outward solutions performed better than the inward solutions. This indicates that the statistics of the stimuli may play an important role in selecting solutions for loom detection. However, it remains less clear what the true performance limits of loom detection are, since most experiments use substantially impoverished looming stimuli. Moreover, it is challenging to characterize the properties of natural looming events. An interesting future direction will be to investigate the effects of more complex and naturalistic stimuli on the model's filters and performance, as well as on LPLC2 neuron responses themselves (Zhou, 2022).

For simplicity, the models did not impose the hexagonal geometry of the compound eye ommatidia. Instead, it was assumed that the visual field is separated into a Cartesian lattice with 5° spacing, each representing a local motion detector with two spatially separated inputs. This simplification alters slightly the geometry of the motion signals compared to the real motion detector receptive fields. This could potentially affect the learned spatial weightings and reproduction of the LPLC2 responses to various stimuli, since the specific shapes of the filters matter. Thus, the hexagonal ommatidial structure and the full extent of inputs to T4 and T5 might be crucial if one wants to make comparisons with the dynamics and detailed responses of LPLC2 neurons. However, this geometric distinction seems unlikely to affect the main results of how to infer the presence of hit stimuli (Zhou, 2022).

The model requires a field of estimates of the local motion. Here, the simplest model was used - the Hassenstein-Reichardt correlator model Equation 3 - but the model could be extended by replacing it with a more sophisticated model for motion estimation. Some biophysically realistic ones might take into account synaptic conductances and could respond to static features of visual scenes. Alternatively, in natural environments, contrasts fluctuate in time and space. Thus, if one includes more naturalistic spatial and temporal patterns, one might consider a motion detection model that could adapt to changing contrasts in time and space (Zhou, 2022).

Although the outward filter of the unit emerges naturally from the gradient descent training protocol, that does not mean that the structure is learned by LPLC2 neurons in the fly. There may be some experience dependent plasticity in the fly eye , but these visual computations are likely to be primarily genetically determined. Thus, one may think of the computation of the LPLC2 neuron as being shaped through millions of years of evolutionary optimization. Optimization algorithms at play in evolution may be able to avoid getting stuck in local optima, and thus work well with the sort of shallow neural network found in the fly eye (Zhou, 2022).

This study focused on the motion signal inputs to LPLC2 neurons, and other inputs to LPLC2 neurons were neglected, such as those coming from the lobula that likely report non-motion visual features. It would be interesting to investigate how this additional non-motion information affects the performance and optimized solutions of the inference units. For instance, another lobula columnar neurons, LC4, is loom sensitive and receives inputs in the lobula. The LPLC2 and LC4 neurons are the primary excitatory inputs to the GF, which mediates escape behaviors. The inference framework set out here would allow one to incorporate parallel non-motion intensity channels, either by adding them into the inputs to the LPLC2-like units, or by adding in a parallel population of LC4-like units. This would require a reformulation of a probabilistic model. Notably, one of the most studied loom detecting neurons, the lobula giant movement detector (LGMD) in locusts, does not appear to receive direction-selective inputs, as LPLC2 does. Thus, the inference framework set out here could be flexibly modified to investigate loom detection under a wide variety of constraints and inputs, which allow it to be applied to other neurons, beyond LPLC2 (Zhou, 2022).

The perception of motion is an important function of vision. Neural wiring diagrams for extracting directional information have been obtained by connectome reconstruction. Direction selectivity in Drosophila is thought to originate in T4/T5 neurons through integrating inputs with different temporal filtering properties. Through genetic screening based on synaptic distribution, a new type of TmY neuron, termed TmY-ds, was isolated that forms reciprocal synaptic connections with T4/T5 neurons. Its neurites responded to grating motion along the four cardinal directions and showed a variety of direction selectivity. Intriguingly, its direction selectivity originated from temporal filtering neurons rather than T4/T5. Genetic silencing and activation experiments showed that TmY-ds neurons are functionally upstream of T4/T5. These results suggest that direction selectivity is generated in a tripartite circuit formed among these three neurons-temporal filtering, TmY-ds, and T4/T5 neurons, in which TmY-ds plays a role in the enhancement of direction selectivity in T4/T5 neurons (Zhao, 2022). The HisCl1 histamine receptor acts in photoreceptors to synchronize Drosophila behavioral rhythms with light-dark cycles"

In Drosophila, the clock that controls rest-activity rhythms synchronizes with light-dark cycles through either the blue-light sensitive cryptochrome (Cry) located in most clock neurons, or rhodopsin-expressing histaminergic photoreceptors. This study shows that, in the absence of Cry, each of the two histamine receptors Ort and HisCl1 contribute to entrain the clock whereas no entrainment occurs in the absence of the two receptors. In contrast to Ort, HisCl1 does not restore entrainment when expressed in the optic lobe interneurons. Indeed, HisCl1 is expressed in wild-type photoreceptors and entrainment is strongly impaired in flies with photoreceptors mutant for HisCl1. Rescuing HisCl1 expression in the Rh6-expressing photoreceptors restores entrainment but it does not in other photoreceptors, which send histaminergic inputs to Rh6-expressing photoreceptors. These results thus show that Rh6-expressing neurons contribute to circadian entrainment as both photoreceptors and interneurons, recalling the dual function of melanopsin-expressing ganglion cells in the mammalian retina (Alejevski, 2019).

The Drosophila sleep–wake rhythms are controlled by a brain circadian clock that includes about 150 clock neurons. Light synchronizes the clock neuronal network through cell-autonomous and non-cell-autonomous light input pathways. Cry is a blue-light sensitive photoreceptor protein that is expressed in most clock neurons. In the absence of Cry, flies do not phase-shift their behavioral rhythms in response to a short light pulse but still synchronize to light–dark (LD) cycles. Only flies devoid of both Cry and rhodopsin-expressing photoreceptors fail to entrain to LD cycles. Six different rhodopsins (Rhs) have been characterized in the Drosophila photoreceptive structures, which include the compound eye, the Hofbauer-Buchner (H-B) eyelet, and ocelli. The compound eye strongly contributes to circadian photoreception, whereas a modest contribution appears to be brought by the H-B eyelet and the ocelli. A circadian function has been recently associated with the yet poorly characterized rhodopsin 7, although its exact contribution and localization in the brain and/or the eye remains controversial. In addition to entrainment, the visual system controls other features of the clock neuron network by conveying light information to either promote or inhibit the behavioral output of specific clock neuron subsets (Alejevski, 2019).

The compound eye includes about 800-unit eyes (ommatidia), each of which contains eight photoreceptors. The six Rh1-expressing outer photoreceptors (R1–6) are involved in motion detection and project to the lamina neuropile of the optic lobe. The two inner photoreceptors (R7–8) are important for color detection and project to the medulla. They express four different rhodopsins and thus define two types of ommatidia: 'pale' (p) ommatidia (30%) include a Rh3-expressing R7 and a Rh5-expressing R8, whereas 'yellow' (y) ommatidia (70%) include a Rh4-expressing R7 and a Rh6-expressing R8. Each extra-retinal H-B eyelet contains four Rh6-expressing photoreceptors that project to the accessory medulla, in the vicinity of key pacemaker neurons, the ventral lateral neurons (LNvs) that produce the pigment-dispersing factor (PDF) neuropeptide9,20–24. Each of the three ocelli contains about 80 photoreceptors that express Rh225. The Drosophila rhodopsins cover a wide range of wavelengths from 300 nm to 600 nm18,19, with only Rh1 and Rh6 being sensitive to red light (Alejevski, 2019).

Rhodopsin-dependent circadian entrainment involves two downstream signaling pathways, the canonical one that relies on the phospholipase C encoded by the no receptor potential A gene (norpA)2 or an unknown pathway that does not contribute in very low light levels. All but Rh2- and Rh5- expressing photoreceptors support synchronization in very low light, and at least Rh1, Rh5, and Rh6 can signal through the NorpA-independent pathway. Photoreceptors of the compound eye are histaminergic but the H-B eyelet expresses both histamine and acetylcholine. Although the two neurotransmitters might contribute to circadian entrainment, flies devoid of Cry and histidine decarboxylase do not synchronize their rest–activity rhythms with LD cycles. This suggests that besides Cry, there is no histamine-independent pathway to entrain the clock (Alejevski, 2019).

Two genes encoding histamine-gated chloride channels, ora transientless (ort) and Histamine-gated chloride channel subunit 1 (HisCl1), have been identified in Drosophila. The ort-null mutants are visually blind and their electroretinograms have no ON and OFF transients. In contrast, HisCl1 mutants show increased OFF transients, whereas slower responses were observed in the postsynaptic laminar monopolar cells. Based on transcriptional reporters, ort expression in the optic lobes was observed in neurons of both the lamina and medulla/lobula neuropils. Based on reporter gene expression, HisCl1 was localized in glial cells of the lamina. However, recent work reported expression in photoreceptors, in particular in the R7 and R8 inner photoreceptor subtypes. Indeed, Ort and HisCl1 support color opponency between the two subtypes of 'inner' photoreceptors, the ultraviolet (UV)-sensitive R7 and non-UV-sensitive R8, with HisCl1 and Ort mediating direct and indirect inhibition, respectively. The histaminergic pathways that are involved in circadian entrainment are unknown and are the subject of the present study. The results show that both Ort and HisCl1 define two different pathways for circadian entrainment. Whereas Ort contributes through its expression in the interneurons of the optic lobe, HisCl1 mostly contributes through its expression in the Rh6-expressing retinal photoreceptors. The work thus reveals that Rh6-expressing neurons contribute to light-mediated entrainment as both photoreceptors and interneurons (Alejevski, 2019).

This work reveals that the Cryptochrome-independent entrainment of rest–activity rhythms relies on distinct pathways that are contributed by the two histamine receptors Ort and HisCl1. Whereas Ort mediates circadian entrainment through the optic lobe interneurons that are involved in visual functions, HisCl1 defines a new photoreceptive pathway through Rh6-expressing photoreceptors. Although both receptors mediate synchronization with a shifted LD cycle, it seems likely that the two pathways will show differences in specific light conditions. It was not possible to rescue Ort function with HisCl1 expression in the ort-expressing cells, whereas the Ort could replace HisCl1 in Rh6 photoreceptors. It is possible that HisCl1 has a lower affinity for histamine with Rh6 cells receiving more neurotransmitter than optic lobe interneurons. Alternatively, interneurons could sufficiently differ from photoreceptors for their physiology or specific receptor-interacting protein content, preventing HisCl1 from working efficiently. HisCl1 downregulation in Rh6 cells slows down synchronization and flies with HisCl1134 mutant eyes synchronize very poorly with advanced LD cycles, and fail to synchronize with delays. It cannot be excluded that non-photoreceptor cells contribute to HisCl1-dependent entrainment but other pathways appear to have a modest contribution if any (Alejevski, 2019).

HisCl1 is expressed in the H-B eyelet, which could thus contribute to this synchronization pathway. However, the cell-killing experiments indicate that H-B eyelet is not required for HisCl1-mediated synchronization through Rh6 cells. In the recently described color opponency mechanism, retinal R7 cells inhibit R8 and vice versa through HisCl1 expression in the photoreceptors (Schnaitmann, 2018). It is supposed that HisCl1-dependent clock synchronization is also mediated by the hyperpolarization of Rh6-expressing cells. How this hyperpolarization interacts with the light-induced depolarization in Rh6 photoreceptors to result in a synchronization message to the clock neurons remains to be understood. Since only Rh6-expressing R8 and not the other inner photoreceptors contribute to this circadian photoreception pathway, Rh6 cells might have specific connections with downstream interneurons. Such specificity has been described for color vision where each of the four inner photoreceptor subtypes connects to a different type of TmY interneuron in the Medulla. This study shows that HisCl1 expression in Rh6 cells supports synchronization with red light, in the absence of Rh1, indicating that an intra-Rh6-photoreceptor circuit is sufficient. This indicates that Rh6-expressing R8 photoreceptors play a dual photoreceptor/interneuron role in this pathway (Model for the retinal input pathways to the brain clock). Whether the same individual cells have the two roles is unknown, although the HisCl1-dependent color opponency mechanism suggests that it could be the case. It is also unclear whether all Rh6-expressing R8 photoreceptors or only a fraction of them contribute to circadian synchronization. The results imply that, in addition to histaminergic neurotransmission, Rh6-expressing photoreceptors can talk to downstream interneurons through histamine-independent neurotransmission. A recent transcriptomics study indeed revealed the expression of cholinergic markers in R7 and R8 cells, supporting cholinergic transmission in the inner photoreceptors, in addition to histaminergic transmission (Alejevski, 2019).

The data indicate that histaminergic inputs from both outer and inner photoreceptors converge to Rh6 cells to contribute to circadian entrainment. It is possible that some of these inputs rely on Rh7, which seems to be expressed in Rh6-expressing photoreceptors, according to transcriptional reporter data. Putative connections between photoreceptors have been described in Drosophila and other insects. How R1–6 photoreceptors might be connected to Rh6-expressing R8 cells remains difficult to understand, but a few putative contacts between presynaptic outer cells and postsynaptic inner cells have been observed in Musca. The intra-retinal functional connectivity that this study reports in Drosophila is reminiscent to the circuit logic of circadian entrainment in the mammalian retina, where intrinsically photoreceptive retinal ganglion cells express the melanopsin photopigment in addition to receiving inputs from rods and cones. Interestingly, melanopsin appears to share light-sensing properties with the rhabdomeric photoreceptors of invertebrates. It has been shown that the mammalian circadian clock can synchronize with day–night cycles by tracking light color changes in addition to light intensity changes. It will be interesting to investigate the possible contribution of the dual function of Rh6-expressing photoreceptors to integrating different color cues into the retinal information that is sent to the clock (Alejevski, 2019).

The circuitry underlying the detection of visual motion in Drosophila melanogaster is one of the best studied networks in neuroscience. Lately, electron microscopy reconstructions, algorithmic models, and functional studies have proposed a common motif for the cellular circuitry of an elementary motion detector based on both supralinear enhancement for preferred direction and sublinear suppression for null-direction motion. In T5 cells, however, all columnar input neurons (Tm1, Tm2, Tm4, and Tm9) are excitatory. So, how is null-direction suppression realized there? Using two-photon calcium imaging in combination with thermogenetics, optogenetics, apoptotics, and pharmacology, this study discovered that it is via CT1, the GABAergic large-field amacrine cell, where the different processes have previously been shown to act in an electrically isolated way. Within each column, CT1 receives excitatory input from Tm9 and Tm1 and provides the sign-inverted, now inhibitory input signal onto T5. Ablating CT1 or knocking down GABA-receptor subunit Rdl significantly broadened the directional tuning of T5 cells. It thus appears that the signal of Tm1 and Tm9 is used both as an excitatory input for preferred direction enhancement and, through a sign inversion within the Tm1/Tm9-CT1 microcircuit, as an inhibitory input for null-direction suppression (Braun, 2023).

Vision is critical for the regulation of mating behaviors in many species. This study discovered that the Drosophila ortholog of human GABA (A) -receptor-associated protein (GABARAP; Atg8a) is required to fine-tune male courtship by modulating the activity of visual feedback neurons, lamina tangential cells (Lat). GABARAP is a ubiquitin-like protein that regulates cell-surface levels of GABA (A) receptors. Knocking down GABARAP or GABA (A) receptors in Lat neurons or hyperactivating them induces male courtship toward other males. Inhibiting Lat neurons, on the other hand, delays copulation by impairing the ability of males to follow females. Remarkably, the human ortholog of Drosophila GABARAP restores function in Lat neurons. Using in vivo two-photon imaging and optogenetics, Lat neurons were shown to be functionally connected to neural circuits that mediate visually-guided courtship pursuits in males. This work reveals a novel physiological role for GABARAP in fine-tuning the activity of a visual circuit that tracks a mating partner during courtship (Mabuchi, 2023).

An important step in neural information processing is the transformation of membrane voltage into calcium signals leading to transmitter release. However, the effect of voltage to calcium transformation on neural responses to different sensory stimuli is not well understood. This study used in vivo two-photon imaging of genetically encoded voltage and calcium indicators, ArcLight and GCaMP6f, respectively, to measure responses in direction-selective T4 neurons of female Drosophila Comparison between ArcLight and GCaMP6f signals reveals calcium signals to have a significantly higher direction selectivity compared with voltage signals. Using these recordings, a model was built which transforms T4 voltage responses into calcium responses. Using a cascade of thresholding, temporal filtering and a stationary nonlinearity, the model reproduces experimentally measured calcium responses across different visual stimuli. These findings provide a mechanistic underpinning of the voltage to calcium transformation and show how this processing step, in addition to synaptic mechanisms on the dendrites of T4 cells, enhances direction selectivity in the output signal of T4 neurons. Measuring the directional tuning of postsynaptic vertical system (VS)-cells with inputs from other cells blocked, this study found that, indeed, it matches the one of the calcium signal in presynaptic T4 cells (Mishra, 2023).

To survive, animals must convert sensory information into appropriate behaviours. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. This study shows through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs) into synaptic weight gradients of VPN outputs onto central brain neurons. This gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly's directional escape. Specifically, it was discovered that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, graded responses along the dorsoventral axis were demonstrated. This synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs (Dombrovski, 2023).

Tracking visual objects while maintaining stable gaze is complicated by the different computational requirements for figure-ground discrimination, and the distinct behaviors that these computations coordinate. Drosophila melanogaster uses smooth optomotor head and body movements to stabilize gaze, and impulsive saccades to pursue elongated vertical bars. Directionally selective motion detectors T4 and T5 cells provide inputs to large-field neurons in the lobula plate, which control optomotor gaze stabilization behavior. This study hypothesized that an anatomically parallel pathway represented by T3 cells, which provide inputs to the lobula, drives bar tracking body saccades. Physiological and behavioral experiments were combined to show that T3 neurons respond omnidirectionally to the same visual stimuli that elicit bar tracking saccades, silencing T3 reduced the frequency of tracking saccades, and optogenetic manipulation of T3 acted on the saccade rate in a push-pull manner. Manipulating T3 did not affect smooth optomotor responses to large-field motion. These results show that parallel neural pathways coordinate smooth gaze stabilization and saccadic bar tracking behavior during flight (Frighetto, 2023).

Visual motion estimation is a canonical neural computation. In Drosophila, recent advances have identified anatomic and functional circuitry underlying direction-selective computations. Models with varying levels of abstraction have been proposed to explain specific experimental results but have rarely been compared across experiments. This studys used the wealth of available anatomical and physiological data to construct a minimal, biophysically inspired synaptic model for Drosophila's first-order direction-selective T4 cells. This model relates mathematically to classical models of motion detection, including the Hassenstein-Reichardt correlator model. Numerical simulation was used to test how well this synaptic model could reproduce measurements of T4 cells across many datasets and stimulus modalities. These comparisons include responses to sinusoid gratings, to apparent motion stimuli, to stochastic stimuli, and to natural scenes. Without fine-tuning this model, it sufficed to reproduce many, but not all, response properties of T4 cells. Since this model is flexible and based on straightforward biophysical properties, it provides an extensible framework for developing a mechanistic understanding of T4 neural response properties. Moreover, it can be used to assess the sufficiency of simple biophysical mechanisms to describe features of the direction-selective computation and identify where understanding must be improved (Zavatone-Veth, 2020).

In visual systems, neurons adapt both to the mean light level and to the range of light levels, or the contrast. Contrast adaptation has been studied extensively, but it remains unclear how it is distributed among neurons in connected circuits, and how early adaptation affects subsequent computations. This study investigated temporal contrast adaptation in neurons across Drosophila's visual motion circuitry. Several ON-pathway neurons showed strong adaptation to changes in contrast over time. One of these neurons, Mi1, showed almost complete adaptation on fast timescales, and experiments ruled out several potential mechanisms for its adaptive properties. When contrast adaptation reduced the gain in ON-pathway cells, it was accompanied by decreased motion responses in downstream direction-selective cells. Simulations show that contrast adaptation can substantially improve motion estimates in natural scenes. The benefits are larger for ON-pathway adaptation, which helps explain the heterogeneous distribution of contrast adaptation in these circuits (Matulis, 2020).

Neurons can change their gain in response to their inputs. In the visual system, as scenes move across the retina, neurons may encounter many different luminances over time. Neurons can adapt to the mean of these inputs and also to the range of these inputs, or the temporal contrast. Contrasts can vary widely within a scene or across scenes. Many retinal neurons change their gain in ways that amplify low-contrast signals relative to high-contrast ones. In these cases, the adaptation has been proposed to maximize information transfer to downstream circuits. However, it remains unclear how contrast adaptation is distributed among parallel processing streams in visual circuits and how early changes in gain affect specific downstream computations. This study investigated contrast adaptation in Drosophila's visual motion circuits, where powerful genetic tools and known connectivity allow investigation both the breadth of contrast adaptation and its consequences for circuit computations (Matulis, 2020).

The benefits of contrast adaptation have often been framed in terms of efficient coding. Neurons have a limited range of responses but often receive a wide range of input signals. By adapting to the distribution of inputs, neurons can make efficient use of their limited output range to encode a fuller range of inputs; this can be formalized in terms of transmitted information. In this view, contrast adaptation is beneficial because it permits cells to represent inputs from both high- and low-contrast scenes using the full range of their responses. This study examined the role of contrast adaptation in circuitry associated with direction-selective (DS) signals and use simulations to understand contrast adaptive properties in the context of specific downstream motion computations (Matulis, 2020).

Contrast adaptation has been studied extensively in vertebrate retina. There, photoreceptors and horizontal cells do not show contrast adaptation, but downstream cells, including bipolar, amacrine, and retinal ganglion cells, exhibit varying degrees of contrast adaptation. Adaptation can occur quickly, on the scale of 100 s of milliseconds, or slowly, on the scale of several seconds. This adaptation has been found to occur cell intrinsically, mediated by membrane channels, as well as by upstream synaptic depletion (Matulis, 2020).

In the fly eye, light is detected by photoreceptors, and, from there, signals are split into parallel pathways, which relay information about the visual scene with varying degrees of rectification and delay. These ON and OFF pathways converge onto the cells T4 and T5, which respond direction selectively to light edges and dark edges, respectively, and are required for optomotor orientation and speed regulation. In flies, contrast adaptation has been observed in wide-field DS cells, which receive inputs from the local motion detectors, T4 and T5. When these wide-field cells are presented with high-contrast sinusoidal stimuli, they reduce the gain in their responses to stimuli in their preferred direction. The adapting stimulus can be presented in any direction, suggesting that this mechanism depends on upstream, non-DS cells. In a different wide-field DS cell, studies have characterized another form of adaptation to stimulus variance. There, experiments examined responses to stimulus velocity, rather than to contrast, and found that cells strongly adapted to the variance of the velocity distribution. Thus, in flies, adaptation to contrast and, more generally, to stimulus variance exists in wide-field DS cells. But contrast adaptation has not been observed in neurons upstream of them (Matulis, 2020).

This study measured contrast adaptation in the neurons upstream of motion detection. Focus was placed on one strongly adapting neuron, Mi1, and it was shown to adapt roughly as fast as it responds. Silencing was used to rule out several potential mechanisms for the contrast adaptation, including inheritance from specific other cells and synaptic depletion in a primary presynaptic partner. A simple phenomenological model can account for this neuron's fast changes in gain. Downstream of Mi1, contrast adaptation was examine in the neurons T4 and T5. Simulations show how adaptation to the stimulus contrast could substantially improve motion detection computations, an effect that is stronger in ON motion pathways (Matulis, 2020).

Detecting the direction of motion contained in the visual scene is crucial for many behaviors. However, because single photoreceptors only signal local luminance changes, motion detection requires a comparison of signals from neighboring photoreceptors across time in downstream neuronal circuits. For signals to coincide on readout neurons that thus become motion and direction selective, different input lines need to be delayed with respect to each other. Classical models of motion detection rely on non-linear interactions between two inputs after different temporal filtering. However, recent studies have suggested the requirement for at least three, not only two, input signals. This study comprehensively characterized the spatiotemporal response properties of all columnar input elements to the elementary motion detectors in the fruit fly, T4 and T5 cells, via two-photon calcium imaging. Between these input neurons, large differences were found in temporal dynamics. Based on this, computer simulations show that only a small subset of possible arrangements of these input elements maps onto a recently proposed algorithmic three-input model in a way that generates a highly direction-selective motion detector, suggesting plausible network architectures. Moreover, modulating the motion detection system by octopamine-receptor activation, the temporal tuning of T4 and T5 cells was found to be shifted toward higher frequencies, and this shift can be fully explained by the concomitant speeding of the input elements (Arenz, 2017).

The perception of visual motion is critical for animal navigation, and flies are a prominent model system for exploring this neural computation. In Drosophila, the T4 cells of the medulla are directionally selective and necessary for ON motion behavioral responses. To examine the emergence of directional selectivity, genetic driver lines were developed for the neuron types with the most synapses onto T4 cells. Using calcium imaging, it was found that these neuron types are not directionally selective and that selectivity arises in the T4 dendrites. By silencing each input neuron type, which neurons are necessary for T4 directional selectivity and ON motion behavioral responses were identified. Tthe sign of the connections between these neurons and T4 cells were determined using neuronal photoactivation. These results indicate a computational architecture for motion detection that is a hybrid of classic theoretical models (Strother, 2017).

The behavioral state of an animal can dynamically modulate visual processing. In flies, the behavioral state is known to alter the temporal tuning of neurons that carry visual motion information into the central brain. However, where this modulation occurs and how it tunes the properties of this neural circuit are not well understood. This study shows that the behavioral state alters the baseline activity levels and the temporal tuning of the first directionally selective neuron in the ON motion pathway (T4) as well as its primary input neurons (Mi1, Tm3, Mi4, Mi9). These effects are especially prominent in the inhibitory neuron Mi4, and this study shows that central octopaminergic neurons provide input to Mi4 and increase its excitability. It was further shown that octopamine neurons are required for sustained behavioral responses to fast-moving, but not slow-moving, visual stimuli in walking flies. These results indicate that behavioral-state modulation acts directly on the inputs to the directionally selective neurons and supports efficient neural coding of motion stimuli (Strother, 2018).

Sensory systems sequentially extract increasingly complex features. ON and OFF pathways, for example, encode increases or decreases of a stimulus from a common input. This ON/OFF pathway split is thought to occur at individual synaptic connections through a sign-inverting synapse in one of the pathways. This study showed that ON selectivity is a multisynaptic process in the Drosophila visual system. A pharmacogenetics approach demonstrates that both glutamatergic inhibition through GluClα and GABAergic inhibition through Rdl mediate ON responses. Although neurons postsynaptic to the glutamatergic ON pathway input L1 lose all responses in GluClalpha mutants, they are resistant to a cell-type-specific loss of GluClα. This shows that ON selectivity is distributed across multiple synapses, and raises the possibility that cell-type-specific manipulations might reveal similar strategies in other sensory systems. Thus, sensory coding is more distributed than predicted by simple circuit motifs, allowing for robust neural processing (Molina-Obando, 2019).

Animals rely on their sensory systems to process behaviorally relevant information. One common feature of sensory systems is the sequential processing of information to extract complex features from simple inputs. For example, in the visual system, photoreceptors detect light and then downstream neurons progressively extract distinct features, such as contrast, direction of motion, form, or specific objects. Sensory pathways diverge into pathways that become selective for increasingly specific features (Molina-Obando, 2019).

One prominent example is the split into ON and OFF pathways, where individual neurons become selective to either increases (ON) or decreases (OFF) in a signal. Such an ON/OFF dichotomy enables more efficient coding of stimuli in the visual system and occurs across many different species and sensory modalities, such as vision, olfaction, audition, thermosensation, and electrolocation. Examples of how the split into ON and OFF pathways is implemented in sensory information processing have already been described. In the vertebrate retina, ON and OFF pathways split downstream of glutamatergic photoreceptors where ionotropic glutamate receptors on OFF bipolar cells maintain the sign of the response in the OFF pathway, and the metabotropic glutamate receptor mGluR6, located on ON bipolar cells, inverts the sign in the ON pathway. In the olfactory system of C. elegans, an odor response can be split into parallel pathways in which glutamate-gated chloride channels mediate the ON response. While these transformations are thought to occur at specific synapses, connectomics data reveals that neural circuits are intricate and that many of the possible neuronal connections are realized. This argues that important signal transformations might actually be distributed across wider circuit motifs (Molina-Obando, 2019).

In the Drosophila visual system, ON and OFF pathways functionally split in the first order lamina interneurons, but the physiological specialization occurs one synaptic layer further downstream. In brief, information travels from the retina, which houses the photoreceptors, through three optic ganglia: the lamina, the medulla, and the lobula complex, comprising lobula and lobula plate (see Figure 1 at the following site: ON pathway medulla neurons that receive graded, glutamatergic input). Contrast is encoded by the transient response of photoreceptors, and downstream lamina neurons amplify the contrast-sensitive signal component. Then, distinct ON and OFF pathways are required to detect contrast increments and decrements, respectively. In the lamina, L1 is the major input to the ON pathway, whereas L2 and L3 feed into the OFF pathway. The assignment of L1, L2, and L3 to ON and OFF pathways originates from neuronal silencing studies. However, all lamina neurons receiving direct input from photoreceptors depolarize to the offset of light and hyperpolarize to the onset of light, thus passing on information about both ON and OFF. Voltage or calcium signals in most downstream medulla neurons then selectively report only one type of contrast polarity. The major ON pathway medulla neurons Mi1 and Tm3, for example, selectively respond with depolarization or an increase in calcium signal to ON. In the OFF pathway, most neurons instead selectively respond to OFF stimuli, retaining the response polarity of their lamina inputs. Therefore, ON selectivity requires a sign inversion between the L1 input and its postsynaptic partners Mi1 and Tm3. Previous work suggested that the L1 input to the ON pathway is glutamatergic, whereas L2 and L3, the two major inputs to the OFF pathway, are cholinergic. This suggests that glutamate might also be used as an inhibitory neurotransmitter to implement ON/OFF dichotomy in the fly visual system. However, the molecular and cellular mechanisms implementing this signal transformation are not known in Drosophila visual circuitry (Molina-Obando, 2019).

This study has identified the mechanisms underlying splitting of the ON and OFF pathways in the Drosophila visual system. As expected from the major input to the ON pathway being glutamatergic, broad GluClα function is required for all ON responses in medulla neurons or downstream direction-selective cells. However, individual cell types downstream of the glutamatergic L1 input are resilient to a cell-type-specific loss of GluClα, demonstrating that ON selectivity is computed in a distributed manner. This study further showed that both the glutamate-gated chloride channel GluClα and the GABA-gated chloride channel Rdl are widely expressed in the visual system and together mediate ON responses. Thus, ON selectivity is a multisynaptic computation that is established across distributed circuits (Molina-Obando, 2019).

This work shows that visual responses in the first ON-selective neuron of the Drosophila visual system uses a combination of GluClα and Rdl receptors. This reveals a new biophysical mechanism through which ON and OFF pathway dichotomy can be established. While pharmacology can be used to deduce the function of specific molecular mechanisms, these approaches are often not specific to one protein. GluCls and GABARs belong to the same receptor family of ligand-gated chloride channels and have closely related structure and phylogeny. All known noncompetitive antagonists like Picrotoxin, γ-HCH, dieldrin, EBOB and fibronil target both receptor types although the actions are weaker in GluCls compared to GABARs. Along these lines, PTX was thought to affect GABAA receptor at low concentrations, and additionally affect GluCls at high concentrations in vitro and in vivo. This study use of PTX-insensitive alleles for glutamate and GABA-gated chloride channels making possible the deduction that, in vivo, GluClα is already blocked by PTX at lower concentrations than previously thought, and that both GluClα and Rdl play critical roles for ON responses in the Drosophila visual system. These pharmacogenetic experiments using toxin-insensitive alleles prove to be a powerful tool to unambiguously assign specific effects to individual channels (Molina-Obando, 2019).

One benefit of the use of two inhibitory transmitter systems might be the distribution of sensory coding across parallel synapses. GluClα and Rdl also appear to have very different channel dynamics. Interestingly, PTX-insensitive GluClα and Rdl alleles predominantly rescue different aspects of the visual responses. Whereas GluClαS278T predominantly rescued the peak response in all medulla layers, RdlMDRR mainly rescued the plateau response. This is consistent with the results and with previous oocyte recordings revealing that GluClα is fast desensitizing. It is also consistent with in vivo recordings of inhibitory glutamate currents in the honeybee. In contrast, GABA receptors stay open throughout the period in which the transmitter is present. Thus, the use of different inhibitory receptors might allow different aspects of a temporally structured stimulus to be encoded. This is consistent with the finding that two different types of inhibition are also in place in the vertebrate retina. There, GABAergic and glycinergic inhibition diversify the response properties of bipolar cells through a direct influence on temporal and spatial features (Molina-Obando, 2019).

While both receptors appear to be broadly expressed in many cell types of the visual system, they could be co-expressed with different transporters and channels, and interact with different molecular partners, further diversifying their role. Another common strategy to generate functional diversity is the bringing together of different receptor subunits with certain homology. Both mammalian GlyR and GABAA receptors can function as hetero-oligomers made up of different subunits and thus generating functional diversity. There are at least three different GluCl subtypes in C. elegans that can be combined. In Drosophila, only one gene coding for a glutamate-gated chloride channel has been identified. Although alternative splicing and post-transcriptional modifications could alter channel function, all known isoforms are identical in their functional domains. However, heteropentameric channels composed of mixed Rdl and GluClα subunits have been suggested biochemically. Such a potential presence of hybrid channels might also explain the higher in vivo sensitivity of GluClα to PTX in some cell types. Finally, two distinct inhibitory transmitter systems might be suitable for individual changes during evolution, allowing for adaptation to specific contextual constraints (Molina-Obando, 2019).

The current experiments revealed that GluClα is not exclusively required in a cell-autonomous manner for ON responses, since loss of GluClα function in Mi1 or Tm3 individually does not lead to a loss of ON responses. It is unlikely that this is due to an incomplete loss of function, since independent genetic tools (FlpStop and RNAi) that both disrupted GluClα expression substantially at the mRNA level gave the same result. Furthermore, the same FlpStop allele effectively abolished all ON responses when GluClα function was disrupted within its entire expression pattern. Additionally, a PTX-resistant Rdl channel can mediate ON responses in a PTX background, although L1 is not GABAergic. Together, these results suggest that ON selectivity is not a monosynaptic computation, but that parallel functional pathways can even compensate for the loss of the major synaptic connection that links L1 directly to Mi1 or Tm3. Thus, the emergence of ON selectivity is more distributed than suggested by minimal core circuit motifs. One synaptic layer further downstream, optogenetic activation of Mi1 and Tm3 most strongly contributes to T4/T5 responses. However, the current data further show that T4/T5 neurons still respond to ON stimuli when both Mi1 and Tm3 responses are completely blocked by PTX, arguing that other neurons also significantly contribute to T4/T5 responses under visual stimulation and suggesting that coding is again more distributed at this stage (Molina-Obando, 2019).

Based on connectomics, one can speculate about candidates for the implementation of these parallel circuit motifs between L1 and Mi1 and Tm3. The lamina neuron L5 and the GABAergic feedback neurons C2 and C3 receive L1 inputs and could be part of an interconnected local microcircuit. Intercolumnar neurons, not present in the current connectome datasets, like Pm or Dm neurons, might also be involved and are likely glutamatergic. In fact, there are close to 100 cell types in the visual system and ~60 medulla neurons, but their role is so far unknown. Sensory pathway splits in the periphery are one of the most fundamental steps in sensory processing. Turning this into a process that parallel pathways can achieve might make this important feature extraction step robust to perturbations (Molina-Obando, 2019).

T4 flash responses in a GluClα-deficient background show an increase in calcium signal during the OFF epoch and a decrease during the ON epoch. For a long time, the mechanisms that generate direction-selective responses in T4/T5 neurons were thought to rely on feedforward excitatory mechanisms. Recently, it was suggested that these direction-selective cells in the fly visual system also implement mechanisms that rely on null-direction suppression. Whereas electrophysiological recordings showed inhibition in T4 when the trailing edge of the receptive field was specifically stimulated, whole-cell recording experiments of T4/T5 neurons are daunting and this is the first time that calcium imaging data directly reveals inhibition in response to single ON flashes. Since glutamatergic inhibition via GluClα was disrupted in this experimental context, the data suggests that this is due to GABAergic inhibition. Several neuronal candidates could make inhibitory synapses onto T4 dendrites. Based on connectomics and neurotransmitter identity, neurons like Mi4, C3, CT1 or TmY15 give direct input and are GABAergic. Alternatively, this decrease in calcium signal in T4 might come from a lack of excitatory inputs in a GluClα mutant background. Interestingly, Mi1 and Tm3 themselves show inhibition in response to light when GluClα is blocked. However, this effect is more pronounced at their dendrites than in their output layer and shows different kinetics. The current work might thus help uncover a GABAergic inhibitory input to T4 that is more strongly apparent in the absence of Mi1 and Tm3 excitation, and could ultimately reveal the circuit implementation for the inhibitory component of T4/T5 receptive fields. Furthermore, the data also reveals an increase in calcium during OFF stimulation. The major inputs to T4 are themselves rectified. However, rectification in T4 might not be purely inherited by its inputs but also further strengthened at the T4 dendrites. The current findings thus suggest that glutamatergic inhibition contributes to establishing or maintaining contrast selectivity in T4 (Molina-Obando, 2019).

Both GluClα and Rdl are ionotropic ligand-gated receptors. While ionotropic receptors also implement the ON and OFF pathway split in C. elegans chemosensation, examples in vertebrate vision, olfaction and gustation require metabotropic receptors. Ionotropic receptors appear to be more common in insects than in vertebrates. Furthermore, glutamate-gated chloride channels have independently arisen three times within invertebrate clades and are present in arthropods, molluscs and flatworms, arguing for a strong evolutionary benefit. Ionotropic receptors mediate rapid transduction events at scales smaller than a millisecond, whereas metabotropic ones are in the millisecond to second range and last longer, from seconds to several minutes, due to an enzymatic secondary cascade previous to channel opening (Betz, 1990; Shiells, 1994). The evolutionary choice of the specific glutamatergic inhibitory system needs to match the sensory processing speed required for accurate behavioral responses in these species. For example, at the photoreceptor level, invertebrate phototransduction is faster than vertebrate phototransduction thanks to sophisticated molecular strategies. Also, the latency of olfactory sensory neurons responses in mammals is longer than that observed in insects. One advantage that metabotropic receptors have over ionotropic receptors is further amplification of the signal. The distributed circuit architecture proposed in this study might therefore strengthen signaling in a system that uses ionotropic signaling (Molina-Obando, 2019).

This study has shown that ON selectivity is not a monosynaptic process as described in other systems. Although acute pharmacological block or a systemic loss of function of GluClα abolished all ON responses in different neurons, cell-type-specific mutants retained intact ON responses, revealing that sensory coding is distributed in the fly visual system. This not only highlights the power of fly genetics but sheds new light onto the mechanisms of ON selectivity in other systems, since conclusions about ON and OFF pathway splits being mediated by specific monosynaptic processes in systems such as the vertebrate retina or the C. elegans chemosensory system relied on systemic loss-of-function approaches. Several of these systems allow for cell-type-specific manipulations using genetic approaches. It will be interesting to revisit these systems and ask if coding is similarly distributed across multiple synapses in different sensory systems and organisms (Molina-Obando, 2019).

Inhibitory interactions and columnar inputs to an object motion detector in Drosophila

The direction-selective T4/T5 cells innervate optic-flow processing projection neurons in the lobula plate of the fly that mediate the visual control of locomotion. In the lobula, visual projection neurons coordinate complex behavioral responses to visual features, however, the input circuitry and computations that bestow their feature-detecting properties are less clear. A highly specialized small object motion detector, LC11, was studied, and its responses were shown to be suppressed by local background motion. LC11 expresses GABA-A receptors that serve to sculpt responses to small objects but are not responsible for the rejection of background motion. Instead, LC11 is innervated by columnar T2 and T3 neurons that are themselves highly sensitive to small static or moving objects, insensitive to wide-field motion and, unlike T4/T5, respond to both ON and OFF luminance steps (Keles, 2020).

The cellular mechanisms of motion vision have become rapidly advanced owing to genetic, optogenetic, and in vivo imaging tools developed in Drosophila melanogaster. As in the mammalian retina, the fly optic lobe segregates ON and OFF polarity luminance changes into parallel cellular pathways. The ON- and OFF-selective pathways supply directionally selective columnar T4 and T5 neurons, respectively. The terminals of these small-field retinotopic motion detectors innervate the third optic ganglion, the lobula plate, where their synaptic output is integrated within the large planar dendrites of projection neurons that map specific wide-field patterns of optic flow onto descending pre-motor neurons to coordinate visual behavior (Keles, 2020).

In parallel to the motion vision pathway of the lobula plate, projection neurons identified in the lobula have been shown to encode moving features such as edges or objects to influence complex visual behaviors. Roughly 20 classes of lobula columnar neurons (LCs) project to the protocerebrum where axon terminals of each class form tight glomerular neuropils. Individual LC11 neurons as well as the glomerular ensemble are highly responsive to small contrasting objects moving in any direction across the ipsilateral field of view. Unlike the output cell types of the lobula plate, little is known about how the receptive field properties of LC11 arise. This study investigated the interactions between background motion and object responses in LC11, has identified a role for GABA-mediated inhibition in shaping object detection by LC11, and identifies presynaptic inputs to LC11. Columnar neurons T2 and T3 projecting from the medulla and terminating in the second and third layers of the lobula overlap with LC11 dendrites. T2 and T3 synapse with dendrites of LC11, and T3 supplies excitatory input to LC11. Finally, it was demonstrated that T2 and T3 neurons are highly selective for small objects, are suppressed by wide-field background motion, and unlike T4/T5, show full-wave rectified ON-OFF excitatory responses to rapid transitions in luminance (Keles, 2020).

In vertebrates, neurons in the retina partially encode object information, but fail to discriminate flicker from coherent motion. Yet, higher-order neurons in the mouse superior colliculus respond strongly only to moving stimuli. Similarly, this study found that T2/T3 neurons are selective for small objects, but respond to ON and OFF flicker as well, whereas downstream LC11 is responsive to object motion, not stationary flicker. It is proposed that LC11 computes continuous object motion from local ON-OFF transients conveyed by T2/T3. Future work on examining the spatiotemporal patterning of columnar inputs to LC11, as well as the cognate neurotransmitters and receptors should reveal how these computations are achieved (Keles, 2020).

In prior work (Keles, 2017), it was demonstrated that bath applied PTX, which selectively blocks chloride currents carried by GABA-A channels or glutamate channels, resulted in LC11 displaying uncharacteristic responses to elongated bars and gratings. This result was predicted under the presumption that inhibition actively filtered wide-field input from LC11. Curiously, in the same preparation, the small object responses for LC11 were essentially eradicated. How can global loss of inhibition by bath applied PTX explain both enhanced wide-field responses and diminished small object responses in LC11? Several lines of evidence suggest that postsynaptic inhibitory neuromodulation acts on LC11 in a center-surround fashion. LC11 expresses both acetylcholine receptors and a GABA-gated chloride channel subunit Rdl. Blocking GABA-A mediated synaptic currents by genetic disruption of Rdl specifically in LC11 neurons results in a decrease in response amplitude to the smallest object tested, and yet, importantly, had no effect on the normal attenuated responses to bars or normal absence of wide-field grating responses. The results support a working model in which Rdl knockout unmasks an ON-pathway input while decreasing the normal OFF object response of LC11. These properties could be explained by an ON-pathway GABAergic input to LC11 through Rdl that normally occludes ON excitation and disinhibits OFF responses. The corollary is that suppression of responses to large objects or wide-field motion occurs upstream of these object detectors. Indeed, LC11 appears to inherit its sensitivity to small object motion from excitatory T2/T3 inputs, perhaps themselves having surround inhibition mechanisms similar to T4/T5. There appears to be two mechanisms of action that are disrupted by PTX application on LC11 receptive field properties (Keles, 2017): crossover inhibition in T2/T3, which would explain their size-tuning, and local inhibition on LC11 that normally enhances small object responses. Thus, it is proposed that upon PTX delivery abnormal bar and wide-field motion responses are conveyed from T2/T3 to LC11 and small object responses are no longer boosted (Keles, 2020).

The importance of dynamic, stimulus-specific inhibition for spatial vision has been elucidated by other studies. In mice, cortical V1 center-surround receptive fields reveal stronger inhibitory currents than excitatory currents in both the surround and center, while inhibitory currents are spread more laterally than excitatory currents. In a visual collision detection circuit in the locust, feedforward inhibitory neurons actively encode dynamical variables such as object angular size. The inhibitory GABA-A receptor subunit Rdl is expressed by nearly all neurons of the fly visual system so far tested, highlighting the ubiquity and importance of inhibition for spatial vision (Keles, 2020).

T2 and T3 neurons share several key features with LC11. First, both show significantly larger responses to small solid objects than to single object edges or elongated bars, with virtually no response to moving wide-field gratings. In the large calliphorid fly Phaenicia sericata, T2 neurons have been examined with intracellular sharp electrodes, which showed that these columnar neurons depolarize to the OFF-phase of flicker, and hyperpolarize to the ON-phase. This contrasts to the GCaMP6f recordings in Drosophila, in which T2 is excited by both ON and OFF luminance transitions. Additionally, in Phaenicia T2 responded robustly to 80 x 62° moving gratings, whereas in Drosophila no response was observed in either T2 or T3 to gratings that filled the 108 x 63° display. The T2a cell type, with similar anatomy but different presynaptic inputs to T2, may show responses more closely matching those from larger flies (Keles, 2020).

An important feature of Drosophila T2/T3 neurons is that unlike T4 and T5 columnar motion detectors, which act as half-wave rectifiers that segregate ON and OFF edge stimuli, respectively, both T2 and T3 neurons show full-wave rectification in that they are excited by both ON and OFF phases of flicker. Notably, T5 shows similar amplitude responses to the OFF edges generated by either a solid two-edged dark object or a single moving OFF edge, whereas T3 responses are markedly stronger for the solid object presenting an OFF-ON sequence than to a single progressing OFF edge. T3 appears to receive input from a combination of neurons that reside in the ON and OFF pathways, including Mi1 and Tm3, providing a possible explanation for this result. Full-wave rectification of ON and OFF stimuli is consistent with single point correlation computations proposed to comprise elementary small target motion detectors (ESTMDs), which underlie the high performance object detection seen in lobula wide-field STMD neurons of hoverflies and dragonflies. Future work must explore the mechanisms that shape responses in T2 and T3, and how the spatiotemporal patterns of input from T2 and T3 confer discrimination of object motion from flicker in LC11 (Keles, 2020).

The identification of neurotransmitter type used by a neuron is important for the functional dissection of neuronal circuits. In the model organism Drosophila melanogaster, several methods for discerning the neurotransmitter systems are available. This study expanded the toolbox for the identification of cholinergic neurons by generating a new line FRT-STOP-FRT-VAChT::HA that is a conditional tagged knock-in of the VAChT gene in its endogenous locus. Importantly, in comparison to already available tools for the detection of cholinergic neurons, the FRT-STOP-FRT-VAChT::HA allele also allows for identification of the subcellular localization of the cholinergic presynaptic release sites in a cell-specific manner. The newly generated FRT-STOP-FRT-VAChT::HA line was used to characterize the Mi1 and Tm3 neurons in the fly visual system and found that VAChT is present in the axons of the both cell types, suggesting that Mi1 and Tm3 neurons provide cholinergic input to the elementary motion detectors, the T4 neurons (Pankova, 2017).

Detecting the orientation and movement of edges in a scene is critical to visually guided behaviors of many animals. What are the circuit algorithms that allow the brain to extract such behaviorally vital visual cues? Using in vivo two-photon calcium imaging in Drosophila, this study describes direction selective signals in the dendrites of T4 and T5 neurons, detectors of local motion. This circuit performs selective amplification of local light inputs, an observation that constrains motion detection models and confirms a core prediction of the Hassenstein-Reichardt correlator (HRC). These neurons are also orientation selective, responding strongly to static features that are orthogonal to their preferred axis of motion, a tuning property not predicted by the HRC. This coincident extraction of orientation and direction sharpens directional tuning through surround inhibition and reveals a striking parallel between visual processing in flies and vertebrate cortex, suggesting a universal strategy for motion processing (Fisher, 2015).

Each time a locomoting fly turns, the visual image sweeps over the retina and generates a motion stimulus. Classic behavioral experiments have suggested that flies use active neural-circuit mechanisms to suppress the perception of self-generated visual motion during intended turns. Direct electrophysiological evidence, however, has been lacking. This study found that visual neurons in Drosophila receive motor-related inputs during rapid flight turns. These inputs arrive with a sign and latency appropriate for suppressing each targeted cell's visual response to the turn. Precise measurements of behavioral and neuronal response latencies support the idea that motor-related inputs to optic flow-processing cells represent internal predictions of the expected visual drive induced by voluntary turns. Motor-related inputs to small object-selective visual neurons could reflect either proprioceptive feedback from the turn or internally generated signals. These results in Drosophila echo the suppression of visual perception during rapid eye movements in primates, demonstrating common functional principles of sensorimotor processing across phyla (Kim, 2015).

Humans scanning a visual scene show periods of stable gaze punctuated by rapid eye movements called saccades. During saccades, the visual image translates briskly over the retina and the nervous system employs mechanisms to suppress the perception of such self-induced motion stimuli so as to help perceive the outside environment as stationary. Primate saccades represent just one example in which sensorimotor processing must distinguish between self-generated sensory stimulation, also known as reafference, from externally generated stimulation, or exafference. In a few model systems, there is now even a developed understanding of how neuronal circuits distinguish exafference from reafference. For example, male crickets have an identified interneuron that activates during chirps and inhibits auditory neurons to prevent them from responding to the chirp, whereas weakly electric fish use circuitry in a cerebellum-like structure to subtract the predicted sensory input, resulting from the animal's own electric organ discharge from the incoming electrical sensory stream. One common scenario in which the suppression of reafference is essential, but remains poorly understood, is in the inhibition of stability reflexes during voluntary locomotor turns. Consider a flying fly. Analogously with human eye movements, the fly punctuates periods of stable flight with rapid turns called body saccades. Between saccades, the fly employs an optokinetic reflex, also known as the optomotor response, to help maintain stable flight. In this reflex, wide-field visual motion, say, to the right, is interpreted as being caused by an erroneous turn of the fly to the left (perhaps caused by a gust of wind or noise in the flight motor) and a corrective rightward turn is elicited. Although this reflex is important for stability, if it were always active, it would act against any intended change to the locomotor trajectory. This begs the question of how then do flies ever turn (Kim, 2015).

Guided by simple behavioral experiments, von Holst and Mittelstaedt famously postulated that with each motor command to initiate a voluntary locomotor turn, also known as an efference, flies send a copy of the command, an efference copy, to their visual system. This efference-copy signal was postulated to have the correct sign and magnitude for silencing the reafferent visual input caused by voluntary turns, thereby preventing the optomotor response from kicking in. Subsequent behavioral experiments have continued to argue that locomoting insects send efference copies to their visual system or make use of internal models in their visuomotor processing; however, electrophysiological evidence has been scarce (Kim, 2015).

This study found that Drosophila visual neurons received motor-related inputs during voluntary body saccades. These inputs were similar in magnitude, but opposite in sign, to the expected reafferent visual input caused by saccades. Responses of wide field-sensitive neurons in the visual lobe and a new class of small object-selective cells in the central brain were strongly suppressed during intended flight turns. Other visual cells, whose receptive-field properties were such that they should not respond to wide-field motion during saccades, were only mildly affected. These findings demonstrate cell type-tailored signals that are appropriate for silencing reafferent visual responses during voluntary locomotor turns in flies, as predicted by von Holst and Mittelstaedt 65 years ago (Kim, 2015).

Drosophila visual neurons receive both visually driven and motor-related inputs with voluntary saccades. Motor-related inputs were tailored in sign and latency to effectively suppress neuronal responses to the reafferent visual motion resulting from saccades. Motor-related modulations are observed in cells that arborize in two late stages of visual processing, the lobula plate and the optic glomeruli, and these modulations are sufficiently strong to cancel, rather than just weakly modulate, visual signaling (Kim, 2015).

Von Holst and Mittelstaedt were careful to distinguish silencing of expected visual motion, the computation they believed to take place in fly brains, from complete blindness during voluntary turns. At the cellular level, some visual neurons, such as VS1-2 cells, did not receive prominent motor-related inputs (SRPs or saccade related potentials) during voluntary saccades, and other cells, such as horizontal system north (HSN) cells, were modified in a direction-selective manner that should have allowed them to still respond to unexpected (exafferent) visual motion. Thus, although flies suppress visual input during rapid flight turns, it is unlikely that they are completely blind supporting von Holst and Mittelstaedt's model. In many scenarios, it might make sense for motion-sensitive neurons to actually sense reafference; however, because HSN cells likely contribute to optomotor stability, eliminating reafference in their output signal during voluntary turns is sensible. It has been argued, based on behavioral experiments, that flying flies completely ignore certain visual motion during saccades or selectively ignore visual motion depending on whether this motion is in the expected or unexpected direction. The current data provide a plausible cellular explanation for these behavioral results (Kim, 2015).

Although the fly visual system, overall, shows cell type-specific and direction-selective silencing, it is not yet clear whether von Holst and Mittelstaedt's computation is instantiated in its full form in the Vm of single cells. To do so, each visual neuron would receive a saccade-associated input that is not just of the correct sign, but also of the correct time-varying magnitude to exactly cancel the expected visual drive associated with each voluntary turn. Activating such a negative-image input would require that fly brains instantiate a forward model to predict the visual drive that each neuron will experience from a given saccade. In realizing such a model, flies should scale their silencing signals by an internal estimate of the velocity time course of the upcoming saccade (using a so-called inverse model), as well as by how strongly the current visual environment (for example, a forest or a fog) is expected to drive each visual neuron during each saccade. If flies make use of internal models of this sort, it will be important to determine how they are implemented in the nervous system and how widespread their influence is on sensory processing and behavior (Kim, 2015).

At face value, the fact that SRPs grew in magnitude when flies generated saccades in the context of a preferred steady-state stimulus compared with a uniformly lit screen and the fact that this growth of SRP magnitude was quantitatively matched to the level of ongoing visual drive supports the forward model idea. Note, however, that if saccade-related inputs activate a consistent membrane conductance, independent of visual context, for saccades of a certain direction and size, then saccade-related potentials will naturally grow in size as the cell's Vm moves further away from the reversal potential associated with that conductance. Given that ongoing visual stimulation causes cells to depolarize or hyperpolarize from the resting Vm, SRPs may grow in magnitude as a result of this reason alone. Future work will be needed to differentiate this simple biophysical explanation for why SRPs grow in magnitude during ongoing visual drive, which may represent a rudimentary implementation of a forward model, from a more sophisticated process in which the strength of motor-related conductances in visual neurons are actively scaled on the basis of the structure of the visual environment. Notably, HS cells appear to show similar visual responses to moving natural scenes over a wide range of contrast levels and arrangements of local features40. This fact may allow an efference copy system to get away with injecting silencing signals that have a consistent time-varying profile for saccades of a given direction and magnitude, independent of the structure of the visual environment. Flies may also continuously calibrate the strength of their efference copy signal based on the difference between predicted and experienced sensory feedback (Kim, 2015).

Regardless of whether motor-related inputs are scaled by the structure of the visual environment, an important associated question is whether, in a fixed visual environment, the motor-related inputs are scaled by the magnitude and duration of each saccade. Because torque is not measured in current platform, even if L-R WBA acts as a decent proxy, and because tethered-flight saccades are known to have altered dynamics relative to free-flight saccades, it is difficult to provide a definitive answer to this question with current methods (Kim, 2015).

Classically, only neurons that contribute to optomotor stability, such as HSN cells, should be silenced during voluntary turns. This study found that small object-selective optic-glomeruli interneurons (OGINs) were also silenced. Why might this be? Object-selective OGINs may contribute to behaviors such as small object avoidance during flight or tracking of conspecifics during Drosophila courtship. Distinguishing reafference from exafference would seem to be critical for such object-orienting behaviors to prevent behavioral responses to object motion on the retina caused by the fly's own movements. When a locomoting fly turns in a cluttered visual environment, the image of the entire cluttered panorama, not just that of the small object, translates globally on the retina, and such a global stimulus, simulated by the grating in receptive field mapping experiments, will not excite these OGINs. Thus, the native stimulus selectivity of these cells already helps to distinguish exafferent from reafferent object motion. However, if a single object were situated on a sparse background, such as a spider against a homogenous blue sky, OGINs may very well respond to the reafferent motion of such an object during a locomotor turn and the motor-related silencing mechanism that is described would abrogate this deleterious sensory response (Kim, 2015).

Mechanistically, SRPs in OGINs are consistent with an inhibitory input that arrives on each saccade, either directly to these cells or to upstream neurons, to reduce feedforward excitatory drive. Because OGINs are spiking cells (although their spikes are often very small when measured at the soma), the role of SRPs in these neurons may be to simply eliminate spike output during saccades rather than to activate a precisely time-varying negative-image input. Indeed, SRPs in OGINs lasted ~400-500 ms, which is longer than the time course of the expected reafference during a typical saccade. If OGIN SRPs are the result of mechanosensory feedback associated with saccades, this feedback signal would be expected to be prolonged in tethered flight, as tethered-flight saccades lasted longer (~300-500 ms) than free-flight saccades (~50-150 ms). By contrast, HSN cells are non-spiking neurons that signal both with hyperpolarizations and depolarizations of Vm. As such, HSN cells received both depolarizing and hyperpolarizing SRPs, whose duration (~150-200 ms) was more closely matched to the expected reafference from a typical saccade. SRPs in HSN cells may serve a function closer to that of a negative image of the expected visual drive (Kim, 2015).

The average SRP in HSN cells begins ~30 ms before the wings initiate a saccade, arguing for an internal (rather than sensory feedback) origin for these signals. However, if the sole function of motor-related inputs to HSN cells were to silence visual reafference, one might expect the efference-copy signal to kick in only after the body starts turning, once reafferent visual input is arriving at the cell. One intriguing possibility is that the early component of the motor-related input to HSN cells might help to actually drive the voluntary turn by injecting a small pre-charging signal into the optomotor reflex system, hijacking its natural coupling to the neck and flight motor systems. The sign of this early component is consistent with this possibility, although a rigorous test will require a specific manipulation of the SRPs to the optomotor system, which likely includes many more cells than just HS cells. Flies stabilize flight not just with vision, but also with mechanosensory inputs from their modified hindwings, called halteres, and a similar pre-charging idea was postulated to occur in the haltere stability system during turns, a hypothesis that should now be revisited (Kim, 2015).

Dynamic modulations of visual signaling have been studied primarily during saccadic eye movements in primates. The current results open the door to studying cellular mechanisms for similar processes in Drosophila. For vision research, flies have already offered key insights into state-dependent sensory processing and the circuit basis for direction selectivity. Drosophila may now help in understanding how brains build forward models and how they use these models to modify sensory processing. The tiny fly brain may not perform these tasks in exactly the same manner as the primate brain. However, the advanced genetic and physiological tools in Drosophila should allow for a detailed cellular- and circuit-level description of how the fly brain models and predicts the outside world. This description could yield insight on how similar predictive processes are implemented in all brains (Kim, 2015).

Animals typically combine inertial and visual information to stabilize their gaze against confounding self-generated visual motion, and to maintain a level gaze when the body is perturbed by external forces. In vertebrates, an inner ear vestibular system provides information about body rotations and accelerations, but gaze stabilization is less understood in insects, which lack a vestibular organ. In flies, the halteres, reduced hindwings imbued with hundreds of mechanosensory cells, sense inertial forces and provide input to neck motoneurons that control gaze. These neck motoneurons also receive input from the visual system. Head movement responses to visual motion and physical rotations of the body have been measured independently, but how inertial information might influence gaze responses to visual motion has not been fully explored. In this study, the head movement responses to visual motion were measured in intact and haltere-ablated tethered flies to explore the haltere's role in modulating visually-guided head movements in the absence of rotation. It is noted that visually-guided head movements occur only during flight. Although halteres are not necessary for head movements, the amplitude of the response is smaller in haltereless flies at higher speeds of visual motion. This modulation occurred in the absence of rotational body movements, demonstrating that the inertial forces associated with straight tethered flight are important for gaze-control behavior. The cross-modal influence of halteres on the fly's responses to fast visual motion indicates that the haltere's role in gaze stabilization extends beyond its canonical function as a sensor of angular rotations of the thorax (Mureli, 2017).

The fact that HS cell responses are remarkably robust while optomotor behavior can be quite variable as explicitly shown, for instance, for Calliphora illustrates that visual reflexes are not unimodal all-or-nothing events, but rather are subject to multimodal modification as well as to central gating. This might reflect the ethological need to conditionally modify or disable optomotor compensation by conflicting visual cues, other sensory modalities, or during certain internally generated flight maneuvers like saccades. Identification of further constituents of the optomotor pathways would provide opportunities to elucidate the neural basis for such multimodal stimulus integration, modulation, and gating of the optomotor response. To this end, flies expressing bistable channelrhodopsin-2 in combination with specific driver lines can help to establish the participation of candidate neurons. Using channelrhodopsin-2 variants with longer time constants than ChR2-C128S, the consequence of activating certain neurons could perhaps even be tested in freely behaving, unrestrained animals (Haikala, 2013).

Most of the neurons in the fly brain are dedicated to image processing. The respective part of the head ganglion, called the optic lobe, consists of several layers of neuropile called lamina, medulla, lobula and lobula plate, all built from repetitive columns arranged in a retinotopic way. Each column houses a set of identified neurons that, on the basis of Golgi staining, have been described anatomically, first by Santiago Ramon y Cajal (see Cajal, 1915), in great detail. Owing to their small size, however, most of these columnar neurons have never been recorded from electrophysiologically. Therefore, their specific functional role in visual processing is still largely unknown. This fact is contrasted by rather detailed functional models about visual processing inferred from behavioural studies and recordings from the large, electrophysiologically accessible output neurons of the fly lobula plate (tangential cells). As the most prominent example of such models, the Reichardt detector derives directional motion information from primary sensory signals by multiplying the output from adjacent photoreceptors after asymmetric temporal filtering. This model makes a number of rather counter-intuitive predictions all of which have been confirmed experimentally. Yet, the neurons corresponding to most of the circuit elements of the Reichardt detector have not been identified so far. This study focused on a set of neurons called T4 and T5 cells which, on the basis of circumstantial evidence, have long been speculated to be involved in motion detection. However, it is unclear to what extent T4 and T5 cells are directionally selective or whether direction selectivity is computed or enhanced within the dendrites of the tangential cells. Another important question concerns the functional separation between T4 and T5 cells; that is, whether they carry equivalent signals, maybe one being excitatory and the other inhibitory on the tangential cells, or whether they segregate into directional- and non-directional pathways or into separate ON- and OFF-motion channels (Maisak, 2013).

To answer these questions, Gal4-driver lines specific for T4 and T5 cells were combined with GCaMP5 (Akerboom, 2012) and the visual response properties were optically recorded using two-photon fluorescence microscopy. In a first series of experiments, a driver line labelling both T4 and T5 cells was used. A confocal image revealed clear labelling in the medulla (T4 cell dendrites), in the lobula (T5 cell dendrites), as well as in four distinct layers of the lobula plate, representing the terminal arborizations of the four subpopulations of both T4 and T5 cells. These four layers of the lobula plate can also be seen in the two-photon microscope when the calcium indicator GCaMP5 is expressed. After stimulation of the fly with grating motion along four cardinal directions (front-to-back, back-to-front, upwards and downwards), activity is confined to mostly one of the four layers, depending on the direction in which the grating is moving. The outcome of all four stimulus conditions can be combined into a single image by assigning a particular colour to each pixel depending on the stimulus direction to which it responded most strongly. From these experiments it is clear that the four subpopulations of T4 and T5 cells produce selective calcium signals depending on the stimulus direction, in agreement with previous deoxyglucose labelling. Sudden changes of the overall luminance evokes no responses in any of the layers. However, gratings flickering in counter-phase lead to layer-specific responses, depending on the orientation of the grating (Maisak, 2013).

The retinotopic arrangement of this input to the lobula plate is demonstrated by experiments where a dark edge was moved within a small area of the visual field only. Depending on the position of this area, activity of T4 and T5 cells is confined to different positions within the lobula plate. Consequently, when moving a bright vertical edge horizontally from back to front, activity of T4 and T5 cells is elicited sequentially in layer 2 of the lobula plate. These two experiments also demonstrate that T4 and T5 cells indeed signal motion locally. Next, the question of where direction selectivity of T4 and T5 cells arises was investigated; that is, whether it is already present in the dendrite, or whether it is generated by synaptic interactions within the lobula plate. This question is hard to answer, as the dendrites of both T4 and T5 cells form a dense mesh within the proximal layer of the medulla (T4) and the lobula (T5), respectively. However, signals within the inner chiasm where individual processes of T4 and T5 cells can be resolved in some preparations show a clear selectivity for motion in one over the other directions. Such signals are as directionally selective as the ones measured within the lobula plate, demonstrating that the signals delivered from the dendrites of T4 and T5 cells are already directionally selective (Maisak, 2013).

To assess the particular contribution of T4 and T5 cells to the signals observed in the above experiments, driver lines specific for T4 and T5 cells, respectively, were used. Applying the same stimulus protocol and data evaluation as described above, identical results were obtained as before for both the T4- as well as the T5-specific driver line. It is concluded that T4 and T5 cells each provide directionally selective signals to the lobula plate. Thus, both T4 and T5 cells can be grouped, according to their preferred direction, into four subclasses covering all four cardinal directions, reminiscent of ON–OFF ganglion cells of the rabbit retina (Maisak, 2013).

Next whether T4 cells respond differently to T5 cells was addressed. To answer this question, moving edges instead of gratings were used with either positive (ON edge, brightness increment) or negative (OFF edge, brightness decrement) contrast polarity as visual stimuli. It was found that T4 cells strongly responded to moving ON edges, but showed little or no response to moving OFF edges. This is true for T4 cells terminating in each of the four layers. The opposite was found for T5 cells. T5 cells selectively responded to moving OFF edges and mostly failed to respond to moving ON edges. Again, this was found for T5 cells in each of the four layers. Next, whether there are any other differences in the response properties between T4 and T5 cells was addressed by testing the velocity tuning of both cell populations by means of stimulating flies with grating motion along the horizontal axis from the front to the back at various velocities covering two orders of magnitude. T4 cells revealed a maximum response at a stimulus velocity of 30° s-1, corresponding to a temporal frequency of 1 Hz. T5 cell responses showed a similar dependency on stimulus velocity, again with a peak at a temporal frequency of 1 Hz. Thus, there is no obvious difference in the velocity tuning between T4 and T5 cells. As another possibility, T4 cells might functionally differ from T5 cells with respect to their directional tuning width. To test this, flies were stimulated with gratings moving into 12 different directions, and the relative change of fluorescence was evaluated in all four layers of the lobula plate. Using the T4-specific driver line, an approximate half width of 60-90° of the tuning curve was found, with the peak responses in each layer shifted by 90°. No decrease of calcium was detectable for grating motion opposite to the preferred direction of the respective layer. When the experiments were repeated using the T5-specific driver line, a similar dependence of the relative change of fluorescence was found on the stimulus direction. It is concluded that T4 cells have the same velocity and orientation tuning as T5 cells. The only functional difference that was detected remains their selectivity for contrast polarity (Maisak, 2013).

The finding about the different preference of T4 and T5 cells for the polarity of a moving contrast makes the strong prediction that selective blockade of T4 or T5 cells should selectively compromise the responses of downstream lobula plate tangential cells to either ON or OFF edges. To test this prediction, the output of either T4 or T5 cells was blocked via expression of the light chain of tetanus toxin, and the responses of tangential cells via somatic whole-cell patch was recorded to moving ON and OFF edges. In response to moving ON edges, strong and reliable directional responses were observed in all control flies. However, T4-block flies showed a strongly reduced response to ON edges, whereas the responses of T5-block flies were at the level of control flies. When moving OFF edges were used, control flies again responded with a large amplitude. However, the responses of T4-block flies were at the level of control flies, whereas the responses of T5-block flies were strongly reduced. These findings are reminiscent of the phenotypes obtained from blocking lamina cells L1 and L2 (Joesch, 2010) and demonstrate that T4 and T5 cells are indeed the motion-coding intermediaries for these contrast polarities on their way to the tangential cells of the lobula plate. Whether the residual responses to ON edges in T4-block flies and to OFF edges in T5-block flies are due to an incomplete signal separation between the two pathways or due to an incomplete genetic block in both fly lines is currently unclear (Maisak, 2013).

To address the question of whether T4 and T5 cells are the only motion detectors of the fly visual system, or whether they represent one cell class, in parallel to other motion-sensitive elements, tethered flies walking on an air-suspended sphere were used, and and they were stimulated by ON and OFF edges moving in opposite directions. As in the previous experiments, T4 and T5 cells were blocked specifically by selective expression of the light chain of tetanus toxin. During balanced motion, control flies did not show significant turning responses to either side. T4-block flies, however, strongly followed the direction of the moving OFF edges, whereas T5-block flies followed the direction of the moving ON edges. In summary, the selective preference of T4-block flies for OFF edges and of T5-block flies for ON edges not only corroborates the findings about the selective preference of T4 and T5 cells for different contrast polarities, but also demonstrates that the signals of T4 and T5 cells are indeed the major, if not exclusive, inputs to downstream circuits and motion-driven behaviours (Maisak, 2013).

Almost a hundred years after T4 and T5 cells have been anatomically described, this study reports their functional properties in a systematic way. Using calcium as a proxy for membrane voltage, this study found that both T4 and T5 cells respond to visual motion in a directionally selective manner and provide these signals to each of the four layers of the lobula plate, depending on their preferred direction. Both cell types show identical velocity and orientation tuning which matches the one of the tangential cells. The strong direction selectivity of both T4 and T5 cells is unexpected, as previous studies had concluded that the high degree of direction selectivity of tangential cells is due to a push–pull configuration of weakly directional input with opposite preferred direction. Furthermore, as the preferred direction of T4 and T5 cells matches the preferred direction of the tangential cells branching within corresponding layers, it is currently unclear which neurons are responsible for the null-direction response of the tangential cells. As for the functional separation between T4 and T5 cells, this study found that T4 cells selectively respond to brightness increments, whereas T5 cells exclusively respond to moving brightness decrements. Interestingly, parallel ON and OFF motion pathways had been previously postulated on the basis of selective silencing of lamina neurons L1 and L2 (Joesch, 2010). Studies using apparent motion stimuli to probe the underlying computational structure arrived at controversial conclusions: whereas some studies concluded that there was a separate handling of ON and OFF events by motion detectors, others did not favour such a strict separation. The present study directly demonstrates the existence of separate ON and OFF motion detectors, as represented by T4 and T5 cells, respectively. Furthermore, the results anatomically confine the essential processing steps of elementary motion detection -- that is, asymmetric temporal filtering and nonlinear interaction -- to the neuropile between the axon terminals of lamina neurons L1 and L2 (Joesch, 2013) and the dendrites of directionally selective T4 and T5 cells. The dendrites of T4 and T5 cells might well be the place where signals from neighbouring columns interact in a nonlinear way, similar to the dendrites of starburst amacrine cells of the vertebrate retina (Maisak, 2013).

Neuronal computation underlying detection of visual motion has been studied for more than a half-century. In Drosophila, direction-selective T4/T5 neurons show supralinear signal amplification in response to stimuli moving in their preferred direction, in agreement with the prediction made by the Hassenstein-Reichardt detector. Nevertheless, the molecular mechanism explaining how the Hassenstein-Reichardt model is implemented in T4/T5 cells has not been identified yet. The present study utilized cell type-specific transcriptome profiling with RNA-seq to obtain a complete gene expression profile of T4/T5 neurons. The expression was analyzed of genes that affect neuronal computational properties and can underlie the molecular implementation of the core features of the Hassenstein-Reichardt model to the dendrites of T4/T5 neurons. Furthermore, the acquired RNA-seq data was used to examine the neurotransmitter system used by T4/T5 neurons. Surprisingly, co-expression of the cholinergic markers and the vesicular GABA transporter was observed in T4/T5 neurons. Previously undetected expression of vesicular GABA transporter was documented in T4/T5 cells using VGAT-LexA knock-in line. The provided gene expression dataset can serve as a useful source for studying the properties of direction-selective T4/T5 neurons on the molecular level (Pankova, 1016).

How neurons become sensitive to the direction of visual motion represents a classic example of neural computation. Two alternative mechanisms have been discussed in the literature so far: preferred direction enhancement, by which responses are amplified when stimuli move along the preferred direction of the cell, and null direction suppression, where one signal inhibits the response to the subsequent one when stimuli move along the opposite, i.e. null direction. Along the processing chain in the Drosophila optic lobe, directional responses first appear in T4 and T5 cells. Visually stimulating sequences of individual columns in the optic lobe with a telescope while recording from single T4 neurons, this study found both mechanisms at work implemented in different sub-regions of the receptive field. This finding explains the high degree of directional selectivity found already in the fly's primary motion-sensing neurons and marks an important step in understanding of elementary motion detection (Haag, 2016).

In the visual motion pathways contained within the fly's optic lobe, two cell types-T4 and T5-are the first known relay neurons to signal small-field direction-selective motion responses (see Graphical abstract). These cells then feed into large tangential cells that signal wide-field motion. Recent studies have identified two types of columnar neurons in the second neuropil, or medulla, that relay input to T4 from L1, the ON-channel neuron in the first neuropil, or lamina, thus providing a candidate substrate for the elementary motion detector (EMD). Interneurons relaying the OFF channel from L1's partner, L2, to T5 are so far not known, however. This study report that multiple types of transmedulla (Tm) neurons provide unexpectedly complex inputs to T5 at their terminals in the third neuropil, or lobula. From the L2 pathway, single-column input comes from Tm1 and Tm2 and multiple-column input from Tm4 cells. Additional input to T5 comes from Tm9, the medulla target of a third lamina interneuron, L3, providing a candidate substrate for L3's combinatorial action with L2. Most numerous, Tm2 and Tm9's input synapses are spatially segregated on T5's dendritic arbor, providing candidate anatomical substrates for the two arms of a T5 EMD circuit; Tm1 and Tm2 provide a second. Transcript profiling indicates that T5 expresses both nicotinic and muscarinic cholinoceptors, qualifying T5 to receive cholinergic inputs from Tm9 and Tm2, which both express choline acetyltransferase (ChAT). It is hypothesized that T5 computes small-field motion signals by integrating multiple cholinergic Tm inputs using nicotinic and muscarinic cholinoceptors (Shinomiya, 2014).

The L2 pathway has been identified as the substrate for detecting moving dark edges. L2's partner cell, L4, and its common medulla target, Tm2, are both essential components of this dark-edge pathway prior to the computation of directionality, which occurs first in the dendrites of T5. Tm2 and L4 cells both respond with a nondirectional increase in activity to moving dark (OFF) edges, and silencing of both either singly or in combination also abolishes the response to moving dark edges in downstream LPTCs. The anatomical receptive fields described in this report for the input terminals to T5 cell dendrites are compatible with Tm2's being wired as one of the two arms of an EMD circui and with Tm1, Tm9, or both as the other. The existence of two EMD circuits would require that the inputs from Tm1/Tm2 and Tm9/Tm2 have aligned vector angles, since each T5 receives both circuits but must respond to dark-edge motion in only one of the four cardinal directions. This alignment is clear for three of the T5 cells plotted in detail, but less so for T5-08. Clearly, additional detailed plots of T5 anatomical receptive fields are needed (Shinomiya, 2014).

Even though this scheme is still highly speculative, nonlinear interaction between two input arms of the EMD circuits is the computational basis of local motion-detection models, while specific computational models favor different types of interactions: either multiplication or facilitation for the Hassenstein-Reichardt model or inhibition for the Barlow-Levick model (see Motion sensing in vision) (Shinomiya, 2014).

A parallel may be seen in the vertebrate retina. There, turtle B10 bipolar cells use ionotropic and metabotropic glutamate receptors to signal, respectively, L and M cone inputs, a dual deployment that has been suggested to form the basis for B10 red-ON, blue/green-OFF color opponency. The expression of both nicotinic and muscarinic cholinoceptors in T5 may provide a similar means to integrate multiple cholinergic Tm inputs and so compute small-field motion signals. Vertebrate muscarinic receptors are coupled to G proteins and various downstream signaling pathways to regulate a broad spectrum of cellular functions, including neuronal excitability. In Drosophila, agonist activation of mAchR-A acts via the IP3 pathway to increase calcium release from internal stores, which elsewhere is reported in turn to activate high-conductance calcium-dependent potassium (BK) channels (the Slowpoke channel), leading to membrane hyperpolarization. It was therefore interesting that, using single-cell RT-PCR, slowpoke transcripts were detected in both T4 and T5, consistent with a previous immunohistochemical study that Slowpoke is expressed in the optic lobe, including the lobula. It is proposed that these two postsynaptic events, mAchR-A-mediated increased intracellular calcium and mAchR activation of Slowpoke channels, occur at postsynaptic sites activated by different inputs distributed over the T5 dendritic arbor. Given the relative temporal inflexibility of an excitatory nicotinic cholinoceptor synaptic response and the fact that their anatomy qualifies Tm2/Tm9 and Tm2/Tm1 input pairs as two independent pairs of EMD input arms to T5 that share Tm2, it seems most reasonable that T5 uses the nicotinic receptor inputs for fast excitation from Tm2 (as the instantaneous signal) and compares this with a slow inhibitory input from either Tm9 or Tm1 (as the delayed signal). This arrangement could allow T5 to inherit motion information with two temporal characteristics provided that Tm1 and Tm9 have their own time delay. The coupling of the inhibitory inputs to secondary messenger system also provides a potential mechanism to adapt the temporal delay filter. In addition, the activation of Drosophila mAchR-A in cultured cells also induces a secondary calcium influx, potentially originating from an extracellular calcium pool, while the activation of vertebrate muscarinic receptors has been shown to inhibit potassium channels, including those of the Kv7 (KCNQ/M) type, and to lower the excitability threshold. It is therefore possible that an interaction between the nicotinic and muscarinic inputs provides some form of multiplication, as suggested in the Hassenstein-Reichardt model. Further definition of the roles of the Tm1, Tm2, and Tm9 input cells as input arms to different EMD circuits and determination of the synaptic mechanisms for detecting moving dark edges must obviously await additional genetic silencing experiments and electrophysiological recordings from all these cells, which this study now clearly identifies as synaptic inputs to T5 in Drosophila (Shinomiya, 2014).

In the fly optic lobe, approximately 800 highly stereotypical columnar microcircuits are arranged retinotopically to process visual information. Differences in cellular composition and synaptic connectivity within functionally specialized columns remain largely unknown. This study describes the cellular and synaptic architecture in medulla columns located downstream of photoreceptors in the dorsal rim area (DRA), where linearly polarized skylight is detected for guiding orientation responses. Only in DRA medulla columns both R7 and R8 photoreceptors target to the bona fide R7 target layer where they form connections with previously uncharacterized, modality-specific Dm neurons: two morphologically distinct DRA-specific cell types (termed Dm-DRA1 and Dm-DRA2) stratify in separate sublayers and exclusively contact polarization-sensitive DRA inputs, while avoiding overlaps with color-sensitive Dm8 cells. Using the activity-dependent GRASP and trans-Tango techniques, it was confirmed that DRA R7 cells are synaptically connected to Dm-DRA1, whereas DRA R8 form synapses with Dm-DRA2. Finally, using live imaging of ingrowing pupal photoreceptor axons, it was shown that DRA R7 and R8 termini reach layer M6 sequentially, thus separating the establishment of different synaptic connectivity in time. It is proposed that a duplication of R7-->Dm circuitry in DRA ommatidia serves as an ideal adaptation for detecting linearly polarized skylight using orthogonal e-vector analyzers (Sancer, 2019a).

Natural scenes contain complex visual cues with specific features, including color, motion, flicker and position. It is critical to understand how different visual features are processed at the early stages of visual perception to elicit appropriate cellular responses, and even behavioral output. The visual orientation response induced by flickering stripes in a novel behavioral paradigm in Drosophila melanogaster. This study found that free walking flies exhibited bandpass orientation response to flickering stripes of different frequencies. The most sensitive frequency spectrum was confined to low frequencies of 2-4 Hz. Through genetic silencing, this study showed that lamina L1 and L2 neurons, which receive visual inputs from R1-R6 neurons, were the main components in mediating flicker-induced orientation behavior. Moreover, specific blocking of different types of lamina feedback neurons Lawf1, Lawf2, C2, C3 and T1 modulated orientation responses to flickering stripes of particular frequencies, suggesting that bandpass orientation response was generated through cooperative modulation of lamina feedback neurons. Furthermore, this study found that lamina feedback neurons Lawf1 were glutamatergic. Thermal activation of Lawf1 neurons could suppress neural activities in L1 and L2 neurons, which could be blocked by the glutamate-gated chloride channel inhibitor picrotoxin (PTX). In summary, lamina monopolar neurons L1 and L2 are the primary components in mediating flicker-induced orientation response. Meanwhile, lamina feedback neurons cooperatively modulate the orientation response in a frequency-dependent way, which might be achieved through modulating neural activities of L1 and L2 neurons (Yuan, 2020).

The optic lobes of the fruit fly Drosophila melanogaster form a highly wired neural network composed of roughly 130.000 neurons of more than 80 different types. This study use the optic lobe of the fruit fly as a paradigm to understand how neuroblasts generate multiple neuron types. A large-scale lineage bioinformatics analysis was performed using the graph theory. A large collection of cell clones were generated that genetically label the progeny of neuroblasts, and a database was built to draw graphs showing the lineage relationships between cell types. By establishing biological criteria that measures the strength of the neuronal relationships and applying community detection tools, eight clusters of neurons were identified. Each cluster contains different cell types that are posed to be the product of eight distinct classes of neuroblasts. Three of these clusters match the available lineage data, supporting the predictive value of the analysis. Finally, it was shown that the neuronal progeny of a neuroblast do not have preferential innervation patterns, but instead become part of different layers and neuropils. This study established a new methodology that helps understanding the logic of Drosophila brain development and can be applied to the more complex vertebrate brains (Del Valle Rodriguez, 2020).

Sensory systems need to reliably extract information from highly variable natural signals. Flies, for instance, use optic flow to guide their course and are remarkably adept at estimating image velocity regardless of image statistics. Current circuit models, however, cannot account for this robustness. This study demonstrates that the Drosophila visual system reduces input variability by rapidly adjusting its sensitivity to local contrast conditions. Functional properties were exhaustively mapped of neurons in the motion detection circuit; local responses were found to be compressed by surround contrast. The compressive signal is fast, integrates spatially, and derives from neural feedback. Training convolutional neural networks on estimating the velocity of natural stimuli shows that this dynamic signal compression can close the performance gap between model and organism. Overall, this work represents a comprehensive mechanistic account of how neural systems attain the robustness to carry out survival-critical tasks in challenging real-world environments (Drews, 2020).

aThe anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. This study characterized gene expression patterns in distinct neural cell types of the Drosophila visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. These tools were used to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. Examples are described that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types (Davis, 2020).

Visual perception scales with changes in the visual stimulus, or contrast, irrespective of background illumination. However, visual perception is challenged when adaptation is not fast enough to deal with sudden declines in overall illumination, for example, when gaze follows a moving object from bright sunlight into a shaded area. This study shows that the visual system of the fly employs a solution by propagating a corrective luminance-sensitive signal. In vivo 2-photon imaging and behavioral analyses were used to demonstrate that distinct OFF-pathway inputs encode contrast and luminance. Predictions of contrast-sensitive neuronal responses show that contrast information alone cannot explain behavioral responses in sudden dim light. The luminance-sensitive pathway via the L3 neuron is required for visual processing in such rapidly changing light conditions, ensuring contrast constancy when pure contrast sensitivity underestimates a stimulus. Thus, retaining a peripheral feature, luminance, in visual processing is required for robust behavioral responses (Ketkar, 2020).

Sensory systems have evolved to detect changes rather than absolute inputs from their environment. For example, while it is possible to forget that glasses are perched on top of our head, insect landing on our skin can be felt. Similarly, visual systems are well suited to detect changes in light intensity. In many contexts, changes in perception are proportional to relative changes in a stimulus (Weber contrast). The ability to process such changes in sensory input under changing conditions is crucial to the behavior and survival of many organisms (Ketkar, 2020).

In vision, the most basic characteristic of a visual stimulus is the distribution of luminance, which is the luminous intensity per unit area. Rather than responding to luminance, many cells in the visual system instead respond to contrast. Contrast can be defined in the temporal or spatial domain. This study examined temporal contrast, which corresponds to the relative change in luminance (Weber contrast) over time at a single point in space. This study describes a mechanism that ensures contrast constancy, a visual phenomenon that allows discrimination of a visual feature from its background solely based on its contrast, regardless of the global light level (Ketkar, 2020).

Many visual systems are well suited to function at dusk, dawn, or in daylight as well as in rapidly changing environments. In both vertebrates and invertebrates, photoreceptor adaptation is a key mechanism that ensures contrast computation irrespective of background illumination. Photoreceptor adaptation happens across different timescales that range from tens of milliseconds to tens of seconds. However, animals frequently encounter close-to-instantaneous changes in luminance that far exceed photoreceptor adaptation. For example, during self-motion, saccadic eye movements, or when gaze follows a moving object into the shade, background luminance changes within milliseconds. A failure to adjust light sensitivity equally fast would lead to inaccurate calculation of contrast and a misinterpretation of salient cues. Therefore, the timescale of adaptation might limit the capability of the visual system to ensure contrast constancy in fast-changing light conditions, and thus may be alone insufficient to explain robust visual behaviors under changing conditions (Ketkar, 2020).

Core visual circuits have been mapped in the fruit fly Drosophila, which responds to visual cues at luminances spanning orders of magnitude. Briefly, information passes from the retina through the lamina and medulla to the lobula complex. Visual information is computed in 800 parallel channels, together forming a retinotopic array. Downstream of photoreceptors, visual circuitry splits into pathways that are specialized to detect contrast increments (ON) or decrements (OFF). The lamina neuron L1 forms the principal input to the ON pathway, whereas L2 and L3 are major input neurons to the OFF pathway. Together, L1-L3 are referred to as large monopolar cells (LMCs). Based on behavioral experiments, L2 and L3 were considered to mediate fast and slow motion-processing pathways, respectively. However, while L1 and L2 show transient responses to light and biphasic filtering properties, L3 responses are sustained and display a monophasic linear temporal filter. This argues that L2 and L3 are sensitive to entirely different features of the visual scene (Ketkar, 2020).

In flies, voltage recordings have shown that the photoreceptor cells respond to prolonged, bright illumination with an initial transient phase, which encodes contrast, followed by a luminance-sensitive plateau, which lasts for the duration of the stimulus. Downstream LMCs are thought to amplify photoreceptor contrast signals while discarding information about constant illumination. Such contrast responses in LMCs have been extensively characterized in flies that were adapted to different mean luminances. However, it is not known how the visual system deals with contrast signals in a non-adapted state. Ideally, an animal should be able to show invariant responses to contrast under rapidly changing light conditions. To achieve contrast constancy under such conditions, information about illumination might be advantageous. Interestingly, in the vertebrate retina, luminance sensitivity is retained past photoreceptors at the bipolar cell to amacrine cell synapse. Furthermore, intrinsically photosensitive retinal ganglion cells can contribute to image-forming visual functions. However, in the Drosophila visual system, a luminance-sensitive component has not been described past photoreceptors. Furthermore, it is not known whether luminance indeed contributes to image processing past the initial detection stage (Ketkar, 2020).

This study has uncover a luminance-sensitive pathway in the Drosophila visual system. Contrast-sensitive neuronal responses alone are insufficient to account for behavioral responses to changing visual stimuli, arguing for the presence of a corrective signal that scales contrast-sensitive responses when light levels suddenly decline. Through a series of in vivo calcium-imaging experiments, this study show that the two input neurons to the OFF pathway, L2 and L3, encode contrast and luminance, respectively. Luminance information from L3 is necessary for appropriate behavioral responses to visual cues when background luminance suddenly becomes dim. This is true across a range of adaptation states. The data demonstrate that luminance information is retained in a specialized L3 pathway and used as a corrective signal that ensures contrast constancy. This work thus highlights a novel mechanism that allows accurate image processing under dynamically changing light conditions (Ketkar, 2020).

This study has demonstrated that contrast sensitivity alone is not sufficient to explain the behavioral response to visual stimuli. Luminance, the primary input to the visual system, is additionally required for behavioral responses. This study has shown that the two OFF pathway neurons, L2 and L3, are sensitive to fundamentally different visual features, contrast, and luminance. The presence of a luminance-sensitive pathway boosts behavioral performance where contrast pathways prove inadequate, demonstrating a role for luminance in image processing. The specific requirement of luminance signals in behavior, along with the underlying L3 physiology, are consistent across a range of adaptation states. Together, the data suggest that visual processing can handle changing light conditions because the basic luminance feature is passed on by the early fly visual system and is utilized to modulate further computations in a behaviorally relevant way (Ketkar, 2020).

This study has shown that a sustained component in the lamina neuron L3 is sensitive to luminance. First-order visual interneurons have been characterized extensively, and canonical LMCs are generally thought to be 'amplified and more transient versions of photoreceptor responses' and, therefore, contrast sensitive. This contrast sensitivity is visible in L2 calcium responses. However, it was noted that L3 is physiologically distinct and has a more pronounced sustained component and a slower temporal filter compared with those of L1 and L2. Electrophysiological recordings showed that the L3 membrane potential did not return to baseline upon prolonged visual stimulation. The data show that this is more pronounced for calcium signals and that L3 responds to a step change in luminance with a step change in GCaMP signal. Luminance information is retained in a specialized L3-mediated pathway in parallel to contrast-sensitive pathways to facilitate visual processing under conditions that are challenging for purely contrast-sensitive pathways (Ketkar, 2020).

The parallel presence of sustained (tonic) and transient (phasic) responses has been observed in different neural systems. Examples include proprioceptive neurons, where tonic and phasic neurons encode joint position and movement, or mechanosensory neurons from Johnston's organ, where tonic and phasic responses encode antennal deflection and vibration, respectively (Ketkar, 2020).

The findings suggest that a behavioral response to a stimulus of varying temporal contrast in the Drosophila OFF pathway is computed in two phases -- first, contrast-sensitive LMCs, such as L2, report the luminance difference relative to the adapted luminance, and this signal is then subjected to corrective modulation with the luminance information from the L3 pathway. In species ranging from flies to humans, photoreceptor adaptation mechanisms operate on different timescales, typically a faster one that takes a fraction of a second and a slower one that lasts several seconds or minutes. These processes allow visual systems to operate under a wide range of luminances to detect contrast as a salient feature in the environment. However, fast changes in mean luminance caused by a shadow or by an animal's own movement would cause high contrasts to elicit only very small photoreceptor responses if the adaptation state was not yet fully caught up. Consequently, signals encoded by contrast-sensitive neurons would underrepresent the physical contrast. The presence of a luminance-sensitive pathway appears to overcome these limitations to ensure accurate motion detection in sudden dim light. This could happen through excitatory interactions between the L3 and the L2 pathways. At low luminance, when L3 is most active, such an interaction would amplify signals from contrast-sensitive neurons and thereby allow for a behaviorally adequate response. Such an interaction between luminance- and contrast-sensitive channels might be a general mechanism implemented in visual systems to explain robust behavioral responses when adaptation timescales are insufficient (Ketkar, 2020).

Luminance and contrast signals must ultimately be combined to control motion-guided behaviors. This could for example happen in the transmedullary (Tm) or medulla intrinsic (Mi) neurons that are postsynaptic to LMCs. One major postsynaptic partner of L3, Tm9, possesses similarly sustained responses to those seen in L3. Interestingly, Tm9 has wide receptive field properties, potentially receiving input from several neighboring columns. Thus, Tm9 could serve to integrate over space to identify a change in illumination and help to distinguish global from local changes in signal. Tm9 responses further depend on the biphasic ON pathway input L1, arguing that transient and sustained responses are combined at this stage. All other major medulla OFF pathway interneurons receive predominant input from L2 and have transient response kinetics. Medulla neurons of the ON pathway can also be separated into sustained and transient types, and connectomics suggests that L3 could provide luminance information to both ON and OFF pathways. Thus, relative lamina neuron contributions might determine downstream response properties. Further integration could happen in the dendrites of the downstream direction-selective T4 and T5 cells. Luminance information might simply act on contrast signals to scale them before they are spatiotemporally compared. The addition of a DC component also enhanced predictions of motion responses to slow apparent motion stimuli and reverse-phi illusion. Luminance-sensitive L3 responses may thus provide the neural substrates for conveying this DC component to downstream visual circuitry. Finally, L3 feeds into the color pathway by for example providing input to Dm9 cells and could thus help establish color constancy (Ketkar, 2020).

The finding that the presence of luminance information downstream of photoreceptors allows for appropriate behavioral response could suggest a selective advantage for animals living in certain environments. It will be interesting to see if different species with specific ethological constraints have evolved similar luminance-sensitive pathways and how they facilitate visual behaviors. The requirement for such a pathway appears to be greater in animals encountering broad luminance variance over short time periods. Although local mean and variance in natural scenes vary independently, a positive correlation between them sometimes exists because of shadows. Because objects in bright sunlight tend to cast darker shadows, diurnal animals are more likely to encounter wide variance and to possess a luminance-sensitive channel. Nocturnal animals, on the other hand, may require luminance information to tackle challenges posed by low light levels. Therefore, luminance information preserved beyond photoreceptors might facilitate behavior in different ways depending on the species' ethology (Ketkar, 2020).

Physiologically different LMC subtypes have been described in different species. For example, LMCs in bumblebees and butterflies exhibit sustained components, and motion-sensitive neurons perform better than photoreceptor responses predict in the nocturnal hawkmoth. Interestingly, presynaptic L2 and L3 calcium signals are highly reminiscent of sustained and transient calcium signals in bipolar cells of the vertebrate retina. Information about luminance is retained past vertebrate photoreceptors at the rod bipolar cell to the AII amacrine cells synapse. The rod bipolar cell pathway is an important player specifically in dim light conditions. Together, these findings suggest that luminance information in the vertebrate retina is also used to aid image processing. In line with this idea, human eyes come across substantial variation in luminance every few milliseconds when inspecting natural scenes with saccades. It has been suggested that gain control to luminance fluctuations also occurs within the retinal circuitry and past photoreceptor adaptation. Therefore, the finding that the visual system uses luminance information to aid contrast computation may reveal a general image processing strategy utilized across phyla (Ketkar, 2020).

Many visual animals have innate preferences for particular wavelengths of light, which can be modified by learning. Drosophila's preference for UV over visible light requires UV-sensing R7 photoreceptors and specific wide-field amacrine neurons called Dm8. This study identified three types of medulla projection neurons downstream of R7 and Dm8 and has shown that selectively inactivating one of them (Tm5c) abolishes UV preference. Using a modified GRASP method to probe synaptic connections at the single-cell level, each Dm8 neuron was shown to form multiple synaptic contacts with Tm5c in the center of Dm8's dendritic field but sparse connections in the periphery. By single-cell transcript profiling and RNAi-mediated knockdown, it was determined that Tm5c uses the kainate receptor Clumsy to receive excitatory glutamate input from Dm8. It is concluded that R7s-->Dm8-->Tm5c form a hard-wired glutamatergic circuit that mediates UV preference by pooling approximately 16 R7 signals for transfer to the lobula, a higher visual center (Karuppudurai, 2014).

Understanding how visual systems translate light impulses into adaptively tuned percepts to guide behavior is a central goal in neurobiology. The Drosophila visual system, with its amenability to genetic manipulation, has enabled increasingly deep investigation of the molecular and cellular basis of visual-driven behaviors, including the spectral preference for UV light examined in this study. UV spectral preference has previously been shown to require first-order interneurons in the visual medulla (i.e., the wide-field amacrine Dm8 neurons) that receive inputs from multiple UV-sensing photoreceptors. This study shows that a subclass of Tm5 neurons, called Tm5c, receive excitatory glutamatergic input from Dm8 neurons through the kainate-type receptor Clumsy. Glutamatergic signaling, both to and by Tm5c, was shown to be necessary for normal UV preference. Together these results define not only critical elements of the molecular and cellular machinery underlying UV preference, but also patterns of connectivity and information flow at the first several processing stations of this important visual circuit (Karuppudurai, 2014).

By sparse reconstruction of serial-section transmission EM (ssTEM), Previous work identified Dm8 as the major postsynaptic partner for R7. This study shows that Tm5c interneurons are required for transducing Dm8's signal to the lobula, a higher visual center, in the UV preference pathway. The thin and complex dendrites of Tm5c make them challenging to resolve by ssTEM, due to the limited axial resolution (∼50 nm) of this method. To visualize Dm8->Tm5c and R8->Tm5c synapses, this study therefore resorted to the GRASP technique. To differentiate synaptic contacts from mere membrane contacts and to visualize the spatial distribution of Dm8->Tm5c synapses, the GRASP method was adapted to permit single-cell identification of presumptive presynaptic neurons in which active zones were fluorescently tagged. By applying this single-cell GRASP method, it was demonstrated that each Dm8 neuron has multiple synaptic contacts to one (or, at most a few) Tm5c neuron in the center of its dendritic field but sparse synaptic contacts in the periphery. The nature of synaptic signaling at the R8->Tm5c and Dm8->Tm5c contacts was established by single-cell transcript profiling and functional studies. Tm5c expresses the histamine-gated chloride channel Ort, and restoring Ort expression in Tm5c in an ort mutant background drove strong green preference. Dm8 expresses VGlut, and Tm5c correspondingly expresses glutamate-gated ionotropic receptors. RNAi-mediated knockdown of VGlut in Dm8, or the Clumsy iGluR in Tm5c, abolished UV preference. It is believed that the approach taken in this study, which combines single-cell GRASP, transcript profiling, RNAi-mediated knockdown, and behavioral assays, could be profitably applied to the dissection and characterization of other complex neural circuits (Karuppudurai, 2014).

Using single-cell transcript profiling, four kainate-type glutamate receptor subunits (Clumsy, CG11155, DKaiRIC, and DKaiRID) expressed in Tm5c were identified. These four iGluRs and CG9935 share sequence homology and domain structures with vertebrate kainate-type iGluRs (GluK1/2/3 and GluK4/5). RNAi-mediated knockdown further revealed that Clumsy is functionally required in Tm5c for UV preference. This demonstrates that kainate-type iGluRs function in the Drosophila CNS. In vertebrates, functional kainate receptors assemble tetramerically as dimers of dimers; GluK1-3 are capable of forming functional homotetramers while GluK4/5 are obligatory heteromers. Fly kainate receptor subunits share with vertebrates the key hydrophobic resides at the dimerization interfaces, suggesting that they assemble in a similar way to the vertebrate receptors. RNAi knockdown of CG11155, DKaiRIC, or DKaiRID did not enhance UV preference defects caused by RNAi knockdown of Clumsy. However, simultaneous RNAi knockdown of all three iGluR subunits significantly reduced UV preference, suggesting that they are functionally redundant. It is thus suggested that Clumsy forms functional heterotetramers with one of the other kainate receptors in Tm5c to mediate UV preference. Further in vitro assembly and electrophysiological studies will be needed to determine the exact subunit composition of the functional iGluRs. Ionotropic glutamate receptors in flies have been exclusively studied at the neuromuscular junction, in part as a surrogate model for CNS glutamate synapses. Functional identification of kainate-type iGluRs in the fly visual system, in combination with the robust UV preference behavior they mediate, opens the door to studying the assembly, function, and regulation of this important class of glutamate receptors in the Drosophila CNS (Karuppudurai, 2014).

As the wide-field Dm8 neurons have no presynaptic sites or axonal projections outside of the external medulla, they depend on medulla projection neurons to transduce signals to higher visual centers. While all three subtypes of Tm5 neurons appear to be postsynaptic to Dm8 and therefore are capable of transducing the Dm8 signal to the lobula, this study shows that only Tm5c is functionally required for UV preference. Inactivating Tm5a/b or Dm8 in addition to Tm5c did not enhance UV preference defects, suggesting that Dm8's function in UV preference is solely communicated through Tm5c. Tm5c differs from Tm5a/b in axonal morphology and neurotransmitter usage. It is thus suggested that Tm5c has unique synaptic functions and/or targets in that visual compartment that account for its role in UV preference (Karuppudurai, 2014).

The spatial organization of the R7s->Dm8->Tm5c circuit suggests a neural pooling mechanism for UV preference. Each Dm8 amacrine neuron has a large dendritic field that receives ∼16 R7 inputs, while a single Tm5c is present in most, if not all, medulla columns and receives direct retinotopic inputs from R8s. Single-cell GRASP experiments reveal that each Dm8 forms multiple synaptic contacts with one, or at most a few, Tm5c neurons in the center of Dm8's dendritic field but forms few synapses with Tm5c outside of the center. By pooling 16 R7 inputs to a single Tm5c, Dm8 could increase UV sensitivity by up to 16-fold at some cost in spatial resolution. It is interesting to note that the amplification magnitude of the R7s->Dm8->Tm5c circuit depends primarily on the size of the Dm8's dendritic field, which is negatively regulated by R7-derived Activin during development: excess Activin reduces Dm8's dendritic field size while lack of Activin enhances it (Ting, 2014). It is thus tempting to speculate that size of the Dm8 arbor, and thus the trade off between UV sensitivity and spatial resolution, has been adjusted in the course of insect evolution to meet each insect's ecological needs (Karuppudurai, 2014).

In addition to the excitatory glutamate input from Dm8, Tm5c neurons also receive inhibitory histaminergic inputs directly from R8 photoreceptors. Thus, the R7s->Dm8->Tm5c pooling circuit is superimposed on the retinotopic circuit R8->Tm5c. Tm5c expresses Ort, and restoring Ort expression in Tm5c in various ort mutant backgrounds rescued green phototaxis. Thus, both direct (R8->Tm5c) and indirect (R7s->Dm8->Tm5c) pathways transduce sign-inverting signals to Tm5c and both pathways are capable of driving phototaxis. However, RNAi-mediated knockdown of ort in Tm5c, which prevents the reception of R8 inputs, did not affect normal UV preference. This observation is consistent with previous conclusions that the indirect pathway through R7s->Dm8 is both required and sufficient for optimal UV preference, at least under the test condition, and that multiple redundant pathways mediate green phototaxis. While the direct pathway is not involved in UV preference, it might play a role in true color vision. Notably, chloride ions are a known modulator for kainate receptor and the direct pathway signals through the histamine-gated chloride channel Ort. Given that multiple pathways function redundantly in true color vision, differentiating these possibilities must await single-unit electrophysiological recording and/or new genetic techniques to segregate their functions (Karuppudurai, 2014).

Color and polarization provide complementary information about the world and are detected by specialized photoreceptors. However, the downstream neural circuits that process these distinct modalities are incompletely understood in any animal. Using electron microscopy, this study had systematically reconstructed the synaptic targets of the photoreceptors specialized to detect color and skylight polarization in Drosophila, and light microscopy was used to confirm many of the findings. Known and novel downstream targets were identified that are selective for different wavelengths or polarized light, and their projections were followed to other areas in the optic lobes and the central brain. The results revealed many synapses along the photoreceptor axons between brain regions, new pathways in the optic lobes, and spatially segregated projections to central brain regions. Strikingly, photoreceptors in the polarization-sensitive dorsal rim area target fewer cell types, and lack strong connections to the lobula, a neuropil involved in color processing. This reconstruction identifies shared wiring and modality-specific specializations for color and polarization vision, and provides a comprehensive view of the first steps of the pathways processing color and polarized light inputs (Kind, 2021).

Systematic reconstruction of all synaptic inputs and outputs of identified, functionally specialized Drosophila photoreceptors (pale and yellow R7-8 and two dorsal rim area photoreceptors, R7-DRA and R8-DRA) provides a comprehensive inventory of the first steps of the color and polarization pathways, from which all the computations of the dependent behaviors stem. These data revealed core connectomic motifs shared across column types, multiple new photoreceptor targets, and uncovered additional cell types as being connected to specific photoreceptor subtypes conveying specific color and polarization information to the central brain (Kind, 2021).

This study confirmed previously reported synaptic partners of the inner photoreceptors in the non-DRA medulla and identified new photoreceptor targets. As prior reconstructions were incomplete, it was unclear whether the unidentified connections were mainly onto new target neurons or represented more connections onto known cell types; reconstructions revealed both types of omissions. One functionally important set of missed connections are the synapses between inner photoreceptors from the same central and DRA ommatidia, which was found to be stronger than previously reported, due to significant numbers of synapses outside the medulla. These synapses likely contribute to color-opponent responses seen in central R7 and R8 terminals and the polarization-opponent signals measured from DRA photoreceptors. The reconstructions also support a larger-scale opponent process mediated by multicolumnar Dm9 cells, which also formed some synapses outside the medulla neuropil (Kind, 2021).

Other cell types also received inner photoreceptor input outside the medulla, notably the lamina monopolar cells L1 and L3. These lamina connections indicate that chromatic comparisons arising from R7 and R8 may feed into the motion vision pathway, and identifies a new site for interplay between the 'color' and 'motion' pathways. Together, these observations suggest that synapses in an unexpected location, outside the main synaptic layers of the medulla, could play a significant role in early visual processing (Kind, 2021).

In the non-DRA medulla, this study found, for the first time in an EM study, strong synaptic connections between R7s and MeTu cells that project to the AOTU, confirming previous claims based on light microscopy. This finding may reconcile disparate observations, such as a role in wavelength-specific phototaxis for cells matching MeTu morphology, as well as measurements of color-sensitive signals in the AOTU of bees. Previous anatomical studies partitioned MeTu cells into distinct subclasses that terminate in discrete subdomains of the AOTU. This study identified modality-specific MeTu-DRA cells that only integrate from the polarization-sensitive R7-DRA photoreceptors, while avoiding synaptic contacts with color-sensitive pale or yellow R7s. MeTu and MeTu-DRA cells target adjacent subdomains within the small unit of the AOTU, in agreement with proposals that parallel channels convey different forms of visual information from the eye to the central complex via the AOTU (Kind, 2021).

This study also identified connections from inner photoreceptors to several cell types either not previously described or not known to be photoreceptor targets, thus setting up a clear expectation that these cells should contribute to color or polarization vision. ML1 is a new, major target of R8, a cell type that connects the medulla to the lobula via a previously unknown, non-columnar pathway and the central brain. Previous studies have identified an important role for Tm5a/b/c and Tm20 cell types for chromatic processing in the lobula, and this study has confirmed that these cell types are targets of R7-8. Whether these Tm neurons and ML1 cells have common targets in the lobula, feed into shared central pathways or contribute to separate channels remain open questions; the lobula arbors of the Tm and ML1 cells are mostly in different layers arguing against direct synaptic interactions between the cells (Kind, 2021).

The reconstruction of DRA photoreceptor targets confirmed the modality-specific connectivity of Dm-DRA cell types within layer M6: Dm-DRA1 to R7-DRA, and Dm-DRA2 to R8-DRA. The R7-DRA and R8-DRA cells respond to orthogonal orientations of polarized light at each location of the DRA, so Dm-DRA1 and Dm-DRA2 likely process orthogonal e-vector orientations spatially averaged by pooling over ~10 ommatidia. The data also revealed additional DRA pathways into the PLP in the central brain via VPN-DRA cells, as well as to the contralateral DRA, via MeMe-DRA cells. Such interhemispheric connections have been demonstrated in larger insects, but not in Drosophila, and their synaptic input was not known. Interocular transfer contributes to navigation by desert ants that can see the celestial polarization but not visual landmarks, but the interactions between the DRA regions remain poorly understood. The identification of MeMe-DRA neurons may enable the mechanisms of such phenomena to be explored (Kind, 2021).

This study has additionally identified as pale-specific the aMe12 and ML-VPN1 cell types, and the yellow bias of Tm5c and the pale bias of Mi9. Detailed analysis of Dm8 inputs confirmed that these neurons receive most of their photoreceptor input in a central home column, consistent with pale and yellow subtypes of Dm8 cells having distinct chromatic properties. The selective photoreceptor input to these cell types, combined with input to columnar cells from a single photoreceptor subtype, indicates that wavelength-specific information is maintained in the medulla and lobula. The projections of aMe12 and ML-VPN1 to the central brain indicate the possibility that wavelength-specific photoreceptor responses are directly conveyed into the central brain by these cells, although they likely integrate input from other chromatically sensitive cell types (Kind, 2021).

By focusing on a small number of columns, this study has delivered a near-complete picture of local connectivity, but the possibility cannot be ruled out that certain cell types may have been overlooked. For instance, an arbitrary threshold was chosen of two to three synapses below which no attempts were made to reconstruct synaptic targets to the extent required to uniquely identify them. As a result this study may, in principle, have missed large cells that receive small, but significant inputs across many columns. Furthermore, regional specializations in the optic lobes, such that specific cell types might be found outside of the seed columns, cannot be ruled out. For example, some MeTu cells are only found in the dorsal third of the medulla, where incoming R7 cells are known to co-express Rh3 and Rh4 rhodopsins (Kind, 2021).

Taken together, the data presented in this study provides access to the full complement of R7 and R8 photoreceptor targets from functionally specialized optical units. By reconstructing these local circuits within a full-brain EM volume, it was possible to establish the complete morphology of large, multicolumnar cell types, that are strongly connected to photoreceptors, but had eluded previous connectomic reconstruction efforts. The sparse tracing approach that was implemented and described in this study enabled efficient identification of the complete set of upstream and downstream partners of the inner photoreceptors, in a manner that is complementary to the dense connectomes generated from smaller-scale medulla volumes. As an example of this synergistic usage of complementary data sets, this study has returned to the 7-column data and used whole-cell morphologies to match the bodies of previously unidentified photoreceptor targets. In that process strong candidates were established for MeTu, ML1, and perhaps for aMe12 and several aspects of their connectivity in the Zheng (2018) data set were confirmed. In so doing, it is now possible to have access to the additional connectivity data provided by the 7-column dense reconstruction. While full exploration and follow-up analyses of these combined data is beyond the scope of this work, the combined analysis of the Zheng (2018), and the medulla-7-column connectome revealed several intriguing connectivity patterns, including new candidate paths for the integration of output from different photoreceptor types. For example, the MeTu cells that are postsynaptic to R7 also receive indirect R8 input via the R8 target Mi15, and ML1 combines direct R8 input with indirect input from outer photoreceptors via lamina neurons. As further connectome data sets are completed, this comparative interplay between data sets with unique advantages and limitations will be an important step in both cross-validating and extending the applicability of all related data sets (Kind, 2021).

Reconstruction of the DRA photoreceptor targets has provided the first EM-based connectomic data set for modality-specific cell types likely to process skylight information in any insect and will be important for developing refined models of skylight navigation. Core motifs shared between DRA and central columns are prime candidates for circuit elements that perform computations, such as establishing opponency, that are key for both polarization and color processing, whereas cell types with preferential connections to either pale or yellow columns are promising candidates for the study of specific aspects of color processing in the insect brain. This comprehensive catalog of the neurons carrying signals from R7 and R8 photoreceptors deeper into the brain establishes a broad foundation for further studies into the mechanistic basis of color vision and its contributions to perception and behavior (Kind, 2021).

Connectomics has become a standard neuroscience methodology in a few model animals, with the visual system being a popular target of study. Combining connectomics with circuit and behavioral physiology, recent studies on the color vision of the fruit fly Drosophila melanogaster have focused on the mechanisms underlying early wavelength processing in the optic ganglia. However, the color vision capabilities of D. melanogaster are limited, compared with many flower-visiting insects. For example, a butterfly Papilio xuthus has six spectral classes of photoreceptors. Each ommatidium contains nine photoreceptors in one of three fixed combinations, making the eye an array of three spectrally distinct ommatidia types. Behaviorally, P. xuthus can detect 1 nm differences in light wavelength across the spectrum from ultraviolet to red, outperforming humans. What is the neuronal basis of such precise color vision? How does such a system evolve? Addressing these questions requires comparative studies at the circuit level. This study performed a connectome analysis in the first optic ganglion, the lamina, of P. xuthus. The lamina comprises cartridges, each typically containing nine photoreceptor axons from a single ommatidium and four second-order neurons. Abundant inter-photoreceptor connections were found that are absent in the lamina of D. melanogaster. Connections were identified between neighboring cartridges, particularly those receiving inputs from spectrally distinct ommatidia. The linear summation of synaptic connections well explains the spectral sensitivity of photoreceptors and second-order neurons in the lamina (Matsushita, 2022).

Sensory systems rely on neuromodulators, such as serotonin, to provide flexibility for information processing as stimuli vary, such as light intensity throughout the day. Serotonergic neurons broadly innervate the optic ganglia of Drosophila. This study mapped of patterns of serotonin receptors in the visual system, focusing on a subset of cells with processes in the first optic ganglion, the lamina. Serotonin receptor expression was found in several types of columnar cells in the lamina including 5-HT2B in lamina monopolar cell L2, required for spatiotemporal luminance contrast, and both 5-HT1A and 5-HT1B in T1 cells, whose function is unknown. Subcellular mapping with GFP-tagged 5-HT2B and 5-HT1A constructs indicated that these receptors localize to layer M2 of the medulla, proximal to serotonergic boutons, suggesting that the medulla neuropil is the primary site of serotonergic regulation for these neurons. Exogenous serotonin increased basal intracellular calcium in L2 terminals in layer M2 and modestly decreased the duration of visually induced calcium transients in L2 neurons following repeated dark flashes, but otherwise did not alter the calcium transients. Flies without functional 5-HT2B failed to show an increase in basal calcium in response to serotonin. 5-HT2B mutants also failed to show a change in amplitude in their response to repeated light flashes but other calcium transient parameters were relatively unaffected. While serotonin receptor expression in L1 neurons was not detected, they, like L2, underwent serotonin-induced changes in basal calcium, presumably via interactions with other cells. These data demonstrate that serotonin modulates the physiology of interneurons involved in early visual processing in Drosophila (Sampson, 2020).

Appropriate termination of the photoresponse in image-forming photoreceptors and downstream neurons is critical for an animal to achieve high temporal resolution. Although the cellular and molecular mechanisms of termination in image-forming photoreceptors have been extensively studied in Drosophila, the underlying mechanism of termination in their downstream large monopolar cells remains less explored. This study shows that synaptic ACh signaling, from both amacrine cells (ACs) and L4 neurons, facilitates the rapid repolarization of L1 and L2 neurons. Intracellular recordings in female flies show that blocking synaptic ACh output from either ACs or L4 neurons leads to slow repolarization of L1 and L2 neurons. Genetic and electrophysiological studies in both male and female flies determine that L2 neurons express ACh receptors and directly receive ACh signaling. Moreover, the results demonstrate that synaptic ACh signaling from both ACs and L4 neurons simultaneously facilitates ERG termination. Finally, visual behavior studies in both male and female flies show that synaptic ACh signaling, from either ACs or L4 neurons to L2 neurons, is essential for the optomotor response of the flies in high-frequency light stimulation. This study identifies parallel synaptic ACh signaling for repolarization of L1 and L2 neurons and demonstrates that synaptic ACh signaling facilitates L1 and L2 neuron repolarization to maintain the optomotor response of the fly on high-frequency light stimulation (Wu, 2021).

A fascinating question in neuroscience is how ensembles of neurons, originating from different locations, extend to the proper place and by the right time to create precise circuits. This study investigated this question in the Drosophila visual system, where photoreceptors re-sort in the lamina to form the crystalline-like neural superposition circuit. The repeated nature of this circuit allowed establishment of a data-driven, standardized coordinate system for quantitative comparison of sparsely perturbed growth cones within and across specimens. Using this common frame of reference, the extension was investigated of the R3 and R4 photoreceptors, which is the only pair of symmetrically arranged photoreceptors with asymmetric target choices. Specifically, it was found that extension speeds of the R3 and R4 growth cones are inherent to their cell identities. The ability to parameterize local regularity in tissue organization facilitated the characterization of ensemble cellular behaviors and dissection of mechanisms governing neural circuit formation (Ji, 2021).

Electron microscopy-based connectomes provide important insights into how visual circuitry of fruit fly Drosophila computes various visual features, guiding and complementing behavioral and physiological studies. However, connectomic analyses of the lobula, a neuropil putatively dedicated to detecting object-like features, remains underdeveloped, largely because of incomplete data on the inputs to the brain region. This study attempted to map the columnar inputs into the Drosophila lobula neuropil by performing connectivity- and morphology-based clustering on a densely reconstructed connectome dataset. While the dataset mostly lacked visual neuropils other than lobula, which would normally help identify inputs to lobula, clustering analysis successfully extracted clusters of cells with homogeneous connectivity and morphology, likely representing genuine cell types. It was possible to draw a correspondence between the resulting clusters and previously identified cell types, revealing previously undocumented connectivity between lobula input and output neurons. While future, more complete connectomic reconstructions are necessary to verify the results presented in this study, they can serve as a useful basis for formulating hypotheses on mechanisms of visual feature detection in lobula (Tanaka, 2022).

The accurate processing of contrast is the basis for all visually guided behaviors. Visual scenes with rapidly changing illumination challenge contrast computation because photoreceptor adaptation is not fast enough to compensate for such changes. Yet, human perception of contrast is stable even when the visual environment is quickly changing, suggesting rapid post receptor luminance gain control. Similarly, in the fruit fly Drosophila, such gain control leads to luminance invariant behavior for moving OFF stimuli. This study shows that behavioral responses to moving ON stimuli also utilize a luminance gain, and that ON-motion guided behavior depends on inputs from three first-order interneurons L1, L2, and L3. Each of these neurons encodes contrast and luminance differently and distributes information asymmetrically across both ON and OFF contrast-selective pathways. Behavioral responses to both ON and OFF stimuli rely on a luminance-based correction provided by L1 and L3, wherein L1 supports contrast computation linearly, and L3 non-linearly amplifies dim stimuli. Therefore, L1, L2, and L3 are not specific inputs to ON and OFF pathways but the lamina serves as a separate processing layer that distributes distinct luminance and contrast information across ON and OFF pathways to support behavior in varying conditions (Ketkar, 2022).

The present study establishes that contrast and luminance are basic visual features that interact with both ON and OFF pathways. In both pathways, the interaction between these features enables stable visual behaviors across changing conditions. The lamina neurons L1, L2, and L3 act as the circuit elements segregating both contrast and luminance information. Behavioral experiments show that luminance-sensitive input neurons scale behavioral responses to contrast in both ON and OFF pathways. While L1 and L2 provide distinct contrast inputs, L1 also encodes luminance, together with L3. Whereas L3 activity non-linearly increases with decreasing luminance, L1 shows a linear relationship with luminance. Input from both luminance-sensitive neurons is differently used in ON and OFF pathways. Thus, L1, L2, and L3 are not ON or OFF pathways specific inputs, but they instead distribute the two most basic visual features, contrast and luminance, across pathways to enable behaviorally relevant computations (Ketkar, 2022).

Changing visual environments impose a common challenge onto the encoding of both ON and OFF contrasts, namely the contrasts are underestimated in sudden dim light. This work shows that visual behaviors guided by both ON and OFF pathways approach luminance invariance and are not susceptible to underestimation of contrast in sudden dim conditions. Similarly, luminance invariance has been shown in human perception of both ON and OFF contrasts, and in neural responses in cat LGN at fast time scales. This argues that the implementation of a rapid luminance gain is a common feature of all visual systems, which is relevant for any species that relies on visual information for its survival in changing visual environments. In Drosophila, luminance information from both L1 and L3 are required for rapid luminance gain control, but the impact of the two neurons on behavior is pathway dependent. In the OFF pathway, losing either L1 or L3 function leads to a strong deviation from luminance invariance, such that the dim light stimuli are underestimated. On the contrary, ON motion-driven behavior only underestimates dim stimuli if both L1 and L3 neuron types are not functional. Furthermore, L2 neurons, which were formerly thought to be OFF-pathway inputs, contribute contrast-sensitive information to ON behavior. Notably, ON and OFF contrast constancy is not achieved symmetrically at every processing stage. For example, in the vertebrate retina, ON RGCs encode a mixture of luminance-invariant and absolute (i.e. luminance-dependent) contrast, whereas OFF RGCs encode predominantly absolute contrast. Thus, asymmetrical implementation of contrast-corrective mechanisms can be common across visual systems, too (Ketkar, 2022).

Input from the three lamina neurons is differentially utilized across ON and OFF pathways. How does this fit with the established notion that L1 is an input to the ON and L2 and L3 are inputs to OFF pathways? The luminance-varying stimuli sets used in this study were able to pull out lamina neuron contributions that were not obvious with simpler stimuli. For example, the data show that L1 and L2 provide redundant contrast input to the ON pathway at 100% contrast and varying luminance. However, L1 is still strictly required for ON responses if different contrasts are mixed. This is consistent with a more complex ON-pathway input architecture and hints at a role for the L1 pathway in contrast adaptation. Interestingly, Mi1, an important post-synaptic partner of L1, shows an almost instantaneous and strong contrast adaptation (Ketkar, 2022).

While all three lamina neuron types hyperpolarize to light onset and depolarize to light offset, contrast selectivity emerges downstream of these neurons: post-synaptic partners of L1 acquire ON contrast selectivity due to inhibitory glutamatergic synapses, whereas cholinergic L2 and L3 synapses retain OFF contrast selectivity. L3 had furthermore mostly been considered an OFF-pathway neuron because the OFF-pathway neuron Tm9 receives its strongest input from L3. However, L3 itself actually makes most synaptic connections with the Mi9 neuron that plays a role in guiding behavioral responses to ON stimuli. Further synapses of L3 with the ON-selective Mi1 neuron are similar in number to those with Tm9. Finally, L3 can potentially also convey information to the chromatic pathway, as Tm20 is its second strongest postsynaptic connection. There, L3 luminance sensitivity might play a relevant role in achieving color constancy, that is color recognition irrespective of illumination conditions. Altogether, anatomical and functional data indicate that it is time to redefine L3 as part of a luminance-encoding system rather than a mere OFF-pathway input. Other synaptic connections that link L2 to downstream ON-selective neurons still have to be investigated in detail (Ketkar, 2022).

A role of L1 beyond the ON pathway is supported by functional connectivity studies showing that Tm9 properties rely in part on L1 input, and that Tm9 together with other OFF-pathway interneurons displays contrast-opponent receptive fields, showing the presence of ON information in the OFF pathway. Connectomics data did not identify any known OFF-pathway neurons postsynaptic to L1, but among the strongest postsynaptic partners of L1 are the GABAergic interneurons C2 and C3 that connect to the OFF pathway. Intercolumnar neurons downstream of L1, such as Dm neurons, could further carry information to OFF-selective neurons, likely through disinhibition from ON-selective inputs. In the vertebrate retina, intercolumnar amacrine cells mediate interaction between ON and OFF bipolar cells, which has been shown to extend the operating range of the OFF pathway (Ketkar, 2022).

Altogether, it now becomes evident that a split in ON and OFF circuitry only truly exists in downstream medulla neurons and direction-selective cells. The luminance and contrast features encoded differently in L1, L2 and L3 lamina neurons are shared by both pathways. Importantly, the distinct features that are passed on by the specific inputs downstream of photoreceptors guide distinct behavioral roles (Ketkar, 2022).

Despite being postsynaptic to the same photoreceptor input, L1, L2, and L3 all show different contrast and luminance sensitivities. L1 was previously considered the ON-pathway sibling of the contrast-sensitive L2, both with regard to its temporal filtering properties and at the transcriptome level. However, L1 calcium signals show a transient and a sustained response component, which are contrast- and luminance-sensitive, respectively. Compared to photoreceptors, which also carry both contrast and luminance components, L1 still amplifies the contrast signals received from the photoreceptors, since its transient component is more pronounced than the one seen in the photoreceptor calcium traces. In other insect species, different types of lamina neurons have also been distinguished based on their physiological properties, although their specific luminance and contrast sensitivities are yet unknown (Ketkar, 2022).

The two luminance-sensitive neurons L1 and L3 differ in their luminance-encoding properties. L1's initial transient contrast response might reduce the operating range of the subsequent luminance-sensitive baseline. L3's calcium responses show little adaptation and can utilize most of its operating range to encode luminance. L3 seems to invest this wider operating range into amplifying the darkest luminance values selectively and non-linearly. Thus, a predominantly luminance-sensitive channel among LMCs may have evolved to selectively process stimuli in the low luminance range. The different linear and non-linear properties of L1 and L3 might further increase the dynamic range of luminance signaling. Together with the pure contrast sensitivity of L2, the first-order interneurons in flies exhibit a wide range of sensitivities with respect to contrast and luminance, and different functional relevance. Diversifying feature encoding through distinct temporal properties of first-order interneurons is a strategy employed to reliably handle wide luminance ranges (Ketkar, 2022).

In flies, three first-order interneurons feed contrast and luminance information into downstream circuitry. In the mouse retina, more than 30 functionally distinct bipolar types show a spectrum of temporal filter properties rather than a strict transient-sustained dichotomy, thus capturing a larger diversity of temporal information in parallel channels. Many bipolar cell types resemble L1, in that they have both luminance and contrast signals in distinct response components. However, the degree of transiency varies from cell type to cell type, and some predominantly sustained bipolar cell types are also found, closely resembling the luminance-sensitive L3. Such diversification of feature extraction at the periphery has been shown to be computationally advantageous, especially when processing complex natural scenes. For example, during daylight, visual scenes can differ in intensity by 4-5 log units, whereas electrical signals in cone photoreceptors reach a dynamic range of only two orders of magnitude (Ketkar, 2022).

Although the vertebrate retina apparently has a much larger diversity of cell types to handle the wide and complex statistics of the visual environments, there is only a single layer of processing between photoreceptors and the first direction-selective cells, whereas in insects, there are two: the lamina and the medulla. It seems as if the combined properties of bipolar cells are spread across these two processing stages in the fly visual system: whereas some properties, such as diversity of temporal filtering starts in LMCs, contrast selectivity only emerges in medulla neurons and not directly in the first-order interneurons as it happens in bipolar cells. In both vertebrates and invertebrates, the emergence of ON selectivity occurs through inhibitory glutamatergic synapses, but whereas this happens at the photoreceptor-to-bipolar cell synapse in vertebrates, it happens one synapse further down between lamina and medulla neurons in flies. Taken together, LMCs and downstream medulla neurons combined appear to be the functional equivalents of vertebrate bipolar cell layers. Given the size limitations of the fly visual system to encode the same complex environment effectively, one benefit of this configuration with an extra layer could be that it allows more combinations. Furthermore, the photoreceptor-to-lamina synapse in the fly superposition eye already serves to spatially pool information from different photoreceptors. In both visual systems, diversifying distinct information across several neurons could serve as a strategy to reliably respond to contrast when luminance conditions vary (Ketkar, 2022).

Activity-dependent synaptic plasticity is crucial for responses to the environment. Although the plasticity mechanisms of presynaptic photoreceptor neurons in the Drosophila visual system have been well studied, postsynaptic modifications remain elusive. In addition, further studies on the adaption of the visual system to different light experiences at a circuitry scale are required. Using the modified transcriptional reporter of intracellular Ca(2+) method, this study describes a way to visualize circuitry changes according to different light experiences. Enhanced postsynaptic neuronal activity responses were found in lamina monopolar neuron L2 after prolonged light treatment. Although L1 also has connections with photoreceptors, there were no enhanced activity responses in L1. This study also reports that activity-dependent transcriptional downregulation of inhibitory histamine receptor (HR), Ort, occurs in postsynaptic neuron L2, but not in L1, during continuous light conditions. This study produced exogenous Ort proteins in L2 neurons and found that it attenuated the enhanced activity response caused by constant light exposure. These findings, together with the fact that histamine is the main inhibitory neurotransmitter released by photoreceptors in the Drosophila visual system, confirmed the hypothesis that the activity-dependent transcriptional downregulation of HR is responsible for the constant light exposure-induced circuitry response changes in L2. The results successfully demonstrated the selective circuit change after synaptic remodeling evoked by long-term activation and provided in vivo evidence of circuitry plasticity upon long-term environmental stimulation (Bai, 2022).

The ability of animals to adapt to their environment by modifying the structure, connections, or molecular process in their brains is known as 'plasticity.' Plasticity allows animals to develop mature brains, recover from injury, and respond to various stimuli and experiences in the environment. Activity-dependent synaptic plasticity, a component of activity-dependent neuroplasticity in the nervous system, is believed to be crucial for responses to the environment and adaptive behaviors. Activity-dependent synaptic plasticity is adopted through the adjustment of the connection strength or information flow according to environmental stimulation (Bai, 2022).

Many studies have been conducted to investigate the activity-dependent synaptic plasticity that occurs between one neuron and another. For example, in the mammalian central nervous system, activity-dependent synaptic plasticity regarding long-term potentiation (LTP) or long-term depression (LTD) has been well studied. Numerous pre- and postsynaptic mechanisms of plasticity have been studied at excitatory chemical synapses in the rodent hippocampus, including reorganization of synaptic components, regulation of neurotransmitter release, and postsynaptic regulation of receptors. However, further study is required to understand how synaptic plasticity affects circuitry changes. When a circuit receives an input activity, direct synaptic changes that have subsequent consequences in the circuit occur. Because one type of presynaptic neuron usually has connections with various types of postsynaptic neurons, it remains a big question whether synaptic plasticity happens among all connections or whether only some specific subsets of postsynaptic neurons experience changes in the connections with the presynaptic neuron and modify their activity responses according to the input activity (Bai, 2022).

The current research aimed to visualize the circuitry changes after synaptic modification evoked by long-term activation and to find in vivo evidence of circuitry plasticity after long-term environment stimulation. To accomplish this, focus was placed on the visual system of the model animal Drosophila melanogaster. The Drosophila visual system, either in the developing or adult stage, is a powerful model to study the activity-dependent synaptic plasticity evoked by external stimulation from the environment. Many sophisticated genetic tools and experimental methods have already been developed and applied in the Drosophila visual system. Moreover, the small-sized brains, well studied genome and anatomical structures, and advanced imaging techniques make the Drosophila visual system an advantageous model for elucidating neural circuit changes (Bai, 2022).

The adult Drosophila visual system comprises the retina and optic lobe. The optic lobe consists of the lamina, medulla, lobula, and lobula plate. The retina consists of approximately 750 ommatidia (small eyes), and each ommatidium has eight types of photoreceptor neuron (R1 to R8). R1 to R6 are responsible for light sensing and motion detection, R7 expresses UV-sensitive opsins, and R8 expresses blue- and green-sensitive opsins. R8 is also believed to be involved in the color-sensing function. The R1-6 photoreceptors project to the lamina, and the R7 and R8 photoreceptors extend to the M6 and M3 layers of the medulla, respectively. Photoreceptors in Drosophila mainly release the inhibitory neurotransmitter histamine to their postsynaptic neurons. In the mammalian brain, histamine receptors (HRs) are mostly G protein-coupled receptors. In Drosophila, however, HRs in neurons postsynaptic to photoreceptors are histamine-gated chloride channels. Receiving histamine can cause hyperpolarization in the neuron, thereby decreasing activity (Bai, 2022).

Previous research has shown that in the adult Drosophila visual system, under continuous light conditions (LL), photoreceptor neurons, especially R8, reorganize synaptic components at the active zone (AZ) through microtubule destabilization. The activities of both pre- and postsynaptic neurons are involved in synaptic component reorganization. This type of synaptic component reorganization can be reversed when flies are returned to continuous dark conditions (DD) or normal light-dark conditions (LD) for 72 h. However, the subsequent influence of long-term light stimulation on the circuit remains poorly understood. Moreover, although the presynaptic process during synaptic plasticity has been intensely studied, significantly less is known regarding the postsynaptic process in the adult Drosophila visual system (Bai, 2022).

This research applied a transcriptional reporter of intracellular calcium (TRIC) to the neurons postsynaptic to photoreceptors to monitor circuitry response and found selective response changes to subsequent environmental stimulation after constant light exposure. Surprisingly, only a subset of postsynaptic neurons (such as lamina monopolar neuron L2) tended to show a drastic activity response to 1-day light stimulation after constant light exposure. Although lamina monopolar neuron L1 also has connections with photoreceptors, it showed no enhanced activity response (Bai, 2022).

Regarding the underlying mechanism of the selective response changes in the circuit, it was hypothesized that the expression levels of HRs in postsynaptic neurons may change because histamine is the main inhibitory neurotransmitter released by photoreceptors in the Drosophila visual system. A number of genetic experiments were conducted and it was found that in the adult Drosophila visual system, after constant light exposure, transcriptional downregulation of HRs occurs in postsynaptic lamina neuron L2. The transcriptional downregulation of HRs depends on the photoreceptor and postsynaptic neuronal activity, and the process involves CaMKII and CREB-B. The research elucidated the activity-dependent synaptic plasticity in the postsynaptic terminal during constant light exposure in the Drosophila visual system. The transcriptional downregulation of HRs results in an enhanced postsynaptic L2 neuronal response to subsequent light stimulation. Because HR downregulation happens in L2 but not in L1 during constant light exposure, the circuit shows selective activity response changes. The results successfully demonstrated the circuit change after synaptic remodeling evoked by long-term activation and revealed in vivo evidence of the circuitry plasticity upon long-term environmental stimulation. The findings in this research deepen the understanding of the consequences of activity-dependent synaptic plasticity to the circuit and provide new insights into the activity-dependent synaptic plasticity mechanism in the neural circuit (Bai, 2022).

This study applied the Transcriptional Reporter of Intracellular Ca2+ (TRIC) method to express GFP in neurons postsynaptic to photoreceptors when there was calcium influx to achieve better understanding of the subsequent nfluence of activity-dependent, environmental stimulation-induced synaptic plasticity on the circuit in the Drosophila visual system. The TRIC validation of the physiological response showed that only a subset of postsynaptic neurons, L2 neurons, exhibited a robust activity response to environmental stimulation after chronic light treatment. L1 neurons also have connections with photoreceptors, but their activity responses did not change. This research succeeded in visualizing the circuit change in a selective circuitry after activity-dependent synaptic plasticity evoked by long-term activation. It has been reported previously that in photoreceptors, ambient light exposure induces the dominant arrestin isoform, Arr2, which is translocated to the rhabdomere loaded with R1-6 and the inactivated photoreceptor response. Moreover, recent studies have indicated that continuous light conditions trigger photoreceptor neuronal component reorganization in the AZ through microtubule destabilization and reduction in the number of synapses. Such processes allow the Drosophila visual system to adapt to prolonged light exposure. Additional to these findings, it is proposed that the enhanced L2 neuronal activity response is also part of the adaptation to prolonged light exposure (Bai, 2022).

Activity response changes at synapses can be achieved through postsynaptic modifications, such as regulation of neurotransmitter receptor availability at the postsynaptic terminal. Previous studies have shown that the postsynaptic modification during activity-dependent synaptic plasticity probably occurs mainly through the regulation of postsynaptic receptor localization and recycling. For example, in the rodent hippocampus, postsynaptic regulation of AMPA glutamate receptors (AMPARs), which involves the trafficking and recycling of receptors, was found to be crucial for plasticity. The exocytosis and endocytosis of AMPARs were found to account for the AMPAR level at the synapse due to LTP or LTD. However, cases of transcriptional postsynaptic receptor regulation remain relatively rare. Moreover, although some previous studies have demonstrated activity-dependent regulation of glutamate receptor localization during the development of Drosophila neuromuscular junction, there are few studies that investigate postsynaptic modification in adult Drosophila. This study aimed to discover what happens in the postsynaptic neurons of Drosophila photoreceptors during prolonged light exposure and successfully observed a reduction in both HR protein and mRNA levels in the medulla arbors of the L2 neurons under LL conditions. It was demonstrated that the HR downregulation in adult Drosophila postsynaptic L2 neurons is activity-dependent and occurs on the transcriptional level (Bai, 2022).

Like the postsynaptic modifications in the rodent hippocampus, HR transcriptional downregulation is regulated by CaMKII and CREB-B. These findings supplement the understanding of activity-dependent synaptic plasticity that occurs in the postsynaptic terminal during long-term environmental stimulation. Activity-dependent transcriptional downregulation of histamine receptors is responsible for the selective response changes evoked by long-term activation. The results of the ninaE mutation experiment showed that R1-6 activities were necessary for HR level regulation in L2 neurons. R1-6 photoreceptors maintain synaptic connections with L2 neurons in the lamina, and L2 neurons mainly receive light information input from R1-6 (Bai, 2022).

Therefore, R1-6 photoreceptor activity appears to account for significant HR downregulation in the L2 neurons. Meanwhile, synaptic connections were found between R8 and L2 in the medulla, and activating R8 was sufficient to downregulate L2 HRs, suggesting that the L2 HR level is regulated by multiple factors. R1-6- and R8-related regulatory mechanisms are activity-dependent. In this study, it was also interesting that HR downregulation occurred only in L2 after 6-day LL, but not in L1, which is consistent with the observation that only L2 neurons showed increased activity responses to photoreceptor activation after prolonged light exposure. Exogenous expression of Ort in L2 neurons attenuates the enhanced activity response caused by constant light exposure. These findings, together with the fact that histamine is the main inhibitory neurotransmitter released by photoreceptors in the Drosophila visual system, affirm the hypothesis that the activity-dependent transcriptional downregulation of HRs is responsible for the constant light exposure-induced circuitry response change. After the visual circuit of Drosophila receives long-term activation, synaptic plasticity involving activity-dependent transcriptional downregulation of HRs occurs specifically in L2 neurons and then changes the activity responses of L2 upon subsequent photoreceptor activation. This implies the special role of L2 neurons in integrating light sensing, motion detection, and color vision, consistent with the central role of L2 as secondary neurons in the medullar visual circuit (Bai, 2022).

After the visual circuit of Drosophila receives long-term activation (6-day LL treatment), synaptic plasticity involving activity-dependent transcriptional downregulation of HRs occurs specifically in L2 neurons, but not L1, and then enhanced the activity responses of L2 upon subsequent photoreceptor activation on Day 7. This activity-dependent selective circuitry response change implies the process of integrating light-sensing (R1-6), motion detection (L2), and color vision (R8) when flies are faced with prolonged light exposure (Bai, 2022).

The results of this study showed that HR protein decrease in the postsynaptic L2 neurons started no earlier than 3 days after eclosion, which was consistent with the fact that the 3-day LL condition was not sufficient to cause an increased L2 activity response to photoreceptor activation. The reason that HR decrease and the postsynaptic circuitry response change only started after 3 days remains to be explained (Bai, 2022).

The mechanism underlying the baseline activity of L2 is also unclear. It is broadly believed that the main input that L2 neurons directly receive from photoreceptors is inhibitory because photoreceptors mainly release histamine. However, there is a baseline calcium influx in L2, which seems to be contradictory. In a recently published study, R8 photoreceptors reportedly released not only inhibitory histamine but also excitatory acetylcholine to the postsynaptic neurons and indirectly activated the L2 neurons. This finding implies that upon light stimulation, L2 is hyperpolarized at first but returns to a slightly depolarized state as the baseline activity after a certain period. This may explain, at least in part, why there is a slight calcium influx in L2 under continuous light conditions even if the main input that L2 neurons directly receive from photoreceptors is inhibitory. Therefore, there may be a threshold of activity for L2, and only after 3 days of LL treatment can calcium influx surpass the threshold and trigger any further changes in the circuit. A previous report also demonstrated that the synapse number in photoreceptors was significantly reduced after constant 3-day light exposure after eclosion in an activity-dependent manner. This finding strongly supports the requirement of a 3-day LL condition for further modification. Upon long-term light exposure, the increased calcium influx in L2 triggers HR downregulation. The reduced HR level in the constitutively active L2 neurons attenuates the inhibitory input directly from the photoreceptors and further enhances calcium influx in L2. Therefore, L2 neurons with fewer HRs can respond more drastically to the photoreceptor stimulation after LL treatment (Bai, 2022).

Meanwhile, there is also another possibility that the HR transcriptional downregulation begins at an early stage after eclosion, but since the transcript is stable, the level of transcription does not start to go down until day 3. Thus, it is difficult to judge which happens earlier, synapse decrease or HR transcriptional downregulation, which requires further studies in the future (Bai, 2022).

This paper has reported that in the adult Drosophila visual system, after constant light exposure-induced synaptic plasticity, inhibitory neurotransmitter histamine receptor transcriptional downregulation occurs in postsynaptic lamina neuron L2, but not in L1. This downregulation depends on the photoreceptor and postsynaptic neuronal activity and involves CaMKII and CREB-B. The histamine receptor transcriptional downregulation results in a more intense postsynaptic L2 neuronal response to subsequent environmental stimulation, especially in the medulla. The results successfully demonstrated the circuit change after synaptic remodeling evoked by long-term activation and demonstrated in vivo evidence of the circuitry plasticity upon long-term environmental stimulation. The findings of this research deepen the understanding of the consequences of activity-dependent synaptic plasticity to the circuit and provide new insights into the activity-dependent synaptic plasticity mechanism in the neural circuit (Bai, 2022).

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. This study reports that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such 'anti-directional turning' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses (Mano, 2023).

Color and motion are used by many species to identify salient objects. They are processed largely independently, but color contributes to motion processing in humans, for example, enabling moving colored objects to be detected when their luminance matches the background. This study demonstrates an unexpected, additional contribution of color to motion vision in Drosophila. Behavioral ON-motion responses are more sensitive to UV than for OFF-motion, and cellular pathways were identified connecting UV-sensitive R7 photoreceptors to ON and OFF-motion-sensitive T4 and T5 cells, using neurogenetics and calcium imaging. Remarkably, this contribution of color circuitry to motion vision enhances the detection of approaching UV discs, but not green discs with the same chromatic contrast, and it was shown how this could generalize for systems with ON- and OFF-motion pathways. The results provide a computational and circuit basis for how color enhances motion vision to favor the detection of saliently colored objects (Longden, 2023).

Color and motion are two visual cues used by many species to identify salient moving objects. They are processed largely independently, for example along the ventral and dorsal pathways, respectively, in primates. However, color contributes to motion perception in humans and other primates, as indicated by psychophysical experiments using chromatic stimuli lacking luminance contrast allowing the edge motion of objects to be detected even when the background illumination changes to match the luminance of the object. By identifying cellular pathways for color contributing to motion processing in Drosophila, this study demonstrates how color can additionally boost the motion detection of specifically colored objects, without, surprisingly, improving the motion detection of differently colored objects with the same chromatic contrast (Longden, 2023).

Like primates, color and motion are initially largely processed along separate pathways in Drosophila, and their motion vision was not thought to be influenced by color vision based on studies using blue and green wavelengths. However, connectomic studies found that the photoreceptors used for color vision are also connected to cells in the motion pathway, and a study using sophisticated genetic manipulations indicated that color inputs expand the spectral range of motion vision through unknown cellular mechanisms. Drosophila forages on fruits, flowers, and water droplets that can all have a UV reflectance distinct from background foliage, and UV illumination is used commercially to identify ripe or damaged citrus fruit17. Therefore how color might contribute to object motion vision was explored using the behavioral and cellular responses of Drosophila to objects defined by UV-green color edges (Longden, 2023).

Drosophila has a visual system well suited to parsing UV and green components of the visual scene. Under every eye facet (ommatidium) are eight photoreceptor). The outer six, R1-6, are sensitive to a broad range of wavelengths, especially UV and green. They provide a luminance signal for pathways processing motion vision and also contribute to color vision. The inner two, R7 and R8, provide additional wavelength sensitivity for color vision, and also contribute to luminance processing. The R7 and R8 neurons are paired and come in two flavors, determined by the rhodopsins expressed, across most of the eye: one sensitive to short wavelength UV and blue respectively (in so-called pale ommatidia), and the other to long wavelength UV and green (in so-called yellow ommatidia). R1-6 and R7-8 project to different visual neuropils, the lamina and medulla, respectively, and like vertebrates, flies have visual processing pathways for the movement of bright edges relative to the background (ON-motion), and dark edges relative to the background (OFF-motion). The T4 and T5 cell types are directionally selective for ON- and OFF-motion, respectively2, and the circuitry connecting the photoreceptors to T4 and T5 neurons has been described in considerable detail (Longden, 2023).

To test if color contributes to motion vision in flies, this study developed a custom projector to display spatially precise UV and green patterns that corrected for impedimentary short-wave scattering; then behavioral responses were measured to expanding discs that varied between dark and bright UV with a green background. Flies responded to all intensities of UV discs, indicating that motion vision is not driven by a single luminance channel alone and that color therefore contributes. Notably, responses to ON-motion were much more sensitive to UV than those to OFF-motion in a range of Drosophila species. To identify the cells responsible, genetic reagents were developed to manipulate the function of classes of photoreceptors, and to identify cells downstream of R7, two-photon calcium imaging of neuronal activity and genetic silencing in behavioral experiments were used. Analysis of these results generated the counterintuitive prediction that the contribution of color to motion vision would not support the detection of green discs seen against UV, even though both UV and green discs have the same chromatic contrast, and remarkably, this was the case. Finally, this study determined that the mechanism can be employed in other visual systems to detect objects of other colors. These findings identify neurons linking photoreceptors required for color vision to the different spectral sensitivities of the motion vision pathways. They show how the mechanism can be selective for UV objects in Drosophila, and can be employed for color-selectivity in other visual systems (Longden, 2023).

This study has shown that color contributes to motion vision for UV-green edges in Drosophila. Behavioral responses to ON-motion were much more sensitive to UV than responses to OFF-motion , a difference requiring the R7-8 photoreceptors. The T4 and T5 cells that process ON- and OFF-motion, showed a corresponding difference in their sensitivity to UVe, and in the cells linking the R7-8 photoreceptors and T4 cells, there were consistent spectral differences between lamina monopolar cells and the medulla T4 input cells: L5 and Mi4 were less sensitive to UV, and L3 and Mi9 were more sensitive to UV, compared to the L1 driven Mi1 and Tm3. Silencing cells with divergent isoluminance levels (L3, Mi4 and Mi9) also reduced the difference between the ON- and OFF-motion isoluminance levels, indicating a causal role in behavioral tests. This study correctly predicted that if the augmented UV-sensitivity of ON-motion processing explained the contribution of color to motion vision, then green discs should not be visible against a UV background that was neither bright nor dark. Finally, the contribution of color to motion vision was shown to be not just a mechanism for resolving low-contrast UV edges, but can also be organized to preferentially support the motion detection of objects of specific colors (Longden, 2023).

Previous studies have shown that motion vision is colorblind for blue-green gratings in flies. It is proposed that these studies did not observe a contribution of color to motion vision because gratings induce both ON- and OFF-motion, so differences between these pathways cannot be isolated, and because blue stimuli only weakly drive the UV-sensitive R7 photoreceptors. That work was extended by not using gratings, and by developing methods to accurately display wide-field UV-green stimuli. Prior work also indicated that color might contribute to motion vision by broadening the spectral sensitivity of the luminance channel through unidentified cellular mechanisms, and subsequent EM reconstructions indicated that the R7 and R8 photoreceptors form synapses in the medulla with cells specifically presynaptic to T4. These results indicate that UV-sensitivity is maintained along the R7-L3-Mi9-T4 pathway and predict that R7 cells innervate L3. Indeed, it was recently demonstrated in an EM reconstruction study that R7 cells form substantial numbers of previously unreported synapses with L3 and other cells in the optic chiasm between the lamina and medulla. L3 has been recently identified as critical for encoding luminance information, particularly in dim light conditions. These experiments used only a restricted range of luminance levels, and was focused on providing a tight linkage between visual stimuli used for behavior and imaging. It was demonstrated that the UV-sensitivity of behavioral ON-motion responses scaled in proportion to a doubling of the green channel luminance, but it will be important to establish if the bright luminance of full daylight alters the effects: it is possible that UV augments the detection of approaching color objects only during the dawn and dusk periods favored by Drosophila melanogaster (Longden, 2023).

In future work it will be possible to causally test the contribution of cell types to UV-sensitivity along the ON-motion pathway by using multiple expression control systems to silence a cell type and independently image from downstream cells. However, understanding how sensitivity to UV propagates from R7 through the lamina and medulla circuitry to T4 cells is complex due to sign changes, asymmetric spectral tuning, and recurrent connections along the pathway. To focus on just one example, the Mi4 and Mi9 cell types, which are inhibitory to T4 cells, heavily synapse onto each other and reciprocal inhibition between these cell types may amplify their chromatic differences (Longden, 2023).

Although this study used expanding discs that triggered aversive turns, it is not thought that color motion vision is specifically adapted for predator evasion, particularly because it was possible to carefully map the UV-sensitivity of ON- and OFF-motion using moving edges. Flies are attracted to UV using motion-independent visual pathways and using a stimulus that generated aversive turns allowed ensuring that UV phototaxis was not masking deficits in motion vision. In natural situations, the chromatic motion vision mechanisms identified in this study may combine with phototaxis and other visual processing to identify salient edges as the fly navigates its path. How motion and chromatic signals are integrated in output cells of the optic lobes, such as the lobula columnar cells, many of which respond to looming including LC4, LC6, LC10, LC16 and LPLC2, is currently being explored. Across these cell types, it is possible that different pathways mediate the response to dark and bright discs (Longden, 2023).

This study established that augmented ON-motion UV-sensitivity is not limited to D. melanogaster but is also displayed by other drosophilids. Among invertebrates, color has been reported to contribute to motion vision in other insects including the honeybee and the butterfly Papilio xuthus, whose behavioral responses to moving colored ON- and OFF-edges indicated that responses to ON-motion were more sensitive to red, compared to responses to OFF-motion that were more sensitive to blue and green. If Papilio implements the chromatic mechanism that this study has proposed for Drosophila, then this would predict that its ON- and OFF-motion pathways support seeing red objects against green backgrounds, for example red flowers set against foliage. However, in Papilio, spectral information is preprocessed in the lamina through lateral connections that are not found in Drosophila, indicating earlier interactions between the color and other visual pathways. Tantalizingly, central brain neurons responding to gratings with chromatic contrast have been discovered that project to the medulla, but the supporting cellular mechanisms remain unknown (Longden, 2023).

In vertebrates, differences in the spectral sensitivity to ON- and OFF-motion have not been thoroughly investigated, and if present they could support a contribution of color to motion vision as was found for Drosophila. Larval zebrafish use UV-ON processing to detect paramecia while foraging, and the mechanism described in this study has the potential to operate in zebrafish to enhance the detection of approaching paramecia. Mice are also sensitive to UV and green wavelengths, and since they have the greatest chromatic sensitivity in the visual circuits viewing the sky, they are thought to use color vision to detect approaching predators. Again, the mechanism described in this study is in theory directly applicable to the mouse visual system: it predicts that if OFF-motion responses were more sensitive to UV than for ON-motion, this would favor the detection of an approaching object seen against a UV-rich sky. In mice, ON and OFF retinal ganglion cells have been recently reported that nonlinearly integrate UV and green to allow an OFF cell, for example, to have different isoluminance levels for UV-OFF and green-ON and for green-OFF and UV-ON. In this cell the spectral divergence of ON and OFF processing supports the detection of a light decrement, whether the decrement is in green or UV wavelengths (UV-OFF and green-OFF), and in other cells the nonlinearities were UV-selective (UV-ON and UV-OFF). These nonlinearities are algorithmically very similar to the mechanism proposed in this stduy and indicate a potential platform for motion-sensitive cells downstream to be sensitive to isoluminant motion, and to preferentially detect objects rich in UV or green, relative to the background. In primates, motion-sensitive cells in area MT contribute to the smooth pursuit tracking of objects and frequently retain some degree of chromatic sensitivity, such that around the isoluminance level the response to motion is decreased, but not to zero. Any implementation in another animal would have to be integrated with many aspects of visual processing, such as luminance, and may not involve directional selectivity, for example. Nevertheless, the current results reveal that, in addition to allowing the animal to view isoluminant edges, an individual cell’s chromatic sensitivity may be organized to enhance the detection of motion of targets of a particular color (Longden, 2023).

In summary, this study has shown how UV contributes to the processing of ON-motion in Drosophila in a way that enhances responses to expanding UV discs. Key cellular components are reported of how color contributes to motion vision in flies, the R7-8 and T4 cells, and how cells linking them show consistent differences in spectral tuning and alter the behavior when silenced. How a spectral divergence in ON- and OFF-motion processing can be used to favor objects of a specific color was shown, an insight that is directly applicable to many vertebrate and invertebrate sensory systems (Longden, 2023).

In natural environments, light intensities and visual contrasts vary widely, yet neurons have a limited response range for encoding them. Neurons accomplish that by flexibly adjusting their dynamic range to the statistics of the environment via contrast normalization. The effect of contrast normalization is usually measured as a reduction of neural signal amplitudes, but whether it influences response dynamics is unknown. This study shows that contrast normalization in visual interneurons of the Drosophila melanogaster optic lobe not only suppresses the amplitude but also alters the dynamics of responses when a dynamic surround is present. A simple model is presented that qualitatively reproduces the simultaneous effect of the visual surround on the response amplitude and temporal dynamics by altering the cells' input resistance and, thus, their membrane time constant. In conclusion, single-cell filtering properties as derived from artificial stimulus protocols like white-noise stimulation cannot be transferred one-to-one to predict responses under natural conditions (Pirogova, 2023).

Visual pathways from the compound eye of an insect relay to four neuropils, successively the lamina, medulla, lobula, and lobula plate in the underlying optic lobe. The medulla, lobula, and lobula plate are interconnected by the complex second optic chiasm, through which the anteroposterior axis undergoes an inversion between the medulla and lobula. By densely reconstructing axon trajectories using a volumetric scanning electron microscopy technique, this study reveals the three-dimensional structure of the second optic chiasm of Drosophila comprising interleaving bundles and sheets of axons insulated from each other by glial sheaths. These axon bundles invert their horizontal sequence in passing between the medulla and lobula. ~19,500 cells per hemisphere, about two thirds of the optic lobe neurons, contribute to the second chiasm, most being Tm cells, with an estimated additional 2,780 T4 and T5 cells each. The chiasm mostly comprises axons and cell body fibers, but also a few synaptic elements. It is proposed that a chiasmal structure between the neuropils is potentially advantageous for processing complex visual information in parallel. The EM reconstruction suggest that the projection patterns of the neurons comprising the chiasm may be determined by the proliferation centers from which the neurons develop. Such a complex wiring pattern could only have arisen in several evolutionary steps (Shinomiya, 2019).

Specialized ommatidia harboring polarization-sensitive photoreceptors exist in the 'dorsal rim area' (DRA) of virtually all insects. Although downstream elements have been described both anatomically and physiologically throughout the optic lobes and the central brain of different species, little is known about their cellular and synaptic adaptations and how these shape their functional role in polarization vision. Previous work has shown that in the DRA of Drosophila melanogaster, two distinct types of modality-specific 'distal medulla' cell types (Dm-DRA1 and Dm-DRA2) are post-synaptic to long visual fiber photoreceptors R7 and R8, respectively. This study describes additional neuronal elements in the medulla neuropil that manifest modality-specific differences in the DRA region, including DRA-specific neuronal morphology, as well as differences in the structure of pre- or post-synaptic membranes. Furthermore, certain cell types (medulla tangential cells and octopaminergic neuromodulatory cells) were shown to specifically avoid contacts with polarization-sensitive photoreceptors. Finally, while certain transmedullary cells are specifically absent from DRA medulla columns, other subtypes show specific wiring differences while still connecting the DRA to the lobula complex, as has previously been described in larger insects. This hints towards a complex circuit architecture with more than one pathway connecting polarization-sensitive DRA photoreceptors with the central brain (Sancer, 2019b).

To navigate through the environment, animals rely on visual feedback to control their movements relative to their surroundings. In dipteran flies, visual feedback is provided by the wide-field motion-sensitive neurons in the visual system called lobula plate tangential cells (LPTCs). Understanding the role of LPTCs in fly behaviors can address many fundamental questions on how sensory circuits guide behaviors. The blowfly was estimated to have ~ 60 LPTCs, but only a few have been identified in Drosophila. A Gal4 driver screen was conducted and five LPTC subtypes were identified in Drosophila, based on their morphological characteristics: LPTCs have large arborizations in the lobula plate and project to the central brain. Their morphologies were compared to the blowfly LPTCs and were named after the most similar blowfly cells: CH, H1, H2, FD1 and FD3, and V1. Their pre- and post-synaptic organizations, as well as their neurotransmitter profiles, were further characterized. These anatomical features largely agree with the anatomy and function of their likely blowfly counterparts. Nevertheless, several anatomical details indicate the Drosophila LPTCs may have more complex functions. This characterization of these five LPTCs in Drosophila will facilitate further functional studies to understand their roles in the visual circuits that instruct fly behaviors (Wei, 2019).

The neural apparatus for detecting the direction of visual movement consists of two spatially separated input lines that are asymmetrically filtered in time and then interact in a nonlinear way. However, the cellular implementation of this computation remains elusive. Recent connectomic data of the Drosophila optic lobe has suggested a neural circuit for the detection of moving bright edges (ON motion) with medulla cells Mi1 and Tm3 providing spatially offset input to direction-selective T4 cells, thereby forming the two input lines of a motion detector. Electrophysiological characterization of Mi1 and Tm3 revealed different temporal filtering properties and proposed them to correspond to the delayed and direct input, respectively. This hypothesis was tested by silencing either Mi1 or Tm3 cells and using electrophysiological recordings and behavioral responses of flies as a readout. It was shown that Mi1 is a necessary element of the ON pathway under all stimulus conditions. In contrast, Tm3 is specifically required only for the detection of fast ON motion in the preferred direction. This study thereby provides first functional evidence that Mi1 and Tm3 are key elements of the ON pathway and uncover an unexpected functional specialization of these two cell types. The results thus require an elaboration of the currently prevailing model for ON motion detection and highlight the importance of functional studies for neural circuit breaking (Ammer, 2015).

Two candidate pathways for ON- and OFF-edge motion detection in the visual system act via circuits that use respectively either T4 or T5, two cell types of the fourth neuropil, or lobula plate (LOP), that exhibit narrow-field direction-selective responses and provide input to wide-field tangential neurons. T4 or T5 both have four subtypes that terminate one each in the four strata of the LOP. Representatives are reported in a wide range of Diptera, and both cell types exhibit various similarities in: (1) the morphology of their dendritic arbors; (2) their four morphological and functional subtypes; (3) their cholinergic profile in Drosophila; (4) their input from the pathways of L3 cells in the first neuropil, or lamina (LA), and by one of a pair of LA cells, L1 (to the T4 pathway) and L2 (to the T5 pathway); and (5) their innervation by a single, wide-field contralateral tangential neuron from the central brain. Progenitors of both also express the gene atonal early in their proliferation from the inner anlage of the developing optic lobe, being alone among many other cell type progeny to do so. Yet T4 receives input in the second neuropil, or medulla (ME), and T5 in the third neuropil or lobula (LO). This study suggests that these two cell types were originally one, that their ancestral cell population duplicated and split to innervate separate ME and LO neuropils, and that a fiber crossing-the internal chiasma-arose between the two neuropils (Shinomiya, 2015).

Spatial contrast, the difference in adjacent luminance values, provides information about objects, textures, and motion and supports diverse visual behaviors. Contrast computation is therefore an essential element of visual processing. The underlying mechanisms, however, are poorly understood. In human psychophysics, contrast illusions are means to explore such computations, but humans offer limited experimental access. Via behavioral experiments in Drosophila, this study shows that flies are also susceptible to contrast illusions. Using genetic silencing techniques, electrophysiology, and modeling, the mechanisms and neuronal correlates underlying the behavior were dissected. Results indicate that spatial contrast computation involves lateral inhibition within the same pathway that computes motion of luminance increments (ON pathway). Yet motion-blind flies, in which downstream motion-sensitive neurons needed for optomotor behavior were silenced, exhibit fully intact contrast responses. In conclusion, spatial contrast and motion cues are first computed by overlapping neuronal circuits which subsequently feed into parallel visual processing streams (Bahl, 2015).

Across animal phyla, motion vision relies on neurons that respond preferentially to stimuli moving in one, preferred direction over the opposite, null direction. In the elementary motion detector of Drosophila, direction selectivity emerges in two neuron types, T4 and T5, but the computational algorithm underlying this selectivity remains unknown. This study found that the receptive fields of both T4 and T5 exhibit spatiotemporally offset light-preferring and dark-preferring subfields, each obliquely oriented in spacetime. In a linear-nonlinear modeling framework, the spatiotemporal organization of the T5 receptive field predicts the activity of T5 in response to motion stimuli. These findings demonstrate that direction selectivity emerges from the enhancement of responses to motion in the preferred direction, as well as the suppression of responses to motion in the null direction. Thus, remarkably, T5 incorporates the essential algorithmic strategies used by the Hassenstein-Reichardt correlator and the Barlow-Levick detector. The model developed in this paper for T5 also provides an algorithmic explanation for the selectivity of T5 for moving dark edges: the model captures all two- and three-point spacetime correlations relevant to motion in this stimulus class. More broadly, the findings reveal the contribution of input pathway visual processing, specifically center-surround, temporally biphasic receptive fields, to the generation of direction selectivity in T5. As the spatiotemporal receptive field of T5 in Drosophila is common to the simple cell in vertebrate visual cortex, this stimulus-response model of T5 will inform efforts in an experimentally tractable context to identify more detailed, mechanistic models of a prevalent computation (Leong, 2016).

In flies, the direction of moving ON and OFF features is computed separately. T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. A recent study of T4 found that the integration of offset depolarizing and hyperpolarizing inputs is critical for the generation of directional selectivity. However, T5s lack small-field inhibitory inputs, suggesting they may use a different mechanism. This study used whole-cell recordings of T5 neurons and found a similar receptive field structure: fast depolarization and persistent, spatially offset hyperpolarization. By assaying pairwise interactions of local stimulation across the receptive field, no amplifying responses, only suppressive responses were found to the non-preferred motion direction. Passive, biophysical models were evaluated, and ut was found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli (Gruntman, 2019).

Understanding information coding is important for resolving the functions of visual neural circuits. The motion vision system is a classic model for studying information coding as it contains a concise and complete information-processing circuit. In Drosophila, the axon terminals of motion-detection neurons (T4 and T5) project to the lobula plate, which comprises four regions that respond to the four cardinal directions of motion. The lobula plate thus represents a topographic map on a transverse plane. This enables study of the coding of diagonal motion by investigating its response pattern. By using in vivo two-photon calcium imaging, the axon terminals of T4 and T5 cells in the lobula plate were found to be activated during diagonal motion. Further experiments showed that the response to diagonal motion is distributed over the following two regions compared to the cardinal directions of motion-a diagonal motion selective response region and a non-selective response region-which overlap with the response regions of the two vector-correlated cardinal directions of motion. Interestingly, the sizes of the non-selective response regions are linearly correlated with the angle of the diagonal motion. These results revealed that the Drosophila visual system employs a composite coding for diagonal motion that includes both independent coding and vector decomposition coding (Yue, 2016).

Animals estimate visual motion by integrating light intensity information over time and space. The integration requires nonlinear processing, which makes motion estimation circuitry sensitive to specific spatiotemporal correlations that signify visual motion. Classical models of motion estimation weight these correlations to produce direction-selective signals. However, the correlational algorithms they describe have not been directly measured in elementary motion-detecting neurons (EMDs). This study employed stimuli to directly measure responses to pairwise correlations in Drosophila's EMD neurons, T4 and T5. Activity in these neurons was required for behavioral responses to pairwise correlations and was predictive of those responses. The pattern of neural responses in the EMDs was inconsistent with one classical model of motion detection, and the timescale and selectivity of correlation responses constrained the temporal filtering properties in potential models. These results reveal how neural responses to pairwise correlations drive visual behavior in this canonical motion-detecting circuit (Salazar-Gatzimas, 2016).

The receptor mechanism for color vision has been extensively studied. In contrast, the circuit(s) that transform(s) photoreceptor signals into color percepts to guide behavior remain(s) poorly characterized. Using intersectional genetics to inactivate identified subsets of neurons in the optic lobe, this study has uncovered the first-order interneurons that are functionally required for hue discrimination in Drosophila. A novel aversive operant conditioning assay was developed for intensity-independent color discrimination (true color vision) in Drosophila. Single flying flies are magnetically tethered in an arena surrounded by blue and green LEDs (light-emitting diodes). The flies' optomotor response is used to determine the blue-green isoluminant intensity. Flies are then conditioned to discriminate between equiluminant blue or green stimuli. Wild-type flies are successfully trained in this paradigm when conditioned to avoid either blue or green. Functional color entrainment requires the function of the narrow-spectrum photoreceptors R8 and/or R7, and is within a limited range, intensity independent, suggesting that it is mediated by a color vision system. The medulla projection neurons, Tm5a/b/c and Tm20, receive direct inputs from R7 or R8 photoreceptors and indirect input from the broad-spectrum photoreceptors R1-R6 via the lamina neuron L3. Genetically inactivating these four classes of medulla projection neurons abolished color learning. However, inactivation of subsets of these neurons is insufficient to block color learning, suggesting that true color vision is mediated by multiple redundant pathways. It is hypothesized that flies represent color along multiple axes at the first synapse in the fly visual system. The apparent redundancy in learned color discrimination sharply contrasts with innate ultraviolet (UV) spectral preference, which is dominated by a single pathway from the amacrine neuron Dm8 to the Tm5c projection neurons (Melnattur, 2014).

Given the cellular and synaptic complexity of medulla circuitry, as well as the wealth of distinct behaviours that are guided by visual motion, additional cell types are likely to play computational roles in Drosophila elementary motion detectors. Nonetheless, the data show that Mi1, Tm3, Tm1 and Tm2 possess response properties that are consistent with implementing the algorithmic steps that precede the correlation operations in the motion detecting pathways in Drosophila (Behnia, 2014).

Associated with their proposed role as matched filters for sensing the optic flow generated by an animal's self-motion, in contrast to dLGN neurons, lobula plate tangential cells have large receptive fields, in some cases covering more than 100 degrees of visual space. Independent movement, e.g., originating from conspecifics or foliage, thus poses a challenge to the system by providing excitatory drive to tangential cells not associated with self-motion. The current experiments with intact and silenced LPi neurons support the idea that such inputs are attenuated by antagonistic signals from oppositely moving objects elsewhere in the visual scene. Perhaps more importantly, different flight maneuvers generate ambiguous optic flow patterns in sub-parts of the receptive field. For instance, both lift and forward translation cause downward optic flow in the ventral visual field, while only the latter produces upward flow dorsally. Taking into account excitation only, a reliable distinction between those patterns, especially under varying stimulus intensities, i.e., contrasts as experienced in natural scenes, seems inconceivable. This study has demonstrated that LPi cells strongly reduce such ambiguities, most likely by cancelling the excitation caused in one part of the dendrite by inhibition in another part. Motion opponency is thus reminiscent of other neural opponent mechanisms. In the classical example of color opponency, neural comparison discriminates sensory signals that are ambiguous at the level of photoreceptors in terms of wavelength and stimulus intensity. Notably, while color vision requires at least two separate measurements at any point in space, motion opponency disambiguates different optic flowfields derived from the same photoreceptor responses. Given that wide-field motion-sensitive neurons in various other systems are also motion opponent, it is suggested that such a mechanism might be universally required to increase sensitivity and selectivity for optic flow-fields associated with selfmotion. Similar neural comparators might be widely used for the extraction of equally complex sensory features (Mauss, 2015).

Optical illusions provide powerful tools for mapping the algorithms and circuits that underlie visual processing, revealing structure through atypical function. Of particular note in the study of motion detection has been the reverse-phi illusion. When contrast reversals accompany discrete movement, detected direction tends to invert. This occurs across a wide range of organisms, spanning humans and invertebrates. This study mapped an algorithmic account of the phenomenon onto neural circuitry in the fruit fly Drosophila melanogaster. Through targeted silencing experiments in tethered walking flies as well as electrophysiology and calcium imaging, it was demonstrated that ON- or OFF-selective local motion detector cells T4 and T5 are sensitive to certain interactions between ON and OFF. A biologically plausible detector model accounts for subtle features of this particular form of illusory motion reversal, like the re-inversion of turning responses occurring at extreme stimulus velocities. In light of comparable circuit architecture in the mammalian retina, it is suggested that similar mechanisms may apply even to human psychophysics (Leonhardt, 2017).

The assembly of functional neuronal circuits requires growth cones to extend in defined directions and recognize the correct synaptic partners. Homophilic adhesion between vertebrate Sidekick proteins promotes synapse formation between retinal neurons involved in visual motion detection.This study shows that that Drosophila Sidekick accumulates in specific synaptic layers of the developing motion detection circuit and is necessary for normal optomotor behavior. Sidekick is required in photoreceptors, but not their target lamina neurons, to promote the alignment of lamina neurons into columns and subsequent sorting of photoreceptor axons into synaptic modules based on their precise spatial orientation. Sidekick is also localized to the dendrites of the direction-selective T4 and T5 cells, and is expressed in some of their presynaptic partners. In contrast to its vertebrate homologues, Sidekick is not essential for T4 and T5 to direct their dendrites to the appropriate layers or to receive synaptic contacts. These results illustrate a conserved requirement for Sidekick proteins to establish visual motion detection circuits that is achieved through distinct cellular mechanisms in Drosophila and vertebrates (Astigarraga, 2018).

Establishing synaptic circuits requires neurons to identify and interact with the correct partners within a highly complex cellular environment. These interactions depend on the combinatorial actions of a wide variety of cell-surface adhesion molecules. Proteins of the cadherin, neurexin, leucine-rich repeat and immunoglobulin (Ig) superfamilies can engage in homophilic or heterophilic interactions, leading either to adhesion or repulsion of the expressing cells. It is not yet clear how such molecular interactions enable the assembly of complex neural circuits with high specificity (Astigarraga, 2018).

The Drosophila visual system has been used as a model system for many studies of the development and function of neural circuits. Each ommatidium in the compound eye contains eight photoreceptors: R1-R6 express rhodopsin Rh1 and detect visual motion, while R7 and R8 each express one of four different rhodopsins and mediate color vision. Motion detection is subdivided into 'ON' and 'OFF' pathways that are specialized to detect moving bright edges and moving dark edges, respectively. Lamina neurons L1 and L2 receive direct synaptic input from R1-R6; L1 provides input to the 'ON' pathway, while L2 is the major link to the 'OFF' pathway. Information from the lamina is conveyed to the medulla, where the 'ON' and 'OFF' signals are transmitted by distinct populations of neurons that converge on the dendrites of T4 ('ON') and T5 ('OFF') lobula plate neurons in specific layers of the medulla and lobula, respectively. Four subtypes of T4 and T5 neurons are each sensitive to one of the four cardinal directions of motion and provide input to lobula plate tangential neurons that are segregated in four corresponding layers (Astigarraga, 2018).

In the third larval instar, Hedgehog and Spitz signals from the R1-R6 photoreceptor axons induce the final division and neuronal differentiation of their target lamina neurons. Interactions between the Ig family members Hibris (Hbs) on postmitotic lamina neurons and Roughest (Rst) on photoreceptor axons then induce the lamina neurons to line up in columns along the retinal axon bundles. The R1-R6 axons from a single ommatidium subsequently diverge during pupal development to project to six different synaptic modules known as lamina cartridges, enabling each cartridge to collect information from six photoreceptors located in different neighboring ommatidia that view the same point in visual space. This sorting process requires the early polarization of the 'front' of each photoreceptor growth cone towards its target, while the 'heels' of these growth cones form a stable scaffold. Correct polarization and extension are thought to require a comparison of the relative levels of the cadherin Flamingo (Fmi) on neighboring growth cones, as well as adhesive interactions with other growth cones and target lamina neurons that are mediated by N-cadherin (Ncad). Photoreceptor axons later develop synaptic terminals that connect to their target lamina neurons through tetrad synapses, at which a single presynaptic active zone contacts four postsynaptic cells. Two of these are always the lamina neurons L1 and L2, while the others may be L3 in combination with amacrine or glial cells (Astigarraga, 2018).

The vertebrate retina is comparable in complexity and organization to the combined Drosophila retina, lamina and medulla, and the two visual systems share many design principles. In the chick retina, synapses between different types of bipolar, amacrine and ganglion cells form in distinct sublaminae of the inner plexiform layer that are specified by homophilic adhesion molecules of the Sidekick (Sdk), Down's syndrome cell adhesion molecule (Dscam) and Contactin families. Each of these proteins is localized to a specific layer and expressed by cells that synapse on each other in that layer. In the mouse retina, sidekick cell-adhesion molecules 1 and 2 (Sdk1 and Sdk2) label largely non-overlapping subsets of cells, but these are not clearly separated by layer. Sdk2 is required in both VG3 amacrine cells and W3B retinal ganglion cells to form a strong synaptic connection between these neurons, which sense object motion relative to the surroundings (Astigarraga, 2018 and references therein).

The Drosophila genome encodes a single Sdk protein. Null sdk alleles have previously been shown to produce small changes in the numbers of photoreceptors and non-neuronal cells in the eye (Nguyen, 1997). This study shows that Sdk is a homophilic adhesion molecule that is expressed in several cells of the visual motion detection circuit and is localized to specific synaptic layers. Consistent with its location, mutants lacking Sdk show defects in optomotor behavior. sdk was found to be required in photoreceptors, but not in their target lamina neurons, for the correct organization of lamina columns and cartridges, indicating that, unlike its vertebrate counterparts, Sdk does not mediate adhesion between pre- and post-synaptic cells. Although Sdk is present on the dendrites of the direction-selective T4 and T5 cells, it is not required for these neurites to arborize in the appropriate layer or to receive synaptic input. These results suggest that Sdk proteins in Drosophila and vertebrates have a common functional role in establishing visual motion detection circuits, but act through distinct cellular mechanisms (Astigarraga, 2018).

This study shows that Drosophila Sdk is required to establish a functional pathway for visual motion detection. Defective optomotor behavior in sdk mutant flies appears to result from defects in the organization of connections between R1-R6 photoreceptors and their target lamina neurons. Sdk acts in photoreceptors both to organize lamina neurons into columns and to enable photoreceptor axons to sort to the correct lamina cartridges. Sdk is also expressed in T4 and T5 neurons, the output cells of the 'ON' and 'OFF' motion detection circuits; however, no morphological or functional defects were observed in these cells in the absence of sdk (Astigarraga, 2018).

In the chicken retina, it was proposed that the homophilic adhesion molecules Sdk1 and Sdk2 each drive neurons to arborize and to form synapses with other neurons that express the same Sdk in a specific sublamina of the inner plexiform layer. Drosophila Sdk is also a homophilic adhesion molecule. However, because it is only required in photoreceptors, Sdk-Sdk binding cannot directly mediate the interaction of lamina neurons with photoreceptor axons. The simplest explanation for the presence of lamina neurons beneath the R1-R6 growth cone layer in sdk mutants or when sdk function is removed from the eye is that Sdk-mediated adhesion between photoreceptor growth cones normally forms a barrier to the movement of lamina neurons. An alternative possibility is that Sdk on photoreceptors might interact with a heterophilic binding partner on lamina neurons. The existence of a splice form of mouse Sdk1 that lacks the first two Ig domains, which are necessary for homophilic adhesion, suggests the possibility of functions other than homophilic binding. The only cell-adhesion molecules known to promote interactions between lamina neurons and photoreceptor axons are Rst and Hbs, Ig superfamily members that are homologous to vertebrate Neph and Nephrin proteins, respectively. Loss of Sdk does not affect the ability of lamina neurons to associate with photoreceptor axons to the same extent as loss of Rst or Hbs, but it is possible that Sdk might modulate the interaction between them. Such an effect might explain why human Sdk1 upregulation contributes to the pathology of kidney diseases such as HIV-induced nephropathy and focal segmental glomerulosclerosis, as interacting Neph and Nephrin molecules form the slit diaphragm in the kidney (Astigarraga, 2018).

Homophilic adhesion is also a likely explanation for the requirement for Sdk in photoreceptor axon sorting. Sorting of the axons from a single ommatidium to six different lamina cartridges requires R1-R6 growth cones to polarize such that their mobile 'fronts' move away from their stable 'heels' at characteristic angles. The pattern of Sdk labeling in five puncta, two of which arise from and are adjacent to R4, is consistent with a location at the contact points between the six heels, which are arrayed in a similar crescent. Two cadherins, Fmi and Ncad, have previously been shown to affect photoreceptor growth cone extension and sorting in the lamina. Although they have partially redundant roles, Ncad functions largely autonomously to mediate interactions with target cells and with photoreceptor growth cones within and across ommatidia, whereas a comparison of Fmi levels between neighboring growth cones appears to promote normal polarization. It is suggested that Sdk mediates adhesion between growth cone heels, enabling them to form a stable scaffold that may facilitate the comparison of Fmi levels or provide mechanical resistance necessary for polarized extension (Astigarraga, 2018).

In contrast to knockdown studies in the chick retina, loss of Drosophila sdk does not alter the layer-specific arborization patterns of the Sdk-expressing neurons examined in this study. Sdk is localized to the dendrites of T4 and T5, and is present in at least one of their presynaptic partners. Nonetheless, T4 and T5 still extend their dendrites in the correct layers and form arbors of the normal size and shape in the absence of sdk. In the mouse retina, Sdk2 controls the strength of synaptic connections between Sdk2-expressing neurons that detect motion of the central visual field relative to the surround. This study finds that the Sdk-expressing neuron Tm9 does not require sdk to form synapses with T5, as shown both by punctate localization of the active zone protein Brp at its axon terminals and by activity-dependent synaptic GRASP. It is possible that Sdk is required for synapse formation by other neurons that are presynaptic to T4 or T5, or that it recruits specific postsynaptic proteins to these synapses. Nevertheless, removing sdk function from T4 and T5 does not result in significant defects in optomotor behavior, arguing against an essential role for sdk in these cells (Astigarraga, 2018).

It has been suggested that the fly and vertebrate visual systems share common design principles reflecting deep evolutionary homology. The structural organization of the vertebrate retina resembles the Drosophila retina, lamina and medulla, and the computational mechanisms underlying motion detection also have many common features despite anatomical and molecular differences in the circuits. The current developmental and behavioral studies demonstrate that Sdk shares a physiological function in visual motion detection with its vertebrate counterparts, although their cellular mechanisms of action appear to differ. The functions of other molecules have also diverged between Drosophila and vertebrates; for example, the extensive alternative splicing of Drosophila Dscam1 allows it to mediate self-avoidance during growth and synaptogenesis, contrary to the adhesive function of chick Dscams. Self-avoidance in the mouse instead depends on clustered protocadherins, which achieve diversity through promoter duplication. It has been proposed that the formation of visual motion detection circuits in vertebrate and invertebrate visual systems is controlled by a genetic network derived from a common ancestor. The data suggest that evolutionary changes may include the repurposing of common elements of such a network to mediate distinct functions (Astigarraga, 2018).

A neuron that extracts directionally selective motion information from upstream signals lacking this selectivity must compare visual responses from spatially offset inputs. Distinguishing among prevailing algorithmic models for this computation requires measuring fast neuronal activity and inhibition. In the Drosophila melanogaster visual system, a fourth-order neuron-T4-is the first cell type in the ON pathway to exhibit directionally selective signals. This study used in vivo whole-cell recordings of T4 to show that directional selectivity originates from simple integration of spatially offset fast excitatory and slow inhibitory inputs, resulting in a suppression of responses to the nonpreferred motion direction. A passive, conductance-based model of a T4 cell was constructed that accurately predicts the neuron's response to moving stimuli. These results connect the known circuit anatomy of the motion pathway to the algorithmic mechanism by which the direction of motion is computed (Gruntman, 2018).

Sensory maps and neural circuits are largely genetically 'hardwired' in Drosophila and are usually activity independent. Despite this developmental rigidity, there is a very limited understanding of how genetic programs drive developmental processes that are able to establish precise neural circuits. This study shows that the neurogenic program that specifies the identity of the eight T4/T5 neuron subtypes is also sufficient to establish the coherent retinotopy that supports global motion perception in the fly. It provides an example of how the establishment of connectivity within a neural circuit can only be fully understood in its developmental context. The existence of a causal link between the genetic program that specifies cell fate and the circuit these cells build provides an example of how a complex hardwired neuronal circuit can be built from simple developmental rules (Pinto-Teixeira, 2018).

Many animals guide their movements using optic flow, the displacement of stationary objects across the retina caused by self-motion. How do animals selectively synthesize a global motion pattern from its local motion components? To what extent does this feature selectivity rely on circuit mechanisms versus dendritic processing? This study used in vivo calcium imaging to identify pre- and postsynaptic mechanisms for processing local motion signals in global motion detection circuits in Drosophila. Lobula plate tangential cells (LPTCs) detect global motion by pooling input from local motion detectors, T4/T5 neurons. T4/T5 neurons were shown to suppress responses to adjacent local motion signals whereas LPTC dendrites selectively amplify spatiotemporal sequences of local motion signals consistent with preferred global patterns. It is proposed that sequential nonlinear suppression and amplification operations allow optic flow circuitry to simultaneously prevent saturating responses to local signals while creating selectivity for global motion patterns critical to behavior (Barnhart, 2018).

While linear mechanisms lay the foundations of feature selectivity in many brain areas, direction selectivity in the elementary motion detector (EMD) of the fly has become a paradigm of nonlinear neuronal computation. This study has bridged this divide by demonstrating that linear spatial summation can generate direction selectivity in the fruit fly Drosophila. Using linear systems analysis and two-photon imaging of a genetically encoded voltage indicator, the emergence was measured of direction-selective (DS) voltage signals in the Drosophila OFF pathway. This study is a direct, quantitative investigation of the algorithm underlying directional signals, with the striking finding that linear spatial summation is sufficient for the emergence of direction selectivity. A linear stage of the fly EMD strongly resembles similar computations in vertebrate visual cortex, demands a reappraisal of the role of upstream nonlinearities, and implicates the voltage-to-calcium transformation in the refinement of feature selectivity in this system (Wienecke, 2018).

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In Drosophila melanogaster, recently discovered synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest a motion model that is increasingly intricate when compared with the ubiquitous Hassenstein-Reichardt model. By contrast, knowledge of OFF-pathway (T5) has been incomplete. This study presents a conclusive and comprehensive connectome that, for the first time, integrates detailed connectivity information for inputs to both the T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, previous findings in the T4 pathway were successfully corroborated, and inputs and receptive fields for T5 were comprehensively identified. Although the two pathways are probably evolutionarily linked and exhibit many similarities, interesting differences and interactions were uncovered that may underlie their distinct functional properties (Shinomiya, 2019).

Over half a century ago, Hassenstein and Reichardt working on the beetle Chlorophanus, and later Reichardt working on flies and studying rabbit retinal ganglion cells, all independently presented evidence for motion detection circuits that incorporate a delay-and-compare strategy. In both insect and mammalian model groups, two or more independent, parallel inputs from upstream neurons provide input to elementary motion detector (EMD) circuits. Both models use a similar mechanism to compute the direction of motion, but they differ depending on how they produce a direction-selective response. The Barlow-Levick type circuit detects the preferred-direction signals by suppressing signals in the non-preferred direction; the Hassenstein-Reichardt detector generates an enhancement of signals in the preferred direction (Shinomiya, 2019 and references therein).

The fly's optic lobe consists of four consecutive neuropils: the lamina, medulla, lobula, and lobula plate. Each of these comprises columnar units that correspond to the array of ommatidia in the retina. The motion pathway in the optic lobe arises from the photoreceptor cells (PRs), which receive light signals in the compound eye and extend their axons to the lamina. R1-R6 cells expressing rhodopsin rh1 provide signals to lamina monopolar cells in the lamina cartridges, which project to the distal medulla. The lamina neurons are presynaptic to various types of medulla neurons in the distal medulla. Among them, medulla columnar neurons including Mi, Tm, and TmY cells further provide inputs to the dendritic arbors of T4 in the M10 layer of the medulla and T5 in the Lo1 layer of the lobula (Shinomiya, 2019).

The dendritic arbors of T4 cells receive parallel inputs from multiple columns, and a single arbor receives inputs from columns that signal different positions of the visual field, depending on the cell types of the input neurons. Recent developments in techniques for three-dimensional electron microscopy (3D-EM) have accelerated the identification of neurons and their synaptic circuits, or their connectome, in the brain of the fruit fly Drosophila melanogaster. In the visual system, motion detection pathways in the optic lobe have been a prominent goal for such connectomic approaches, which identify the component neurons using 3D-EM reconstructions of their arbors (Shinomiya, 2019).

The medulla dendritic arbors of T4 cells provide a substrate for the elementary motion detector (EMD) in the ON-edge motion pathway. Using serial-section transmission EM (ssTEM), Mi1 and Tm3 as major inputs to the T4 cell dendrites. A later approach using focused ion beam scanning EM (FIB-SEM) comprehensively revealed other medulla neurons providing inputs to T4. These medulla neurons relay input to T4 from L1, the first of two repeated neuron classes in the first neuropil, or lamina; L1 in turn receives input from the terminals of photoreceptors R1-R6 in the overlying compound eye (Shinomiya, 2019).

Complementary to the T4 cells, narrow-field T5 cells constitute the first output stage of the OFF-edge pathway, and some of T5's input neurons have also been identified from their terminals reconstructed using ssTEM. These inputs relay signals from L2 cells, which partner L1 in all columns, or cartridges, of the lamina and which also receive input from R1-R6. Therefore, the separation between the ON and OFF motion pathways is already established at the level of the lamina neurons (Shinomiya, 2019).

Finally, T4 and T5 cell axons transfer motion information to the fourth neuropil, or lobula plate, where it is integrated and further processed to extract specific motion modalities, before being conducted to the central brain by visual projection neurons (VPNs). VPNs include various types of lobula plate tangential neurons (LPTCs) and lobula plate/lobula columnar cells (Shinomiya, 2019).

The ON and OFF motion pathways are similar in their function, component neurons, and patterns of synaptic connections. Both T4 and T5 cells are direction-selective neurons, and each is further grouped into four subtypes: T4 as T4a, T4b, T4c and T4d; and T5 as T5a, T5b, T5c, and T5d. These T4 and T5 cells specifically signal motion in the four canonical directions. The subtypes a-d detect front-to-back, back-to-front, upward, and downward motion, respectively. Each subtype projects its axon to one of the lobula plate's four strata, depending on the direction of motion that it signals. Developmentally, both T4 and T5 are known to originate from the same subset of progenitor cells in the inner proliferation center and to express a proneural gene, Atonal, uniformly (Shinomiya, 2019).

Given the dimensional constraints of the respective ssTEM and FIB-SEM datasets, however, the T4 and T5 pathways, and their respective input neurons, have been reconstructed independently in separate reports using 3D-EM methods. Series of ultrathin sections have been used to identify medulla cell inputs to T4 cells; these included medulla intrinsic (Mi) and transmedulla (Tm) cells but not their terminals in the lobula, which were lacking from the EM dataset. Similarly, inputs to T5 terminals in the lobula arise from Tm cells, but the medulla arbors of these were also lacking from previous reconstructions. Subsequent reports that repeated the analysis of cells for seven medulla columns, using FIB-SEM, also failed to identify the lobula, but comprehensively identified additional inputs to, and connections between, T4 cells. Consequently, results from these studies cannot be compared directly to the same field size and at the same resolution in a single dataset. This makes it difficult to recognize and resolve deep similarities in the inputs to both pathways, which might support further evolutionary comparisons between those inputs, and which might also enable functional comparisons, especially for the inputs to T5 which to date are known only for four main Tm cells: Tm1, Tm2, Tm4, and Tm9 (Shinomiya, 2019).

This study exhaustively identified the synaptic inputs to T5 cells and described their spatial layouts. The anatomical properties of the dendritic terminals of T4 and T5 were also assessed, after identifying all neurons that have synaptic contacts with the motion-sensing output cells in the medulla and lobula. As a result, this report concludes the connectomic analysis of both the ON- and the OFF- motion-sensitive pathways in Drosophila (Shinomiya, 2019).

Classical correlation models of the motion detection circuit, including the Hassenstein and Reichardt (1956) and Barlow and Levick (1965) models, consider only two independent upstream inputs in the detection of motion. Several studies have provided physiological evidence that the elementary motion detector EMD circuit may be approximated by either of these models or their modified versions. These models cannot sufficiently address the asymmetrical responses of the T4 and T5 pathways, however, because the types and numbers of the neurons involved are limited, and are indeed not consistent with the findings that the input neurons to both T4 and T5 dendrites are clustered into three groups, not two. Previous studies, in particular, failed to include inhibitory inputs from CT1 to the T4 and T5 dendritic arbors. Although CT1 differs from the other medulla neurons providing inputs to T4 or T5 insofar as it lacks a direct synaptic partnership with lamina cells, it still receives indirect inputs from those cells via Mi1 and Mi9 (in M10) and Tm1 and Tm9 (in Lo1). CT1 is the only inhibitory columnar input to the T5 dendrites, and also the only element that is displaced from the other two excitatory legs. In the ON-edge side, CT1, together with the other two GABAergic neurons, C3 and Mi4, provides a measurable input to the base of the T4 dendrites (Shinomiya, 2019).

As a foretaste to human anatomy, a new EMD circuit model with three parallel inputs was recently proposed, based on the two classical motion detection models, on computer simulations, and on activity recordings from T4 and T5 cells. Inputs to the motion-detecting unit include a non-delayed direct input, delayed enhancer input, and a delayed suppressor input (null direction suppression) located on the side opposite to the enhancer signal input, the two inputs bracketing the direct input. The direction from the enhancer signal input to the suppressor signal input corresponds to the preferred direction (PD), and is opposite to the non-preferred direction (ND). This three-way input model incorporates the classical two-inputs models, the Hassenstein and Reichardt (H-R) and Barlow and Levick (B-L) models, as its subsets, so that the outputs, as well as the temporal tuning patterns of the circuits, are still consistent with the previous physiological studies. Applying this model to T4: a) Mi1 and Tm3, which provide inputs to the center of the dendritic arbor, would be direct inputs; b) Mi9 innervating the tip would be an enhancer input; and c) CT1, Mi4, and C3, which innervate the base, would be suppressor inputs. In the case of T5: a) Tm1, Tm2, and Tm4 would be direct inputs; b) Tm9 would be an enhancer, and; c) CT1 would be a suppressor input. Although Tm9 innervates the tip of T5 dendrites and therefore would fall into an enhancer location here, silencing experiments suggested that the contribution of Tm9 to the dark-edge detection was even larger than that of Tm1 and Tm2 combined, so it might not be reasonable to regard Tm9 as an input that simply enhances the main direct inputs (Shinomiya, 2019).

The motion circuit neurons activated by signals from ON-edge motion in either the preferred (A), or non-preferred (B) directions, and by OFF-edge motion in either the preferred (C), or non-preferred. Among these inputs to the T-cells, CT1, C3, and Mi4 are known to be GABA-positive, Mi9 is glutamate-positive, and all other cells, including T4 and T5, are positive for cholinergic reagents. As all neurons providing inputs to the base are putatively GABAergic, and all neurons to the center putatively cholinergic, the connections of the ON- and OFF-edge pathways partially match the three-way EMD circuit model (Shinomiya, 2019).

Both Mi9 and Tm9 cells relay signals from L3, and send inputs to the tip of the T4/T5 dendritic arbors. They both produce slow and sustained responses that serve as low-pass filters. Although Tm9 is putatively cholinergic and provides excitatory signals to T5, putatively glutamatergic signals from Mi9 to T4 are supposed to be inhibitory on the basis of behavioral response assays. This possibility is also suggested by the observation that T4 expresses a glutamate-gated chloride channel, GluClα, which mediates inhibitory signals. Tm9 shows an increased response during OFF stimulation, and Mi9 is also thought to be activated similarly but provides input to the T4 pathway. These neurons may therefore modulate T4 and T5 using independent but opposing respective mechanisms. Mi9 may function as a temporal low-pass filter of the OFF signal and may cancel noise during an OFF stimulus by suppressing T4, whereas Tm9 could enhance OFF signals to T5 along with the direct inputs from Tm1 and Tm2 (see Schematic diagram of inputs onto and outputs from T4 and T5 dendrites). Besides Mi9 and Tm9, the tips of the T4 and T5 dendrites also receive excitatory inputs from T4 and T5 of the same cell type, a-d, which could also enhance signals to the EMD circuits by themselves, although these inputs are fewer in number and presumably would be far weaker than those from Mi9 or Tm9 (Shinomiya, 2019).

CT1, which innervates the base of T4 and T5 dendrites, is an interesting wide-field cell that receives signals from the lamina cell pathways indirectly via other medulla neurons, including Mi1, Mi9, Tm1, and Tm9. The other inputs to the base of T4, Mi4 and C3, likewise lack direct inputs from lamina cells, suggesting that inhibitory signals from CT1, Mi4, and C3 are delayed by an additional synapse relative to those from the direct inputs (Shinomiya, 2019).

The connectivity diagrams are summarized of ON- and OFF-edge EMD circuits that have now been demonstrated anatomically (see Schematic diagram of inputs onto and outputs from T4 and T5 dendrites). A schematized and simplified EMD circuit diagram summarizes three downstream pathways from the photoreceptors (R1-R6). Among the lamina cells, L1 signals to ON pathways selectively, whereas L2 and L3 both signal to OFF pathways. None of the inputs to the base of T4 and T5, that is from CT1, Mi4, and C3, receives direct inputs from lamina interneurons. CT1 terminals in M10 receive only indirect input from L1 and L3, via Mi1 and Mi9, whereas those in Lo1 receive information from L2 and L3 only via Tm1 and Tm9. Mi4 also receives inputs from L1 and L3, through L5 and Mi9. The transfer of information to these neurons may be delayed at additional synaptic relays. Mi4, Mi9, and Tm9 themselves show delayed and sustained calcium responses against white-noise stimuli when compared with the responses of Mi1, Tm3, Tm1, Tm2, and Tm4, suggesting that the responses of Mi4, Mi9, and Tm9 serve as delayed arms in the EMD model. The response properties of CT1 are still unknown. It is suggested that the anatomical pathways of the two EMD circuits have now been reported in sufficient detail in this and previous accounts, but their physiological correlates, including the neurotransmitters and receptors of some constituent neurons, as well as temporal delays at each cell, all still need further analysis to complete the picture of how the T-cells signal motion information (Shinomiya, 2019).

On the basis of the described neuronal connectivity, speculative timing diagrams against ON- and OFF-edge signals to motion in the preferred and non-preferred directions are also shown. Among the lamina cells, only L1 activates the downstream cells during ON-edge signals. The direct inputs (Mi1 and Tm3) and suppressor inputs (CT1 and Mi4) to the T4 dendrites may therefore contribute to detecting ON-edge signals at the level of the EMD circuit. During OFF-edge signals, on the other hand, all three input legs to T5 as well as Mi9, which provides inhibitory inputs to T4, are activated. In both ON- and OFF-edge circuits, excitatory inputs provide signals to T4 or T5 cells first, before delayed inhibitory inputs suppress the responses of these cells to stimuli in the preferred direction. For signals in the non-preferred direction, T4 and T5 cells are inhibited by the suppressor inputs and will not be excited. Besides these, T4 is also likely to be suppressed by Mi9 not during ON-edge signals but during OFF-edge signals, presumably cutting off spontaneous noise from responses to non-preferred stimuli. Such a mechanism is lacking in the T5 pathway (Shinomiya, 2019).

Inputs to T4 and T5 from their upstream neurons reveal significant anatomical similarities between the ON- and OFF-edge EMD circuits. The Mi1 cell in the ON pathway, for example, is similar to the Tm1 and Tm2 cells in the OFF pathway, insofar as all these cells provide inputs to the center of the T4/T5 cell dendrites and use acetylcholine as their neurotransmitter, and differ only in the neuropil of their termination. Like T4, T5 expresses transcripts of two different nicotinic cholinoceptors, as well as those of an A-type muscarinic cholinoceptor, suggesting that T5 receives cholinergic Tm inputs by means of both ionotropic and metabotropic cholinoceptors. Additional neurons, CT1 and TmY15, innervate both M10 and Lo1 and form synapses there with T4 and T5, respectively. On the other hand, three types of GABA-positive neurons provide inputs to the base of T4 dendrites, whereas only one type of GABAergic neuron (CT1) sends inputs to the base of T5. Inputs from CT1 make up a much larger proportion of the total input to T5 cells than to T4 cells. This difference might compensate for the lack of other inhibitory inputs to the base of T5. There are also two tangential elements in Lo1 (LT33 and Tm23) that make synapses with T5 dendrites, and counterparts are not found in M10. These then constitute differences betweeen the inputs to T5 and T4 cells. As the cell body site and projection trajectory of LT33 are both similar to those of CT1, a GABAergic cell, and as the cell is both presynaptic and postsynaptic to the T5 cell dendrites, it is possible that LT33 is also inhibitory and inhibits the activity of T5 regardless of the direction specificity. Even so, the T4 and T5 motion stimulus responses are very similar (Haag et al., 2017) (Shinomiya, 2019).

The T4 and T5 cells not only share functional characteristics, but also correlate closely in their development and are suggested evolutionary siblings. T4 and T5 are produced from the same lobula plate neuroblasts, and the expression of Notch specifies the generation of these two morphologically similar cell types. Assuming that, during the course of evolution, T4 and T5 arose as duplicates from an ancestral cell population, as has been proposed, the neurons that provide their synaptic partnerships, most notably Mi and Tm cells, could also have been duplicated, possibly in an event that was induced by the duplication of T4/T5 (Shinomiya, 2019).

T4 and T5, or morphologically similar optic lobe neurons, have been found in a broad range of arthropod species, including various Diptera, the honeybee, butterfly, and crab, suggesting that the origin of these cells can be traced back to the Cambrian, when pancrustacean ancestors are thought to have given rise to hexapod and crustacean species. Anatomical and functional differences in the T4 and T5 pathways of the fly's brain may therefore have accumulated during the course of evolution, adapting ancestral forms to their living environments, by changes in the synaptic connections of their partner neurons (Shinomiya, 2019).

The two novel cells described in this report, CT1 and TmY15, share important similarities: both are putatively inhibitory, and both provide input to both T4 and T5. They differ chiefly in their anatomical field size. TmY15 is narrow-field, spanning at least 10 columns, whereas CT1 spans the entire field. The architecture of the connecting networks also differs. Thus, both are anatomically qualified to provide an inhibitory surround to the field of T-cells that they innervate (Shinomiya, 2019).

In addition to its similarity to TmY15, CT1 may support a local computation of the inhibitory elements of a Barlow-Levick circuit. Calculations of the space constant for the CT1 arbor suggest that a delay between adjacent columns is sufficient to allow local inhibition, in addition to any global inhibition that this cell may mediate. Such local computation has both the right sign and location (on the leading edge of the anti-preferred direction) in both the ON-pathway of T4 in the medulla, where CT1 is excited by Mi1 and Tm3, and the OFF-pathway of T5 in the lobula, where the inputs are instead Tm1 and Tm9. The latter, in turn, derive their inputs from L1 (to T4) and L2 (to T5) (Shinomiya, 2019).

The functional significance of TmY15 in motion processing must remain speculative. Not only does this cell receive a wide range of inputs from various types of cells in the medulla, lobula, and lobula plate, but it also has much weaker synaptic contacts with T4 and T5 in M10 and Lo1 when compared with other Mi or Tm cell inputs. It specifically innervates the second and third strata (Lop2 and Lop3) in the lobula plate, and preliminary observations show that it receives inputs from cells T4b, T4c, T5b, and T5c, suggesting that TmY15 may also work as a feedback loop to suppress responses in T4 and T5 during regressive and upward motions (Shinomiya, 2019).

In summary, this report comprehensively identifies input and output neurons of the dendritic arbors of T4 and T5 cells, and uses a single dataset to reveal (at synapse level) the detailed similarities between the connections of these two motion-signaling output cells. Together with the functional contribution of individual neurons in the motion-detection circuits shown in several studies, the detailed connectivity diagram that this study provides should further facilitate functional analyses in these cells, through behavioral assays, calcium imaging, and electrophysiological recordings, and by providing comparisons with known neurons in the ON-pathway (Shinomiya, 2019).

Neurogenesis is achieved through a sequence of steps that include specification and differentiation of progenitors into mature neurons. Frequently, precursors migrate to distinct positions before terminal differentiation. The Slit-Robo pathway, formed by the secreted ligand Slit and its membrane bound receptor Robo, was first discovered as a regulator of axonal growth. However, today, it is accepted that this pathway can regulate different cellular processes even outside the nervous system. This study describes the participation of the Slit-Robo pathway in the development of motion sensitive neurons of the Drosophila visual system. Slit and Robo receptors are expressed in different stages during the neurogenesis of motion sensitive neurons. Furthermore, it was found that Slit and Robo regulate multiple aspects of their development including neuronal precursor migration, cell segregation between neural stem cells and daughter cells and formation of their connectivity pattern. Specifically, loss of function of slit or robo receptors in differentiated motion sensitive neurons impairs dendritic targeting, while knocking down robo receptors in migratory progenitors or neural stem cells leads to structural defects in the adult optic lobe neuropil, caused by migration and cell segregation defects during larval development. Thus, this work reveals the co-option of the Slit-Robo signaling pathway in distinct developmental stages of a neural lineage (Guzman-Palma, 2021).

Artificial neural networks trained to solve sensory tasks can develop statistical representations that match those in biological circuits. However, it remains unclear whether they can reproduce properties of individual neurons. This study investigated how artificial networks predict individual neuron properties in the visual motion circuits of the fruit fly Drosophila. Anatomically constrained networks were trained to predict movement in natural scenes, solving the same inference problem as fly motion detectors. Units in the artificial networks adopted many properties of analogous individual neurons, even though they were not explicitly trained to match these properties. Among these properties was the split into ON and OFF motion detectors, which is not predicted by classical motion detection models. The match between model and neurons was closest when models were trained to be robust to noise. These results demonstrate how anatomical, task, and noise constraints can explain properties of individual neurons in a small neural network (Mano, 2021).

Both vertebrates and invertebrates perceive illusory motion, known as "reverse-phi," in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. This study shows that each of Drosophila's elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges (Salazar-Gatzimas, 2018).

The histamine recycling pathway in photoreceptors faces two major physiological demands. First, histamine is released at high rates, which if unopposed would deplete the eye within seconds. Second, the demands on histamine recycling vary greatly from moment to moment, at least within the time frame of 100 ms, depending on the light stimulus conditions that result from the fly's own activity and changes in its ambient light conditions. To maintain a constant supply of histamine may therefore require not only a fast recycling pathway via carcinine but also storage sites for the neurotransmitter, as well as β-alanine and their conjugate carcinine. These storage sites seem to be the marginal and fenestrated glia and, in the retina, the pigment cells. The fenestrated glia have already been recognized as such a site, and three interrelated candidate functions, recycling, spillover and reserve, have been identified. It is imagined that a rapid reuptake pathway is processed via epithelial glia and their capitate projections in the lamina. The additional storage sites in the ommatidium and cartridge are possibly responsible for the slower supply of histamine to photoreceptors for re-release, the supply of β-alanine for synthesis of carcinine in the epithelial and proximal satellite glia, or the return of carcinine to the photoreceptor for the liberation of both (Borycz, 2012).

This study reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow study of variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. This study addresses the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, <1% of contacts are not part of a consensus circuit, and those contacts that supplement (E+) or are missing from it (E-) were classified. Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form >/=20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics (Takemura, 2015).

Columns are structural and functional units of the brain. However, the mechanism of column formation remains unclear. The medulla of the fly visual center shares features with the mammalian cerebral cortex, such as columnar and layered structures, and provides a good opportunity to study the mechanisms of column formation. Column formation is initiated by three core neurons in the medulla, namely, Mi1, R8, and R7. The proper orientation of neurons is required for the orientation and arrangement of multiple columns. Their orientations may be under the control of planar cell polarity (PCP) signaling, because it is known to regulate the orientation of cells in two-dimensional tissue structures. This study demonstrates that the ligands DWnt4 and DWnt10 expressed specifically in the ventral medulla and dorsal medulla, respectively, globally regulate the columnar arrangement and orientation of Mi1 and R8 terminals through Fz2/PCP signaling in a three-dimensional space (Han, 2020).

Optogenetic channels and ion pumps have become indispensable tools in neuroscience to manipulate neuronal activity and thus to establish synaptic connectivity and behavioral causality. Inhibitory channels are particularly advantageous to explore signal processing in neural circuits since they permit the functional removal of selected neurons on a trial-by-trial basis. However, applying these tools to study the visual system poses a considerable challenge because the illumination required for their activation usually also stimulates photoreceptors substantially, precluding the simultaneous probing of visual responses. This study explored the utility of the recently discovered anion channelrhodopsins GtACR1 and GtACR2 for application in the visual system of Drosophila. First, their properties were characterized using a larval crawling assay. Further whole-cell recordings were obtained from cells expressing GtACR1, which mediated strong and light-sensitive photocurrents. Tangential cells of the lobula plate lend themselves well for this purpose since whole-cell patch-clamp recordings can be readily obtained from their large cell bodies. Finally, using physiological recordings and a behavioral readout, it was demonstrated that GtACR1 enables the fast and reversible silencing of genetically targeted neurons within circuits engaged in visual processing (Mauss, 2017).

Estimating motion is a fundamental task for the visual system of sighted animals. In the Drosophila optic lobe, direction-selective T4 and T5 cells respond to moving brightness increments (ON) and decrements (OFF), respectively. Current algorithmic models of the circuit are based on the interaction of two differentially filtered signals. However, electron microscopy studies have shown that T5 cells receive their major input from four classes of neurons: Tm1, Tm2, Tm4, and Tm9. Using two-photon calcium imaging, this study demonstrates that T5 is the first direction-selective stage within the OFF pathway. The four cells provide an array of spatiotemporal filters to T5. Silencing their synaptic output in various combinations, it was found that all input elements are involved in OFF motion detection to varying degrees. This comprehensive survey challenges the simplified view of how neural systems compute the direction of motion and suggests that an intricate interplay of many signals results in direction selectivity (Serbe, 2016).

Previous studies have identified that visual and olfactory associative memories of Drosophila share the mushroom body (MB) circuit. Despite well-characterized odor representations in the Drosophila MB, the MB circuit for visual information is totally unknown. This study shows that a small subset of MB Kenyon cells (KCs) selectively responds to visual but not olfactory stimulation. The dendrites of these atypical KCs form a ventral accessory calyx (vAC), distinct from the main calyx that receives olfactory input. Two types of visual projection neurons (VPNs) directly connecting the optic lobes and the vAC were identified. Strikingly, these VPNs are differentially required for visual memories of color and brightness. The segregation of visual and olfactory domains in the MB allows independent processing of distinct sensory memories and may be a conserved form of sensory representations among insects (Vogt, 2016).

The patterns of neuronal connectivity underlying multisensory integration, a fundamental property of many brains, remain poorly characterized. The Drosophila melanogaster mushroom body-an associative center-is an ideal system to investigate how different sensory channels converge in higher order brain centers. The neurons connecting the mushroom body to the olfactory system have been described in great detail, but input from other sensory systems remains poorly defined. This study used a range of anatomical and genetic techniques to identify two types of input neurons that connect visual processing centers-the lobula and the posterior lateral protocerebrum-to the dorsal accessory calyx of the mushroom body. Together with previous work that described a pathway conveying visual information from the medulla to the ventral accessory calyx of the mushroom body, this study defines a second, parallel pathway that is anatomically poised to convey information from the visual system to the dorsal accessory calyx (Li, 2020).

Sensory systems use different strategies to detect specific physical features of the outside world. For instance, the olfactory system contains many different types of sensory neuron that are each specialized in detecting a specific class of volatile chemicals. Through only two neuronal layers, olfactory information-the identity of an odor and its concentration-is relayed to higher brain centers. In contrast, the visual system contains far fewer types of sensory neuron, but through numerous neuronal layers, it relays a range of highly processed information-for instance, color, brightness, motion, and shape-to higher brain centers. Thus, higher brain centers have to integrate different types of processed information, bind that information into a coherent representation of the outside world, and use such representations to guide behavior. How higher brain centers achieve this feat remains largely unknown. This gap in knowledge mainly stems from the fact that higher brain centers are formed by a large number of neurons and that the projection neurons conveying information from different sensory systems to these centers often remain poorly characterized. This makes it difficult to understand whether there are specific patterns of neuronal connectivity that enable multisensory integration and what the nature of these patterns are. Deciphering the fundamental neuronal mechanisms that underlie multisensory integration requires a model system such as the Drosophila melanogaster mushroom body, which consists of a relatively small number of neurons whose connections can be charted reliably (Li, 2020).

The Drosophila mushroom body is formed by ∼2,000 neurons-called the Kenyon cells-and has long been studied for its essential role in the formation of olfactory associative memories. The identity of the projection neurons that connect the olfactory system to the mushroom body-and the way Kenyon cells integrate input from these neurons-has been characterized in great detail, highlighting fundamental connectivity patterns that enable this higher brain center to represent olfactory information efficiently. Evidence in Drosophila melanogaster shows that the mushroom body is more than an olfactory center, as it is also required for the formation of visual and gustatory associative memories. However, the identity of the neurons that connect the mushroom body to other sensory systems remains poorly characterized. Thus, a first step toward understanding how the mushroom body integrates multisensory information is to identify such non-olfactory mushroom body input neurons and the genetic tools necessary to manipulate these neurons (Li, 2020).

The mushroom body receives its input through its calyx and sends its output through its lobes. The calyx-a morphologically distinct neuropil containing the synapses formed between projection neurons and Kenyon cells-can be divided into four, non-overlapping regions: one main calyx as well as three accessory calyces named the dorsal, lateral, and ventral accessory calyces. The five output lobes-the α, α', β, β', and γ lobes-contain the synapses formed between Kenyon cells, mushroom body output neurons, and dopaminergic neurons. With respect to these input and output regions, Kenyon cells can be divided into seven distinct types. Of these seven types, five types-the α/βc, α/βs, α'/β'ap, α'/β'm, and γmain Kenyon cells-extend their dendrites only into the main calyx and their axons along one or two lobes. Most of the neurons that project to the main calyx emerge from the antennal lobe, the primary olfactory center in the Drosophila brain. Thus, α/βc, α/βs, α'/β'ap, α'/β'm, and γmain Kenyon cells receive input primarily from the olfactory system (Li, 2020).

In contrast, the two other classes of Kenyon cells do not extend their dendrites into the main calyx. Instead, the α/βp Kenyon cells extend their dendrites into the dorsal accessory calyx-avoiding completely the main, lateral, and ventral accessory calyces-and their axons along the α and β lobes. Likewise, the γd Kenyon cells extend their dendrites exclusively into the ventral accessory calyx and their axons along the γ lobe. Thus, both the α/βp and γd Kenyon cells are anatomically poised to receive non-olfactory input. There is evidence suggesting that the ventral accessory calyx receives input from the medulla, a region of the optic lobe that specializes in processing brightness and color. Furthermore, a recent study suggests that the dorsal accessory calyx is a multisensory center that integrates input from multiple sensory pathways, including the olfactory, gustatory, and visual systems (Li, 2020).

This study reports a strategy that uses a combination of genetic tools-including transgenic lines that drive expression in few neurons and a photo-labeling technique used to identify individual neurons and their pre-synaptic partners-to characterize the input neurons of the α/βp Kenyon cells. Two types of mushroom body input neuron were identified in that, together, form about half of the total input the α/βp Kenyon cells receive in the dorsal accessory calyx. The first neuronal type-henceforth referred to as LOPNs-consists of a neuron that projects from the lobula, a region of the optic lobe specialized in detecting visual features, such as shape and motion. The second type of neuron-henceforth referred to as PLPPNs-consists of projection neurons that emerge from the posterior lateral protocerebrum, a brain region that receives input from the optic lobe. Interestingly, LOPN and PLPPNs do not project to the ventral accessory calyx and do not connect to the γd Kenyon cells. Based on these findings, it is concluded that there are two parallel pathways that convey visual information to the mushroom body: a pathway projecting from the medulla to the γd Kenyon cells and another pathway projecting from the lobula and posterior lateral protocerebrum to the α/βp Kenyon cells (Li, 2020).

This study has identified and characterized neurons projecting to the dorsal accessory calyx of the mushroom body and show that these neurons are pre-synaptic to the α/β_p Kenyon cells. Using a combination of genetic and anatomical techniques, it was possible to distinguish two different types of projection neuron: _LOPN projecting from the lobula-an area of the optic lobe processing visual features, such as shape and motion-and the PLPPNs projecting from the posterior lateral protocerebrum. Although the posterior lateral protocerebrum remains poorly characterized in D. melanogaster, evidence from other insects shows that this brain region receives input from the optic lobe. Interestingly, it was found that the dendrites formed by the _PLPPNs in the posterior lateral protocerebrum are in close proximity to neurons that project from the ventral medulla. Based on these results-and considering insights from the connectome-it is estimated that LOPNs and PLPPNs account for half of total input that α/βp Kenyon cells receive in the dorsal accessory calyx. LOPNs and PLPPNs do not extend axonal terminals into the ventral accessory calyx, the other calyx known to receive visual input, but rather extend axonal terminals into the dorsal accessory calyx and into the superior lateral protocerebrum. Likewise, the α/βp Kenyon cells do not connect to the visual projection neurons that are associated with the ventral accessory calyx. These findings suggest that the visual system is connected to the mushroom body via two parallel pathways: the α/βp Kenyon cells receive input from the lobula and the posterior lateral protocerebrum, whereas the γd Kenyon cells receive input directly from the medulla. Further functional studies are necessary to determine what kind of visual information is processed by the α/βp Kenyon cells (Li, 2020).

In Drosophila melanogaster, the mushroom body has long been studied as an olfactory processing center. However, evidence from many insects, including the honeybee Apis mellifera, shows that the mushroom body integrates sensory information across different modalities. In honeybees, the input region of the mushroom body, also called the calyx, is divided into different layers, and each layer receives input from either the olfactory or visual system. Because the dendrites of Kenyon cells are also restricted to specific layers, it has been suggested that, in the honeybee, multisensory integration does not occur at the level of individual Kenyon cells but rather at the population level. Although the honeybee mushroom body differs greatly from the Drosophila mushroom body-it contains about a hundred times as many Kenyon cells and its input region is divided in multiple complex layers-it appears that both mushroom bodies share a common fundamental connectivity principle: the segregation of input based on sensory modality. This connectivity mechanism is immediately apparent in the structural organization of the Drosophila melanogaster mushroom body: the Kenyon cells receiving input from the olfactory system all extend their dendrites into the main calyx, whereas the Kenyon cells receiving input from the visual system extend their dendrites either in the dorsal accessory calyx or the ventral accessory calyx. Many studies have demonstrated that the Kenyon cells that process olfactory information-those associated with the main calyx-integrate input broadly across the different types of olfactory projection neuron. Interestingly, it appears that the Kenyon cells that process visual information are wired differently (Li, 2020).

A thorough understanding is available of how olfactory Kenyon cells integrate input from the antennal lobe: most Kenyon cells receive, on average, input from seven projection neurons, and the projection neurons connecting to the same Kenyon cell share no apparent common features. Theoretical studies have shown that this random-like connectivity pattern enables the mushroom body to form sparse and decorrelated odor representations and thus maximizes learning. Randomization of sensory input is a connectivity pattern that is well suited for representing olfactory information-as an odor is encoded based on the ensemble of olfactory receptors it activates-and might not be suitable for representing visual information. Indeed, the results of this study suggest that specific visual features-the signals processed by the medulla and the ones processed by the lobula and the posterior lateral protocerebrum-need to be represented by two separate subpopulations of Kenyon cells. This observation mirrors anatomical studies of the honeybee brain: the neurons projecting from the lobula terminate in a different layer than the neurons projecting from the medulla. This arrangement might be essential to preserve distinct visual features when forming associative memories. Functional and behavioral studies are required to determine whether indeed the mushroom body represents multisensory stimuli in this manner (Li, 2020).

A mechanistic understanding of neural computation requires determining how information is processed as it passes through neurons and across synapses. However, it has been challenging to measure membrane potential changes in axons and dendrites in vivo. This study used in vivo, two-photon imaging of novel genetically encoded voltage indicators, as well as calcium imaging, to measure sensory stimulus-evoked signals in the Drosophila visual system with subcellular resolution. Across synapses, major transformations were found in the kinetics, amplitude, and sign of voltage responses to light. Distinct relationships were described between voltage and calcium signals in different neuronal compartments, a substrate for local computation. Finally, it was demonstrated that ON and OFF selectivity, a key feature of visual processing across species, emerges through the transformation of membrane potential into intracellular calcium concentration. By imaging voltage and calcium signals to map information flow with subcellular resolution, this study illuminates where and how critical computations arise (Yang, 2016).

Visual projection neurons (VPNs) provide an anatomical connection between early visual processing and higher brain regions. This study characterized lobula columnar (LC) cells, a class of Drosophila VPNs that project to distinct central brain structures called optic glomeruli. This study anatomically describes 22 different LC types and show that, for several types, optogenetic activation in freely moving flies evokes specific behaviors. The activation phenotypes of two LC types closely resemble natural avoidance behaviors triggered by a visual loom. In vivo two-photon calcium imaging reveals that these LC types respond to looming stimuli, while another type does not, but instead responds to the motion of a small object. Activation of LC neurons on only one side of the brain can result in attractive or aversive turning behaviors depending on the cell type. These results indicate that LC neurons convey information on the presence and location of visual features relevant for specific behaviors (Wu, 2016).

This report presents anatomical and functional studies of lobula columnar (LC) cells, prominent visual projection neurons from the lobula to target regions in the central brain called optic glomeruli. Comprehensive anatomical analyses of the dendritic arbors and central brain projections of LC neurons support the notion that these cells encode diverse visual stimuli, distinct for each LC cell type, and convey this information to cell-type specific downstream circuits. Precise genetic tools that target individual LC cell types allowed exploration of the behavioral consequences of optogenetic activation of these cell types. Activating cells of single LC neuron types was often sufficient to evoke a range of coordinated behaviors in freely behaving flies. Using two-photon calcium imaging from head-fixed flies, two LC cell types with activation phenotypes similar to avoidance responses, were shown to selectively encode visual looming, a stimulus that also evokes similar avoidance behaviors, while a third cell type responded strongly to a small moving object. These results suggest that LC cell types encode visual features that are relevant for specific behaviors. Activation of LC cells in only one brain hemisphere can result in either an attractive or repulsive directional turning response, depending on cell type. Thus which LC neuron channel is activated determines the valence of the behavior, whereas comparison across the brain by two such channels of the same type provides information about the location of relevant visual features (Wu, 2016).

Anatomical properties of LC neurons have been previously described both in Drosophila and other Diptera. This work extends these studies by providing a comprehensive description of LC neurons in Drosophila, including the identification of several previously unreported cell types. Further, these anatomical analyses with were combined the generation of highly specific genetic markers (split-GAL4 lines) for each cell type. Each of the 22 LC types described has morphologically distinct dendritic arbors in the lobula with stereotyped arbor stratification, size and shape. As observed in the medulla, where synapse-level connectomics data are available for many cell types, different layer patterns and arbor shapes are likely to reflect differences in synaptic connectivity and neuronal computation. Arbors of LC neurons are found in all lobula strata, though with large differences between layers. Only LC4 (and perhaps LPLC1 and LPLC2) cells are potentially postsynaptic to neurons in the most distal lobula layer, Lo1, while other strata such as Lo4 and Lo5B include processes of more than half of the LC types. The presence of at least some LC dendrites in each lobula layer implies that all of the about 50 different interneuron types that convey visual information from the medulla, and to a lesser extent from the lobula plate, to the lobula, are potentially presynaptic to some LC cells, although a far smaller number is likely presynaptic to any single LC cell type. The predicted differences in the synaptic inputs to different LC cell types also suggest that they will differ in their responses to visual stimuli. Thus, individual LC neuron types are expected to encode specific visual stimuli, while the population of all LC cell types together should signal a wide range of behaviorally relevant visual features (Wu, 2016).

The visual responses of several LC cell types measured using two-photon calcium imaging support the expectation that different types selectively respond to different visual features. The three LC neuron types examined preferentially responded to distinct stimuli, with either a dark looming stimulus (LC6 and LC16) or a small moving object (LC11) evoking the strongest measured responses. LC6 and LC16 showed stronger responses to a dark expanding disc than to related stimuli such as an expanding bright disk or a darkening stimulus that lacks the expanding motion. The reduction in the LC6 and LC16 responses when the edge motion is removed from the stimulus is precisely what is expected of loom-sensitive neurons and is reminiscent of behavioral studies in houseflies showing that darkening contrast combined with edge motion is the most effective stimulus for triggering takeoffs. Consistent with their similar responses in the imaging experiments, LC6 and LC16 have very similar lobula layer patterns while LC11 has a different arbor stratification indicating that LC11 receives inputs from a different set of medulla cell types than LC6 and LC16 (Wu, 2016).

It is likely that the selectivity for visual stimuli observed in LC neuron responses is both a property of the stimulus selectivity of their inputs-some selectivity was seen while imaging in the dendrites of a few LC cell types-and specific computations implemented by individual LC neuron types. In addition, cells post-synaptic to the LC cells may integrate the responses of several individual LC neurons of the same type to provide more robust detection of specific visual features. For example, while LC6 and LC16 cells as populations are strongly excited by dark looming stimuli, it is currently unknown whether individual LC6 and LC16 neurons, which have dendritic extents well below the maximum size of the looming stimuli, and also well below the size known to elicit maximal behavioral responses, show the same response properties. The anatomical data and genetic reagents provide a starting point for the additional functional and ultra-structural studies that will be required to elucidate the circuit mechanisms that produce the response properties of these and other LC cell types (Wu, 2016).

The suggestion that LC cells are feature-responsive neurons has been partly based on the apparent dramatic reduction in retinotopy between LC neuron dendrites, which have a retinotopic arrangement in the lobula, and their axons, which appear to discard this spatial information as they converge onto optic glomeruli, the cell-type specific target regions in the central brain. This study extended previous analyses of LC neuron arbor convergence by directly visualizing multiple single LC cells in a glomerulus in the same fly. These experiments revealed no detectable retinotopy of LC cell processes in most glomeruli even at this cellular level of resolution. It is possible that the responses of individual LC cells carry information about retinotopic position; given the comparatively small size of LC dendrites (the lateral spread of even the largest LC cells covers less than 20% of visual columns) and the retinotopic distribution of these dendrites in the lobula it would be surprising if they did not. Such retinotopic responses could for example be relevant for those LC cell types that appear to have presynaptic sites in the lobula and are thus likely to provide input to retinotopically organized circuits. However, with the caveat that synapse-level connectivity was not examined, for most LCs the available anatomical information appears to support the view that much retinotopic information is discarded at the glomerulus level. Consistent with this anatomical observation, the calcium imaging experiments from single LC cell types revealed visual responses to localized stimuli that could be measured throughout a cross-section of the glomerulus without clear retinotopic arrangement of the responding axons. Because of the columnar nature and apparently restricted visual field of the dendrites of LC neurons, the features computed by individual LC neurons are likely to be well defined in subregions of the eye, with perhaps downstream circuits required to integrate these locally-extracted features, as discussed above for looming. There is currently little insight into how these computations are initiated in the optic glomeruli and this remains an exciting area for future investigation (Wu, 2016).

Unlike the other LC neurons, it was found that LC10, and to a lesser extent LC9, cells retain some retinotopic information in the arrangement of their axon terminals indicating that the loss of retinotopy is not a necessary consequence of axonal convergence onto a glomerular target region. More specifically, it was observed that the order of LC10 axonal terminals in the anterior optic tubercle (AOTu) along the DV axis matches the sequence of AP positions of the corresponding dendrites in the lobula. This organization could facilitate synaptic interactions of LC10 cells corresponding to different azimuthal positions in the visual field with distinct target cells. Consistent with a possible general role of the AOTu in the processing or the relaying of retinotopic information, retinotopic responses have recently been observed in the dendrites of central complex neurons that, mainly based on work in other insects, are thought to be synaptic targets of output neurons of the lateral zone of the AOTu (Wu, 2016).

It was found that, independent of the presence or absence of retinotopy at the glomerulus level, positional information can be extracted from the differential activity of LC cells between the two optic lobes. It was directly demonstrated this capability by genetically restricting optogenetic LC neuron activation to only one optic lobe. This unilateral activation evoked directional turning responses relative to the activated brain side. Thus, LC neuron signaling appears to convey information on both different visual features and their location. This may further extend the similarities to the antennal lobes where differences in odorant receptor neuron activity between the left and right antennal lobes may contribute to odorant tracking (Wu, 2016).

Activation of different types of LC neurons can induce distinct behaviors including jumping, reaching, wing extension, forward walking, backward walking and turning. While specific activation phenotypes have been reported for a variety of cell types and behaviors, many of these studies have focused on command-like neurons thought to orchestrate specific motor programs. By contrast, the activation phenotypes reported in this study result from the optogenetic stimulation of different types of related visual projection neurons. A plausible interpretation of these results is that activation of LC neurons can mimic the presence of the visual features that these neurons normally respond to and thus elicits behavioral responses associated with these fictive stimuli. This possibility is supported by several lines of evidence from studies of LC6 and LC16. First, optogenetic depolarization of each of these cell types evokes a specific behavioral response-backward walking for LC16 and jumping for LC6-that resembles a similar natural avoidance or escape behavior. Second, backward walking and jumping can both also be elicited by presentation of a predator-mimicking visual loom and, third, in calcium imaging experiments both LC16 and LC6 showed a preferential response to a similar looming stimulus compared to a number of related stimuli. Although this study did not explore LC10 response properties, it is noted that LC10-activation phenotypes also show similarities to natural behaviors: movements resembling the directed foreleg extension displayed during activation-evoked reaching occur, for example, during gap-climbing behavior and in aggressive fly-fly interactions (Wu, 2016).

Overall, the LC neuron activation phenotypes that were observed suggest that the encoding of visual information at the level of LC neurons is sufficiently specialized to contribute to distinct behavioral responses in a cell-type dependent fashion. However, patterns of LC neuron activation that produce more refined fictive stimuli than were employed in the current work will be required to fully explore the LC neuron behavioral repertoire. Likewise, more comprehensive physiological studies of the response properties of the LC cell types will be needed (Wu, 2016).

How does LC cell activation evoke specific behavioral responses? In the simplest scenario, LC neuron depolarization could directly activate a single postsynaptic premotor descending interneuron that would then in turn trigger the observed behavior. This appears plausible in some cases: for example, activation of LC4 neurons (called ColA cells in larger flies) might evoke a jumping response via activation of the Giant Fiber (GF) cells, a pair of large descending neurons known to be postsynaptic to ColA and LC4 and which have a known role in escape behavior. For other LC cell types, there is currently no evidence suggesting a direct connection to descending neurons. For example, candidate descending neurons for the LC16 backward walking response, the moon-walker descending interneurons, do not have dendrites in or near the LC16 glomerulus. Responses to diverse visual stimuli, some of which may derive from LC neuron activity, have also been observed in higher order brain centers without direct connections to LC neurons such as the central complex (Wu, 2016).

The activation experiments also provide several indications that the signaling downstream of LC neurons is likely to be more complex; for example, activation of a single LC cell type can give rise to multiple behaviors such as reaching, wing extension and turning for LC10, or backward walking and turning for LC16. Changes of the spatial pattern of LC neuron activation, as in the stochastic labeling experiments of this study, can further modify activation phenotypes. For example, unilateral LC16 activation primarily evokes turning away from the location of LC16 activation, not backward walking, suggesting that the relative differences in LC16 activity between the two eyes can guide the direction of motor output through downstream signaling. Furthermore, several different LC neuron types may contribute to the same or similar behaviors, as suggested by the jumping phenotypes of LC4, LC6, LC15, LPLC1 and LPLC2. Presumably, visual signals and other information downstream of LC neurons are integrated to select appropriate behavioral actions. Such additional processing is also suggested by the cases of neurons with overlapping response properties but distinct activation phenotypes such as LC6 and LC16. It is also noted that some responses to LC neuron activation appear to be context dependent; for example, reduced forward walking was observed for several LC cell types on the platform of the single-fly assay that is much smaller than the arena used in the arena assay (Wu, 2016).

In addition, this study examined onoy the behavior of standing or walking flies and LC neuron signaling might have different consequences depending on the behavioral state. For example, looming stimuli can also elicit avoidance responses in flying flies, but these responses differ from the takeoff or retreat behaviors of walking animals. Therefore, while LC cell activity appears to convey visual information that is specialized for sets of related behavioral responses, LC neurons do not appear to instruct a single behavioral output (Wu, 2016).

The most common activation phenotypes observed in the screen were apparent avoidance responses. Furthermore, in addition to the LC cells examined in this study, other VPNs may also contribute to avoidance behaviors. This predominance of avoidance phenotypes is perhaps not unexpected. Since escape responses have to be fast and reliably executed under many different conditions, neurons that signal features that can evoke escape may be particularly likely to show phenotypes in an activation screen. Given the importance of predator avoidance for fly survival, it appears plausible that a considerable fraction of visual output neurons might be utilized for the detection of visual threats ranging from looming to small objects. Furthermore, it is likely that CsChrimson-mediated depolarization of an entire population of LC neurons is more similar to the pattern of neuronal activity induced by an imminent collision, and thus responses of many individual loom-sensitive neurons, so it is not surprising that the activation screen revealed at least two looming-sensitive neuron types (Wu, 2016).

The escape-inducing neurons that were identifed in this study could provide inputs to different escape response pathways, such as long- and short-mode escape, or act as multiple inputs to the same downstream circuits. Interestingly, neurons with avoidance-like activation phenotypes project to two separate groups of adjacent glomeruli, one in the dorsal Posterior Ventrolateral Protocerebrum (PVLP; LC6, LC16 and also LC15) and one more ventral and medial (LC4, LPLC1 and LPLC2), the latter two with dendrites in the lobula and lobula plate. This spatial organization may facilitate synaptic interactions of functionally related LC neuron types with common downstream pathways for a specific behavior. The second group is close to dendritic branches of the GF, large descending neurons required for short-mode responses in Drosophila and a postsynaptic partner of LC4/ColA and possibly also the two LPLC cell types. LC6 terminals do not overlap with GF dendrites and LC6 cells may play a role in the GF-independent escape pathways that have been proposed in both Drosophila and housefly. Parallel neuronal pathways involved in escape behaviors have been identified or postulated in both vertebrates and invertebrates, but a contribution of several identified visual projection neurons to such pathways, as suggested by the activation screen, has not been previously reported. Different visual output neurons with distinct tuning of their response properties to looming parameters such as speed, size, luminance change or edge detection might have evolved to ensure robust responses to avoid predators or collisions. It is, however, currently not known whether LPLC1, LPLC2, LC4 and LC15 are indeed sensitive to looming stimuli and if so, whether their response details differ from LC16, LC6 and each other. Nevertheless, the identification of these neurons opens the possibility to examine the potential contribution of several visual pathways to avoidance behaviors (Wu, 2016).

LC neurons are a subset of the about a hundred VPN cell types that relay the output of optic lobe circuits to targets in the central brain. These data strongly support existing proposals for LC cell types as feature-detecting neurons, which have been mainly based on the distinct anatomical properties of LC cells. While these anatomical features distinguish LC neurons from many other VPNs, an association of VPN pathways with specific behaviors is not unique to LC cell types. The notion that individual neuronal pathways are tuned for specific behavioral requirements is a prominent theme in invertebrate neuroethology, with these neurons described as 'matched filters' for behaviorally relevant features of the external world. A number of previously studied VPN pathways, outside of the LC subgroup, have been described as encoding specific behaviorally related visual stimuli. In particular, very similar to the current results for LC6 and LC16, a group of tangential cells of the lobula and lobula plate (Foma-1 neurons) were found to respond to looming visual stimuli and, upon optogenetic activation, trigger escape responses. And perhaps most famously, the long-studied lobula plate tangential cells (LPTCs), such as the HS and VS cells, integrate local motion signals so as to preferentially respond to global optic flow patterns that are remarkably similar to visual motion encountered during specific behavioral movements. These findings are consistent with the idea that, at the outputs of the fly visual system, VPN pathways are found whose encoding properties are already well matched to particular fly behaviors or groups of behaviors. Matching the response properties of these deep sensory circuits to behavioral needs may be a general evolutionary solution to the challenge of dealing with the complexity of the visual world with limited resources (Wu, 2016).

LC neurons have long been recognized as a potential entry point for the circuit-level study of visual responses outside of the canonical motion detection pathways. This study has provided a comprehensive anatomical description of LC cell types and genetic reagents to facilitate such further investigations. It was also shown that activation of several LC cell types results in avoidance behaviors and that some of these same LC types respond to stimuli that can elicit such behaviors. Other LC neurons appear to mediate attractive behavioral responses. This work provides a starting point for exploring the circuit mechanisms both upstream and downstream of LC neurons (Wu, 2016).

Many animals rely on vision to detect objects such as conspecifics, predators, and prey. Hypercomplex cells found in feline cortex and small target motion detectors found in dragonfly and hoverfly optic lobes demonstrate robust tuning for small objects, with weak or no response to larger objects or movement of the visual panorama. However, the relationship among anatomical, molecular, and functional properties of object detection circuitry is not understood. This study characterized a specialized object detector in Drosophila, the lobula columnar neuron LC11. By imaging calcium dynamics with two-photon excitation microscopy, it was shown that LC11 responds to the omni-directional movement of a small object darker than the background, with little or no responses to static flicker, vertically elongated bars, or panoramic gratings. LC11 dendrites innervate multiple layers of the lobula, and each dendrite spans enough columns to sample 75 degrees of visual space, yet the area that evokes calcium responses is only 20 degrees wide and shows robust responses to a 2.2 degrees object spanning less than half of one facet of the compound eye. The dendrites of neighboring LC11s encode object motion retinotopically, but the axon terminals fuse into a glomerular structure in the central brain where retinotopy is lost. Blocking inhibitory ionic currents abolishes small object sensitivity and facilitates responses to elongated bars and gratings. These results reveal high-acuity object motion detection in the Drosophila optic lobe (Keles, 2017).

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. This study reconstructed a comprehensive connectome of the circuits of Drosophila's motion-sensing T4 cells using a novel EM technique. Complex T4 inputs were uncovered, and putative excitatory inputs cluster were revealed at T4's dendrite shafts, while inhibitory inputs localize to the bases. Consistent with a previous study, this. study revealed that Mi1 and Tm3 cells provide most synaptic contacts onto T4. It was not possible, however, to reproduce the spatial offset between these cells reported previously. This comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors (Takemura, 2017).

The anterior visual pathway (AVP) conducts visual information from the medulla of the optic lobe via the anterior optic tubercle (AOTU) and bulb (BU) to the ellipsoid body (EB) of the central complex. This paper analyzes the formation of the AVP from early larval to adult stages. The immature fiber tracts of the AVP, formed by secondary neurons of lineages DALcl1/2 and DALv2, assemble into structurally distinct primordia of the AOTU, BU, and EB within the late larval brain. During the early pupal period (P6-P48) these primordia grow in size and differentiate into the definitive subcompartments of the AOTU, BU, and EB. The primordium of the EB has a complex composition. DALv2 neurons form the anterior EB primordium, which starts out as a bilateral structure, then crosses the midline between P6 and P12, and subsequently bends to adopt the ring shape of the mature EB. Columnar neurons of the central complex, generated by the type II lineages DM1-4, form the posterior EB primordium. Starting out as an integral part of the fan-shaped body (FB) primordium, the posterior EB primordium moves forward and merges with the anterior EB primordium. This paper documents the extension of neuropil glia around the nascent EB and BU and analyzes the relationship of primary and secondary neurons of the AVP lineages (Lovick, 2017).

Sd function has been best studied in the context of Hippo pathway-dependent tissue growth, where it serves as the key transcription factor of the Yki transcriptional co-activator. Sd has also been linked to regulation of transcription with other proteins, such as Vestigial, Tondu-domain-containing Growth Inhibitor (Tgi), and in this study, Nerfin-1. Interestingly, the Hippo pathway was among the top signaling pathways identified in KEGG analyses on putative Sd/Nerfin-1 target genes. However, Sd promotes medulla neuronal fate independent of Hippo and Yki, as Yki is not obviously expressed in medulla neurons and cannot induce dedifferentiation. Interestingly, this contrasts with the reported role of Hippo, Yki, and Sd in other neurons. For example, these proteins operate together to control the fate of R8 photoreceptor neurons of the D. melanogaster eye. In one class of these light-sensing neurons, Yki and Sd are required to adopt a fate that allows the sensing of blue light, whereas in the other subtype, which senses green light, the Hippo pathway represses Yki and Sd activity (Vissers, 2018).

Orthologs of Sd and Nerfin-1 have been functionally linked in both C. elegans and vertebrates. In C. elegans, EGL-44 and EGL-46 form a physical complex and are both required to specify neuronal cell fate and Q neuroblast differentiation. By characterizing both the biochemical interaction of Sd and Nerfin-1 and their role in maintenance of neuronal fate, this study shows that they cooperate to perform similar functions in flies and worms. The vertebrate orthologs of Sd and Nerfin-1 (TEAD1-4 and INSM1) have also been implicated in various aspects of neural and neuroendocrine development. Murine INSM1 is required for the development of endocrine and neuroendocrine cells of the pancreas, intestine, pituitary, and lung, while in the CNS, it is required for differentiation of neural progenitor cells. Furthermore, zebrafish insm1a has been implicated in dedifferentiation in the context of Müller glia regeneration. Similarly, expression of a TEAD gain-of-function allele caused a marked expansion of the neural progenitor pool in the developing chick neural tube. Interestingly, preliminary studies suggest that the TEAD/INSMI pair might also operate together in vertebrates; a motif corresponding to the TEAD binding site was enriched in INSM1 target genes, as determined by INSM1 ChIP-seq performed in murine pancreatic beta cells. These studies of Sd and Nerfin-1 in the Drosophila CNS, coupled with the finding that human TEAD1 and INSM1 form a physical complex, further strengthen the idea that these proteins represent an evolutionarily conserved transcription factor pair (Vissers, 2018).

The central nervous system develops from monolayered neuroepithelial sheets. In a first step patterning mechanisms subdivide the seemingly uniform epithelia into domains allowing an increase of neuronal diversity in a tightly controlled spatial and temporal manner. In Drosophila, neuroepithelial patterning of the embryonic optic placode gives rise to the larval eye primordium, consisting of two photoreceptor (PR) precursor types (primary and secondary), as well as the optic lobe primordium, which during larval and pupal stages develops into the prominent optic ganglia. This study characterize a genetic network that regulates the balance between larval eye and optic lobe precursors, as well as between primary and secondary PR precursors. In a first step the proneural factor Atonal (Ato) specifies larval eye precursors, while the orphan nuclear receptor Tailless (Tll) is crucial for the specification of optic lobe precursors. The Hedgehog and Notch signaling pathways act upstream of Ato and Tll to coordinate neural precursor specification in a timely manner. The correct spatial placement of the boundary between Ato and Tll in turn is required to control the precise number of primary and secondary PR precursors. In a second step, Notch signaling also controls a binary cell fate decision, thus, acts at the top of a cascade of transcription factor interactions to define photoreceptor subtype identity. This model serves as an example of how combinatorial action of cell extrinsic and cell intrinsic factors control neural tissue patterning (Mishra, 2018).

In the fruit fly Drosophila melanogaster, all parts of the visual system develop from an optic placode, which forms in the dorsolateral region of the embryonic head ectoderm. During embryogenesis, neuroepithelial cells of the optic placode are patterned to form two subdomains. The ventroposterior domain gives rise to the primordium of the larval eye and consists of two photoreceptor (PR) precursor types (primary and secondary precursors), whereas the dorsal domain harbors neuroepithelial precursors that generate the optic lobe of the adult visual system. The basic helix-loop-helix transcription factor Atonal (Ato) promotes PR precursor cell fate in the larval eye primordium. The orphan nuclear receptor Tailless (Tll) is confined to the optic lobe primordium and maintains non-PR cell fate. Hedgehog (Hh) and Notch (N) signaling are critical during the early phase of optic lobe patterning. The secreted Hh protein is required for the specification of various neuronal and non-neuronal cell types, while Notch acts as neurogenic factor preventing ectodermal cells from becoming neuronal precursors by a process termed lateral inhibition. In the optic placode Ato expression is promoted by Hh and the retinal determination genes sine oculis (so) and eyes absent (eya). Notch delimits the number of PR precursors and maintains a pool of non-PR precursors. Ato is initially expressed in all PR precursors in the placode and its expression gets progressively restricted to primary precursors. In a second step, primary precursors recruit secondary precursors via EGFR signaling: primary precursors express the EGFR ligand Spitz, which is required in secondary precursors to promote their survival. After this initial specification of primary and secondary PR precursors, the transcription factors Senseless (Sens), Spalt (Sal), Seven-up (Svp) and Orthodenticle (Otd) coordinate PR subtype specification. Sens and Spalt are expressed in primary PR precursors, while Svp contributes to the differentiation of secondary PR precursors. By the end of embryogenesis, primary PR precursors have fully differentiated into blue-tuned Rhodopsin5 PRs (Rh5), while secondary PR precursors have differentiated into green-tuned Rhodopsin6 PRs (Rh6). While the functional genetic interactions of transcription factors controlling PR subtype specification has been thoroughly studied, it remains unknown how the placode is initially patterned by the interplay of Hh and Notch signaling pathways. Similarly, the mechanisms of how ato and tll-expressing domains are set up to ensure the correct number of primary and secondary PR precursors as well as non-PR precursors of the optic lobe primordium remain unknown (Mishra, 2018).

This study describes the genetic mechanism of neuroepithelial patterning and acquisition of PR versus non-PR cell fate in the embryonic optic placode and provide the link to subsequent PR subtype identity specification. The non-overlapping expression patterns of ato and tll in the optic placode specifically mark domains giving rise to the larval eye precursors (marked by Ato) and the optic lobe primordium (marked by Tll). ato expression in the larval eye primordium is temporally dynamic and can be subdivided into an early ato expression domain, including all presumptive PR precursors and a late ato domain, restricted to presumptive primary PR precursors. The ato expression domain directly forms a boundary adjacent to tll expressing precursors of the optic lobe primordium. tll is both necessary and sufficient to delimit primary PR precursors by regulating ato expression. Hh signaling regulates the cell number in the optic placode and controls PR subtype specification in an ato- and sens-dependent manner. Finally, this study also shows that Notch has two temporally distinct roles in larval eye development. Initially, Notch represses ato expression by promoting tll expression and later, Notch controls the binary cell fate decision of primary versus secondary PR precursors by repressing sens expression. In summary, this study has identified a network of genetic interactions between cell-intrinsic and cell-extrinsic developmental cues patterning neuroepithelial cells of the optic placode and ensuring the timely specification of neuronal subtypes during development (Mishra, 2018).

Neurogenic placodes are transient structures that are formed by epithelial thickenings of the embryonic ectoderm and give rise to most neurons and other components of the sensory nervous system. In vertebrates, cranial placodes form essential components of the sensory organs and generate neuronal diversity in the peripheral nervous system. How neuronal diversity is generated varies from system to system, and different gene regulatory networks have been proposed for each particular type of neuron. Interestingly, some transcription factors, like Atonal, play an evolutionary conserved role during neurogenesis both in Drosophila and in vertebrates (Mishra, 2018).

Neuroepithelial patterning of the Drosophila optic placode exhibits unique segregation of larval eye and optic lobe precursors during embryogenesis. This study has identified genetic mechanisms that control early and late steps in specifying PR versus non-PR cell fate that ensure the expression of precursor cell fate determinants. During germband extension at stage 10, transcriptional regulators (so, eya, ato and tll) show complex and partially overlapping expression patterns in the optic placode. Their interactions with the Notch and Hh signaling pathways define distinct PR and non-PR domains of the larval eye and optic lobe primordium. Intriguingly, the results show a spatial organization of distinct precursor domains, supporting a new model of how the subdivision of precursor domains emerges. In agreement with previous studies initially the entire posterior ventral tip expresses Ato, defining the population of cells that give rise to PR precursors, while neuroepithelial precursors for the presumptive optic lobe are defined by Tll-expression in the anterior domain of the optic placode. Subsequently, Ato expression ceases in the ventral most cells and thus gets restricted to about four primary PR precursors that are located directly adjacent to the Tll expression domain. Hence, a few cell rows are between the primary PR precursors and the ventral most edge of the optic placode. This is in agreement with a recent observation on the transcriptional regulation of ato during larval eye formation. Thus, primary PR precursors are directly adjacent to the Tll-expressing cells while the Ato and Tll negative domain of secondary PR precursors is located at the posterior ventral most tip of the optic placode. Setting the Tll-Ato boundary is critical to define the number of putative secondary PR precursors, which can be recruited into the larval eye, probably via EGFR signaling. A model is proposed during which coordinated action of Hh, Notch and Tll restricts the initially broad expression of Ato to primary PR precursors (see Ato to primary PR precursors). Lack of Tll results in a de-repression of Ato and results in an increased number of primary PR precursors, which in turn recruit secondary PR precursors. Interestingly, while tll mutants show an increase in both primary and secondary PR precursors, the ratio between both subtypes is maintained. This notion further displays similarities of ommatidal formation in the adult eye-antennal imaginal disc, where Ato expressing R8-precursors recruit R1-R6. In the eye-antennal disc, specification of R8-precursors determines the total number of ommatida and therefore also the total number of PRs, the ratio of R8 to outer PRs however always remains the same. Thus, the initial specification of primary PR precursors defines the total number of PRs in the larval eye similarly to R8 PRs, and the ratio of founder versus recruited cells remains constant. Interestingly, the maintenance of primary versus secondary PR precursor ratio is also maintained in ptc mutants further supporting this model (Mishra, 2018).

During photoreceptor development in the eye-antennal imaginal disc hh is expressed in the posterior margin and is required for the initiation and progression of the morphogenetic furrow as well as the regulation of ato expression. During embryogenesis the loss of hh results in a complete loss of the larval eye, while increasing Hh signaling (by means of mutating ptc) generates supernumerary PRs in the larval eye. During early stages, an increase of Ato expression was found in ptc mutants suggesting that similarly to the eye-antennal disc Hh positively regulates ato expression. The observed increase of Ato-expressing cells is not due to a reduction of Tll but is likely due to increased cell proliferation in ptc mutants. Hh also controls proliferation during the formation of the Drosophila compound eye (Mishra, 2018).

During embryonic nervous system development Notch dependent lateral inhibition selects individual neuroectodermal cells to become neuroblasts. Notch represses neuroblast cell fate and promotes ectodermal cell fate. During compound eye development, Notch regulates Ato expression and acts through lateral inhibition to select Ato expressing R8 PR precursors. Similarly, during Drosophila larval eye development, Notch is required for regulating PR cell number by maintaining epithelial cell fate of the optic lobe primordium. Inhibiting Notch signaling leads to a complete transformation of the optic placode to PRs of the larval eye. In the absence of Notch signaling, Ato expression is expanded in the optic placode and as a result the total number of PRs is increased. Despite the increase of the overall PR-number the number of secondary PR precursors is significantly decreased or lost in the absence of Notch activity. In the compound eye Notch promotes R7 cell fate by repressing the R8-specific transcription factor Sens. It was also proposed that genetic interaction between Notch and Sens is required for sensory organ precursor (SOP) selection in the proneural field in a spatio-temporal manner. This study found that during PR subtype specification Notch represses Sens expression, thereby controlling the binary cell fate decision of primary versus secondary PR precursors. Therefore, in the absence of Notch signaling, Sens expression represses the secondary PR precursor fate. As a result, all PR precursors are transformed and acquire primary PR precursor identity. In conclusion, this study observed that Notch is essential for two aspects during optic placode patterning. First, Notch activity is critical for balancing neuroepithelial versus PR cell fate mediated through Tll-regulated Ato expression. Second, Notch regulates the binary cell fate decision of primary versus secondary PR precursor cell fate through the regulation of Sens expression (Mishra, 2018).

The molecular and cellular mechanism for clearance of dead neurons was explored in the developing Drosophila optic lobe. During development of the optic lobe, many neural cells die through apoptosis, and corpses are immediately removed in the early pupal stage. Most of the cells that die in the optic lobe are young neurons that have not extended neurites. This study shows that clearance was carried out by cortex glia via a phagocytosis receptor, Draper (Drpr). drpr expression in cortex glia from the second instar larval to early pupal stages was required and sufficient for clearance. Drpr that was expressed in other subtypes of glia did not mediate clearance. Shark and Ced-6 mediated clearance of Drpr. The Crk/Mbc/dCed-12 pathway was partially involved in clearance, but the role was minor. Suppression of the function of Pretaporter, CaBP1 and phosphatidylserine delayed clearance, suggesting a possibility for these molecules to function as Drpr ligands in the developing optic lobe (Nakano, 2019).

Many studies have explored the cellular and molecular mechanisms for clearance of dead neurons in the developing Drosophila CNS. During embryonic development, dead neurons are phagocytosed by subperineurial glia. Draper (Drpr) acts as a phagocytosis receptor on the glial membrane to clear dead neurons in the embryo. Another receptor, Six-microns-under (SIMU), works in cortex glia to allow recognition and engulfment of apoptotic cells, whereas Drpr works to degrade apoptotic cells in the embryonic CNS. During metamorphosis, dead neurons are engulfed by glia in the CNS. Elimination of neurites of vCrz neurons during metamorphosis is performed by astrocyte-like glia via the Crk/Mbc/dCed-12 signaling pathway but not the Drpr pathway. In contrast, elimination of cell bodies of vCrz neurons, a group of neurons that express neuropeptide Corazonin, requires Drpr, but its expression is not required in astrocyte-like glia. However, recent studies have reported inconsistent results on the requirement of Drpr for dead cell clearance and the glia subtypes that work for clearance in the brain during metamorphosis. It has been reported that dead neurons that died in the central brain before the beginning of the third larval instar and in the optic lobe before the late third larval instar are cleared by cortex glia via the Drpr pathway, but neurons that die thereafter are efficiently cleared without Drpr. Drpr has been shown to be required for apoptotic cell clearance during metamorphosis and its expression is required in ensheathing glia and astrocyte-like glia, but not in cortex glia (Nakano, 2019).

One of the causes of inconsistency among previous studies may be differences in the cellular materials to be phagocytosed, and different mechanisms could work for phagocytosis of different materials in the CNS during metamorphosis. Three types of neurons need to be phagocytosed during metamorphosis. Obsolete larval neurons die, and their cell bodies and neurites are removed by phagocytosis. Larval neurons of another type are respecified from larval to adult neurons via pruning of larval neurites and extension of new adult neurites. Pruned neurites are removed by phagocytosis. Adult-specific neurons are produced by precursor cells during post-embryonic development and differentiate during metamorphosis. A number of these young neurons die during development before extending neurites. Therefore, studies on a single type of neuron or specifically defined neurons are needed to define the molecular and cellular mechanisms for clearance of dead neurons. Moreover, clearance of neurites and cell bodies of dead neurons should be studied independently (Nakano, 2019).

In this study, clearance of dead neurons in the developing optic lobe was examined. The Drosophila optic lobe is a unique center in which a large number of dying cells are observed during its development. Most dying neurons in the optic lobe are young neurons that had just started to differentiate into adult neurons. One of paired neurons derived from intermediate precursors (GMCs) is eliminated by apoptosis under the control of Notch signaling. Neurons that die in the developing optic lobe have not yet extended neurites at the time they die. Therefore, cellular materials to be cleared after the cell death include nuclei and general cytoplasm, but not neurites in the developing optic lobe (Nakano, 2019).

The adult optic lobe develops from the primordium during metamorphosis. Optic lobe neurons are produced by two proliferation centers, the outer proliferation center (OPC) and inner proliferation center (IPC). Neurons differentiate, extend neurites, and produce four types of neuropil, the lamina, medulla, lobula plate, and lobula. Then, the optic lobe consists of four types of neuropil and surrounding cortices of neuronal cell bodies. According to previous studies, many neurons and a small number of precursor cells undergo cell death during optic lobe development. This cell death does not occur randomly in the optic lobe but occurs in clusters in a specific temporal and spatial pattern. The number of dead cells in the optic lobe starts to increase at the puparium formation, reaches a peak at 24 h after puparium formation (24 h APF), and decreases to almost zero by 48 h APF. Two types of cell death are involved in this process: ecdysone dependent and independent. Both types of cell death are apoptosis and involve the Drosophila effector caspases, DrIce and Dcp-1. DrIce plays an important role in dead cell clearance as well. The role of cell death is to prevent the emergence of abnormal neural structures in the optic lobe (Nakano, 2019).

This study explored the cellular and molecular mechanisms for clearance of dead young neurons in the developing optic lobe. The results showed that clearance was carried out by cortex glia via a phagocytosis receptor, Drpr. Drpr expression in cortex glia from the second instar larval to early pupal stages was required and sufficient for clearance. Signaling molecules, Shark and Ced-6 mediated clearance downstream of Drpr. The Crk/Mbc/dCed-12 pathway was partially involved in clearance, but the role was minor. Suppression of the function of Pretaporter, CaBP1 and phosphatidylserine delayed clearance, suggesting a possibility for these molecules to function as Drpr ligands in the developing optic lobe (Nakano, 2019).

This study revealed that Drpr expressed in cortex glia were required for dead cell clearance in the MLpL region of the developing optic lobe, and that Drpr in other subtypes of glia did not mediate clearance. This is the first study that showed clearance of dead young neurons in the developing optic lobe required Drpr expression in the cortex glia. In the lamina region, lamina distal cortex glia work for dead cell clearance (Nakano, 2019).

The expression pattern of Drpr agreed with the alteration in the activity of dead cell clearance in the optic lobe during metamorphosis. At early pupal stages, Drpr is expressed weakly in a mesh-like pattern and strongly in a centripetal pattern in cortex glia in the MLpL region. This expression weakened thereafter, and only weak expression was seen in a mesh-like pattern during the last half of the pupal period. Moreover, no protrusion was seen of Drpr expressing glial cytoplasm into the neuropil from neuropil glia (NG) at early pupal stages. This agrees with the fact that cell death in the developing optic lobe occurs mainly in young neurons before they extend neurites or in abnormal neurons with no neurites (Nakano, 2019).

After 48 h APF, cell death was rarely observed and thus activity of dead cell clearance was low. However, this does not mean that glia lost potential ability to clear corpses at late pupal stages. Forced expression of wild-type drpr on the drpr mutant background at 48 or 72 h APF resulted in clearance of accumulated TUNEL-positive cells. This indicates that the components of the mechanism for dead cell clearance except Drpr are retained until late pupal stages. Therefore, if some cells died at late pupal stages and Drpr expression was induced in cortex glia, the dead cells would be cleared via Drpr pathway. Moreover, the fact that accumulated TUNEL-positive cells were removed when wild-type drpr was forcibly expressed in late pupal stages on the drpr mutant background suggests that 'eat me' signals were secreted or displayed by accumulated TUNEL-positive cells in drpr mutants not only at early pupal stages, when the cells died, but also at late pupal stages long after cell death (Nakano, 2019).

At late pupal stages, strong Drpr expression appeared in the cytoplasmic protrusions from the neuropil glia (NG), and astrocyte-like glia simultaneously started expressing molecular markers (specific GAL4s). This Drpr was not utilized for clearance of dead neurons, as almost no cell death arises at this stage in control conditions. In the neuropil of the optic lobe at late pupal stages, neurites extend and form synapses to make and complete neural networks. When new synapses are formed during development of the Drosophila larval neuromuscular junction, significant amounts of presynaptic membranes and a subset of immature synapses are removed from the junction by surrounding glia and postsynaptic muscle via the Drpr/dCed-6 pathway (Fuentes-Medel, 2009). Thus, the same process may arise at developing synapses in the developing optic lobe, and astrocyte-like glia expressing Drpr in the cytoplasmic protrusions may function to remove unnecessary presynaptic membranes and immature synapses (Nakano, 2019).

Previous studies have reported that astrocyte-like glia are responsible for clearance of degenerating axons of dying obsolete larval neurons in the ventral nerve cord (Tasdemir-Yilmaz, 2014) and of pruned axons of γ neurons in the mushroom body. In contrast, degenerating axons are removed by ensheathing glia in the olfactory lobe following Wallerian degeneration of the olfactory nerve. Therefore, different subtypes of glia work to clear degenerating axons in different contexts. Astrocyte-like glia may specifically function for clearance of 'programmed' degenerating axons and ensheathing glia for clearance of 'accidently' degenerating axons. In addition, another subtype of glia, cortex glia, functions to remove dead young neurons. These young neurons had just started to differentiate into adult neurons in the developing optic lobe and have not yet extended neurites at the time they die. Tasdemir-Yilmaz (2014) reported that elimination of cell bodies of obsolete vCrz neurons requires Drpr, but its expression is not required in astrocyte-like glia. Young neurons in the optic lobe and cell bodies of obsolete vCrz neurons in the ventral nerve cord both locate in the cortex and almost the same cellular materials are cleared after the cell death, including nucleus and general cytoplasm, but not neurites. Therefore, as with dead young neurons in the optic lobe, the expression of Drpr in cortex glia would be required for clearance of cell bodies of dead vCrz neurons. Comparative studies are expected in the future on the mechanisms for clearance of degenerating axons of dead neurons, degenerating axons of cut nerves, dead young neurons, and cell bodies of dead obsolete neurons. Moreover, considering that 'accidently' degenerating axons are cleared by a different subtype of glia from 'programmed' degenerating axons, a possibility should be tested that cell bodies of neurons that died 'accidently' are cleared by a distinct subtype of glia (Nakano, 2019).

This is the first study to reveal that Shark mediates Drpr-dependent clearance of dead neurons in the CNS. Moreover, this study suggests that Ced-6, Crk/Mbc/dCed-12, and Rac1 are partially involved in clearance of dead young neurons. Therefore, both Drpr/Shark/dCed-6 and Crk/Mbc/dCed-12 pathways work for dead cell clearance in the developing optic lobe. As cortex glia function for clearance of dead neurons in the developing optic lobe, these pathways must work in cortex glia (Nakano, 2019).

A previous study reported that these pathways function to mediate removal of degenerating axons in ensheathing glia in the adult olfactory lobe when the olfactory nerve is cut (Ziegenfuss, 2012). The same pathways work in astrocyte-like glia around the mushroom body when axons of γ neurons are pruned, and in the ventral nerve cord when neurites of dead vCrz neurons are cleared during metamorphosis (Tasdemir-Yilmaz, 2014). Therefore, Drpr/Shark/dCed-6 and Crk/Mbc/dCed-12 pathways generally function to clear corpses in different glia in different contexts. However, the relative role played by each pathway depends on the situation. Although drpr mutation strikingly inhibited dead cell clearance in the optic lobe, knockdown of the Crk/Mbc/dCed-12 pathway had only a moderate effect. In contrast, removal of olfactory nerve axons that have undergone Wallerian degeneration is strongly affected by both drpr mutation and Crk/Mbc/dCed-12 knockdown (Ziegenfuss, 2012). In the pruning of mushroom body γ axons, mutation of drpr and knockdown of Crk/Mbc/dCed-12 additively affect clearance, although mutation of drpr has a stronger effect (Tasdemir-Yilmaz, 2014). In the removal of axons from dead vCrz neurons during metamorphosis, knockdown of dCed-12 causes a moderate defect, whereas drpr mutation causes no defect by itself but only enhances the defect caused by dCed-12 knockdown (Tasdemir-Yilmaz\, 2014). How the relative role of these pathways is regulated and why remain to be defined (Nakano, 2019).

Previous studies reported that Pretaporter, CaBP1 and phosphatidylserine act as Drpr ligands when dead embryonic cells are phagocytosed in the Drosophila embryo and cultured cells. The present study suggested a possibility that these molecules mediate signaling for dead cell clearance as a Drpr ligand in the developing optic lobe. However, Pretaporter and CaBP1 are not essential for clearance and their role would be minor. Therefore, relative role of molecules that work for dead cell clearance as ligands for Drpr may be different depending on the context. As described above, different subtypes of glia work to clear corpse in the CNS: ensheathing glia for Wallerian's degenerating axons, astrocyto-like glia for pruned axons and degenerating axons of dead vCrz neurons, and cortex glia for dead young neurons in the optic lobe. Therefore, it is to be defined whether difference in ligand molecules is involved in activating different subtypes of glia (Nakano, 2019).

The present study agrees with results described by Tasdemir-Yilmaz (2014), who reported that Drpr is required for elimination of cell bodies of vCrz neurons that die at 3-7 h APF. However, the current study disagrees with other studies (Nakano, 2019 and references therein).

Several possible causes may have led to these inconsistencies. One possibility is the difference in cellular materials to be cleared, that is, cell bodies or neurites, as mentioned earlier. The present study examined dead young neurons in the developing optic lobes. Cellular materials to be cleared include only nucleus and general cytoplasm, but not neurites. Tasdemir-Yilmaz (2014) studied vCrz neurons and found that molecular mechanisms for clearance of cell bodies and neurites are different. However, other studies examined the central brain or the whole brain, which include cell bodies of dead neurons, neurites of dead neurons and pruned neurites. Another possibility is the difference in methods to detect dead neurons. This study used the ABC TUNEL method, which detects degraded DNA in dead cell nuclei with the streptavidin-biotin-peroxidase complex (Vector Laboratories). This method is far more sensitive and reliable than the fluorescent TUNEL method (compare the number of TUNEL-positive cells between the present study and other studies). Another method to detect dead cells is anti-Dcp-1 antibody staining. This method has some problems with detection of accumulated dead cell corpses in phagocytosis-defective mutants. It detects activated Dcp-1, one of the effector caspases. However, another effector caspase, DrIce, is also expressed and is a more effective inducer of apoptosis than Dcp-1. Therefore, this method may not detect all dead cells. Another problem with this method is the unknown stability of activated Dcp-1 in dead cells. Therefore, detection of activated Dcp-1 does not show exactly how many dead cells have accumulated in phagocytosis-defective mutants. Moreover, when dendrites are pruned during remodeling of dendritic arborization sensory neurons during metamorphosis, caspase activity is detected in the dendrite. This suggests that the anti-Dcp-1 antibody may detect pruned dendrites as well as dead cells. Finally, the subtypes of glia that clear corpses are also different in the present study and previous ones. This study found that expression of GAL4 in glia subtype-specific GAL4 lines drastically changed during metamorphosis, and the expression pattern at pupal stages was different from adult stages in many GAL4 lines. However, previous studies did not examine the expression pattern of the GAL4 lines they used. Altogether, studies on a single type of dead neuron or identified neurons are required in the future. The mechanisms for clearance of dead cell bodies and degenerating neurites should be studied independently. The expression pattern of GAL4 lines in subtypes of glia should be carefully assessed before using the line as a GAL4 driver (Nakano, 2019).

scarecrow (scro) gene encodes a Drosophila homolog of mammalian Nkx2.1 that belongs to an evolutionally conserved NK2 family. Nkx2.1 has been well known for its role in the development of hypothalamus, lung, thyroid gland, and brain. However, little is known about biological roles of scro. To understand scro functions, this study generated two types of knock-in mutant alleles, substituting part of either exon-2 or exon-3 for EGFP (or Gal4) by employing the CRISPR/Cas9 genome editing tool. Using these mutations, spatio-temporal expression patterns of the scro gene and its mutant phenotypes were characterized. Homozygous knock-in mutants are lethal during embryonic and early larval development. In developing embryos, scro is exclusively expressed in the pharyngeal primordia and numerous neural clusters in the central nervous system (CNS). In postembryonic stages, the most prominent scro expression is detected in the larval and adult optic lobes, suggesting that scro plays a role for the development and/or function of this tissue type. Notch signaling is the earliest factor known to act for the development of the optic lobe. scro mutants lacked mitotic cells and Delta expression in the optic anlagen, and showed altered expression of several proneural and neurogenic genes including Delta and Notch. Furthermore, scro mutants showed grossly deformed neuroepithelial (NE) cells in the developing optic lobe and severely malformed adult optic lobes, the phenotypes of which are shown in Notch or Delta mutants, suggesting scro acting epistatic to the Notch signaling. From these data it is proposed that scro plays an essential role for the development of the optic lobe, possibly acting as a regional specification factor (Yoo, 2020).

Scro was first identified 20 years ago, and it was shown to be a fly homolog of mammalian Nkx2.1, which was discovered originally for its transcriptional activity on the thyroid specific genes. Scro displays approximately 44% identity to Nkx2.1 (ID 21869, mouse) and 47% to Nkx2.4 (ID 644524, mouse), a paralog of Nkx2.1, suggesting that scro is homologous to both genes. Studies in zebrafish showed that Nkx2.1 and Nkx2.4 have partially overlapping functions in the development of the hypothalamus. In Drosophila, scro expression was found in the pharyngeal primordia and in many distinct clusters of neural precursor cells in the embryonic CNS, leading to a speculation that scro function might be involved in the development of these tissues. However, no further studies followed to explore in vivo roles of scro. One of the main reasons is the lack of useful mutations for this gene. Because the scro locus is situated in a heterochromatized region of the third chromosome that is known for a genetically inactive 'cold-spot', genetic manipulations of this gene have been a challenge (Yoo, 2020).

There are four annotated transcript types (RA/RB/RC/RF) from the scro locus, and two start codons are predicted to be used for translation; one in E2 for RA/RB/RC and the other in E3 for RF. To further delve into scro's function, scro^ΔE2 and scro^ΔE3 knock-in alleles in which a reporter gene (EGFP or Gal4) replaces either part of E2 (scro^ΔE2) or E3 (scro^ΔE3). The scro^ΔE2 was designed not only to disrupt but also reflect the expression of RA/RB/RC transcripts (collectively refered to as RA-type). In this mutant, RF expression was unaffected. In contrast, the scro^ΔE3 was intended to disrupt the expression of all four transcript types but to report expression patterns of RF isoform only. However, contrary to prediction, mutant phenotypes and complementation tests suggest that scro^ΔE3 is not a null but rather a weaker allele than scro^ΔE2, and currently little is known about a cause of this unexpected result (Yoo, 2020).

Both types of scro knock-in mutants are homozygous lethal, suggesting that scro plays a vital function. Undisturbed RF expression in scro^ΔE2 suggests that the vital function is mainly provided by the RA-type isoforms. Based on prominent scro expression in the pharyngeal primordia and significantly less food consumption by scro mutant larvae, it is speculated that subnormal feeding activity due to aberrant development and/or function of pharyngeal and foregut tissue might contribute at least partly to the lethality. It was also noticed that numerous clusters of scro-expressing neurons are present in the larval CNS including the pars intercerebralis (PI) region which is considered to be analogous to vertebrate hypothalamus-pituitary axis. PI is a major neuroendocrine center that regulates metabolism and growth in flies. Therefore, defective neuroendocrine system and/or subnormal activities of the CNS might impinge on a lethal cause of the scro mutants (Yoo, 2020).

It is noted that the knock-in reporters may not completely recapitulate the endogenous scro expression for the following reason. Both scro^ΔE2 and scro^ΔE3 knock-ins produce a transcript containing a reporter-coding ORF that starts shortly after the endogenous start codon (AUG). It is known that when two AUGs are closely situated in a single mRNA, 43S pre-initiation complex (PIC) starts translation at the first AUG of upstream ORF (uORF). If uORF is short due to early termination codon, PIC often re-initiates the translation at the downstream AUG, the event of which is referred to as 'leaky scanning'. In the case of E1-E2/Reporter from scro^ΔE2 and E1-E3/Reporter from scro^ΔE3, translation from the first native AUG terminates shortly, raising the probability of the re-initiation by PIC at the reporter AUG. On the other hand, another E1-E2-E3/Reporter transcript type produced also in scro^ΔE3 mutants contains three start codons; two native ones in E2 and E3 and the third one in the reporter. In this case, the most upstream AUG in E2 is expected to be used first; however, the resulting lengthy uORF (173 codons) would likely suppress the re-initiation of translation at the downstream reporter-AUG. Hence, the E1-E3/Reporter transcript should be considered the main source for the reporter expression that was observed with the scro^ΔE3. Despite such complicated molecular events concerning the translation efficiency of the knock-in transcripts, comparable expression patterns between the knock-in reporters and in situ hybridization indicate that at least scro^ΔE2 recapitulates most, if not all, endogenous scro expression patterns. Moreover, scro knockdown by scro^ΔE2-Gal4 phenocopied the lethal phenotype of scro knock-in mutants, further supporting the fidelity of this reporter. Therefore it is argued that scro knock-in alleles are certainly a valuable tool to further understand scro's expression and functions (Yoo, 2020).

Reporter expression patterns by scro^ΔE2 generally agree with those by scro^ΔE3 particularly in the postembryonic CNS, implying that both RA-type and RF transcripts are simultaneously produced in the same neural cells. However, dissimilar expression patterns was also observed between the two reporters. For instance, embryonic expression is detected only in scro^ΔE2, implying that the RA-type transcript is a key player during embryogenesis. In addition, some neural clusters in the adult central brain and larval VNC are unique to either scro^ΔE2 or scro^ΔE3. These results suggest both common and distinct roles played by scro isoforms. Differential expression patterns and distinct functions of individual isoforms are not unprecedented in other NK genes. For instance, only one of two isoforms of Nkx2.1 is known to play a major role in pulmonary differentiation and development. Similarly, two isoforms of vnd gene perform different roles for the embryonic nervous system development and the differentiation of photoreceptor cells during metamorphosis (Yoo, 2020).

This study has shown that scro's function is critical for the OL development. Although the underlying molecular mechanisms remain to be investigated, it is hypothesized that scro might act as a specification factor of the OL precursor cells during embryogenesis. Members of the NK family have been shown to act as specification factors or in upstream event essential for development of a certain tissue (or cell) type. For instance, Drosophila VNC develops from three columns of neuroectodermal cells along the dorsoventral (DV) axis: ventral, intermediate, and dorsal. vnd induces ventral fates and represses intermediate fates through the regulation of an array of proneural genes including achaete, scute, and l'sc. Loss of vnd transforms ventral into intermediate column identity. In mammalian telencephalon development, Nkx2.1 is critical for the specification of the ventral pallidum, as Nkx2.1-deficiency causes the transformation of pallidum to dorsal striatum. Furthermore, Nkx2.1 is important for the specification of post-mitotic GABAergic neurons generated in the medial ganglionic eminence Yoo, 2020 and references therein).

These data provide some clues about what roles scro plays for the OL development. It is known that the Notch signaling is required for fate determination of the embryonic NE cells of the optic placode during and after its invagination and for later differentiation of NE cells into the OL neuropils. According to the current data, scro seems to act epistatic to the Notch signaling. For instance, Delta expression in the optic anlagen is significantly reduced in scro mutants, suggesting that Delta expression is under control of scro. Besides, the Notch-expressing NE cells in scro mutants do not show a typical columnar shape and layer arrangement, and their progeny cells fail to form proper neural projections. These results suggest that scro function is required for the NE cells to establish characteristic morphology and arrangement of NE cells and proper differentiation of progeny cells. In addition, hypomorphic scro mutants fail to form the lamina structure and show a substantially smaller medulla region partly due to defective neural projections of medulla-like cells. Interestingly, similar phenotypes were described for the loss-of-function of Notch or Delta (Yoo, 2020).

scro mutants also show significantly reduced svp and cas expression but elevated expression of Notch, achaete, lethal of scute, and dpn, suggesting that an array of proneural and neural genes involved in cell fate determination of NE and/or NBs is directly or indirectly influenced by scro both positively and negatively. Lastly, following larval hatching, the NE cells actively proliferate through symmetric division and form the outer and inner optic anlage (OOA and IOA), which further develop to outer (OPC) and inner (IPC) proliferation centers, respectively. This study found that scro expression is restricted to the IOA in L2 stage, but Delta expression and mitotic activities of proliferating NE cells at this stage are absent from both optic anlagen in scro^ΔE2 mutants. A plausible explanation for such contradiction is that the fates of embryonic NE cells are altered in scro mutants, thus lacking their cellular properties to proliferate and form both IOA and OOA. Testing this hypothesis will be a future subject of study. In summary, this study generated scro knock-in alleles and characterized developmental and spatial scro expression patterns and mutant phenotypes, leading to the uncovering of a novel function of Drosophila scro for the development of adult visual processing nervous system, possibly as a regional specification factor (Yoo, 2020).

Precise patterns of synaptic connections between neurons are encoded in their genetic programs. This study used single-cell RNA sequencing to profile neuronal transcriptomes at multiple stages in the developing Drosophila visual system. An efficient strategy was devised for profiling neurons at multiple time points in a single pool, thereby minimizing batch effects and maximizing the reliability of time-course data. A transcriptional atlas spanning multiple stages is generated, including more than 150 distinct neuronal populations: of these, 88 are followed through synaptogenesis. This analysis reveals a common (pan-neuronal) program unfolding in highly coordinated fashion in all neurons, including genes encoding proteins comprising the core synaptic machinery and membrane excitability. This program is overlaid by cell-type-specific programs with diverse cell recognition molecules expressed in different combinations and at different times. It is proposed that a pan-neuronal program endows neurons with the competence to form synapses and that cell-type-specific programs control synaptic specificity (Kurmangaliyev, 2020).

Gene expression maps of postmitotic neurons in the fly visual system throughout pupal development (~100 h) were generated using high-throughput scRNA-seq. During this time, neurons complete axon guidance, elaborate distinct axonal and dendritic morphologies, select appropriate synaptic partners, form synapses, and acquire distinct biophysical properties (Kurmangaliyev, 2020).

Two different experimental designs were used. First, optic lobes were profiled at 24, 48, 72, and 96 h after puparium formation (APF). Each time point was profiled as a separate experiment, and for the 48-h time point, two independent samples were profiled. This dataset is referred to as W1118 based on the strain used for these experiments. This provided a high-resolution dataset for unsupervised identification of cell types at different stages of development. The largest number of cells (51,000) was profiled at 48 h APF, when the transcriptional differences between neuronal cell types were maximal (Kurmangaliyev, 2020).

A second set of experiments was undertaken to follow changes in gene expression at 12-h intervals to more precisely define the developmental trajectory of each cell type. A strategy was developed to profile neurons from multiple time points in a single experiment. This strategy exploits natural genetic variation in wild-type strains from the Drosophila Genetic Reference Panel (DGRP) to mark cells isolated at different developmental stages. Males from individual DGRP strains were crossed to females from a common reference strain (W1118) to reduce differences in genetic background. Pupae from the resulting progeny were collected every 12 h from 0 to 96 h APF. For every time point, three individual animals were staged, each tagged by a unique wild-type genotype. Optic lobes for all time points were dissected, pooled, and processed as a single sample from tissue dissociation to sequencing. The resulting single-cell transcriptomes for each time point and individual animals were separated based on SNPs (single-nucleotide polymorphisms) captured in mRNA sequences and matched with the genotypes of parental strains. In this way, cells were profiled at nine stages of development with three biological replicates for each time point (total 27 samples) in a single experiment. Two rounds of this experiment were performed with reshuffled wild-type parental genotypes (Kurmangaliyev, 2020).

Profiling pools of cells together from all time points in a single experiment has several advantages. First, it is highly efficient and cost effective. The entire experiment, from genetic crosses to single-cell capture, took 2 weeks. Second, it largely eliminates batch effects. An inherent limitation in the conventional design of scRNA-seq is the technical variation (batch effects) between different samples. In droplet-based scRNA-seq, for instance, mRNA released from tissue dissociation prior to cell capture is a source of contaminants confounding comparison between samples. In the conventional design, for instance, a contaminant (e.g., a transcript expressed at high levels at a specific time in a subset of cells) may appear as ubiquitously upregulated at this time in all neurons. In the pooled design, however, background contaminants that change with time are distributed among all cells and times and thus are not erroneously attributed to biological differences. Similarly, in pooled samples, other sources of technical variability of unknown etiology arising in the conventional design are eliminated. Finally, genetic multiplexing enables identification of cell doublets, further improving the quality of datasets and reducing cost through maximizing single-cell capture rates in scRNA-seq platforms (Kurmangaliyev, 2020).

In summary, a comprehensive dataset of gene expression in visual system neurons covering every 12 h of pupal development. After quality control, the dataset comprises 208,976 single-cell transcriptomes with a median of 1,650 genes and 5,148 transcripts detected per cell (minimum 2,000 transcripts). Each of the W1118 and DGRP approaches represented approximately half of the dataset (Kurmangaliyev, 2020).

Transcriptional differences between time points for a single neuronal cell type can be comparable to the differences between cell types. Therefore, to follow individual cell types over time, single-cell datasets were first integrated across different stages of development. Both W1118 and DGRP samples were analyzed together to achieve maximal resolution in cell type diversity and to generate a unified atlas of transcriptional cell types. Samples from 0 and 12 h APF differed substantially from other time points both in tissue composition and coverage and were analyzed separately (the early dataset). The main analysis was focused on samples from 24 to 96 h APF (the main dataset) (Kurmangaliyev, 2020).

The integrative analysis of the main dataset was performed using CCA (canonical correlation analysis) and MNN (mutual nearest neighbors)-based workflow implemented in Seurat 3. The integration was performed on the levels of individual time points and replicates. An integrated dataset was used for dimensionality reduction followed by a graph-based clustering method that revealed 196 transcriptionally distinct cell populations. The results of dataset integration and clustering analysis were visualized using t-distributed stochastic neighbor embedding (tSNE) . Importantly, the integrative analysis was used strictly for joint clustering of cell types across different samples (i.e., time points and replicates). Once clusters were identified, however, the original (pre-integration) normalized gene expression values were used for each time point and sample (Kurmangaliyev, 2020).

Most of the clusters were detectable in all samples and comprise a comparable fraction of cells across different time points, while some were distributed less evenly. The variation in cluster proportions could be due to technical reasons (e.g., sampling variation or differences in tissue dissections) or biological differences in cell composition at different stages. Unsupervised analysis grouped clusters into four major classes, which were annotated based on expression of known marker genes: 162 clusters of neurons (elav+), 3 clusters of photoreceptor cells (chp+), 19 clusters of glia (repo+), and 11 clusters of non-neuronal cells of unknown origin (elav/repo minus) (Kurmangaliyev, 2020).

Next, neuronal clusters to previously identified cell types. As a reference, two recent datasets from bulk sequencing of several dozen morphologically distinct cell types in the adult. A set of cluster-specific marker genes were identified and used for correlation analysis. In addition, rxpression patterns of TFs were used to manually curate matched clusters. Most of the reference cell types had a one-to-one match with a single cluster. In a few cases, a reference dataset had a high correlation with multiple clusters representing further heterogeneity. For example, T4/T5 neurons matched eight clusters with similar correlation values. Expression of previously identified marker genes allowed matching these clusters to the eight known morphological subtypes of T4/T5 neurons. Moreover, new transcriptional subtypes (e.g., two Dm3 and two Tm9 neurons) were identified (Kurmangaliyev, 2020).

Overall, cell-type-specific expression profiles of known morphological cell types were in good agreement with previously validated marker genes. For example, the TF bsh has been shown to be expressed in three cell types, and it was detected only in the corresponding clusters . The transcriptional similarities between clusters were also consistent with known anatomical and functional relationships between cell types. In total, it was possible to match identities of 58 neuronal clusters to known morphological cell types (Kurmangaliyev, 2020).

The optic lobe neuropils consist of repeated columns with stereotyped cell-type composition and connectivity. Many cell types are present in a single copy in each column (unicolumnar neurons). In agreement with this, all matched unicolumnar neurons were among the largest 40-50 transcriptional clusters in the data, and each represented on average 0.7%-1.7% of the cells in each sample. It is estimated that these 40-50 largest clusters represent cell types with roughly one copy per column (Kurmangaliyev, 2020).

In parallel to the main atlas, similar integrative analysis was performed for early time points (i.e., 0 and 12 h APF). These samples were integrated together with samples from 24 h APF to generate a separate atlas covering the first day of pupal development (from 0 to 24 h APF). Since cells from 24 h APF were present in both parts of the atlas, it was possible to match clusters between the two datasets. The differences between cell types were less prominent at earlier stages, and fewer cells were profiled. As a result, many related cell types were not resolved into separate clusters. For example, Tm1, Tm2, and Tm4 neurons and one unidentified cluster (N19) grouped together, suggesting a close developmental relationship between these cell types (Kurmangaliyev, 2020).

Transcriptional heterogeneity can reflect neuronal diversity beyond morphological cell types. For example, Dm8 neurons comprise two transcriptionally distinct subtypes with the same morphology but different synaptic specificities. New subtypes were found for Dm3, Tm9, and T4 neurons. Tm9 neurons, for instance, formed two clusters defined by mutually exclusive expression of Wnt4 and Wnt10 genes. Wnt10 expression in vivo was restricted to dorsal Tm9 neurons. It was concluded that identified clusters represent ventral (Wnt4+) and dorsal (Wnt10+) subtypes of Tm9 neurons. A stable subpopulation of T4 neurons was found expressing an uncharacterized gene (CG15537) throughout development that was enriched in T4 a/b subtypes. This group of cells did not form a separate cluster at the resolution of the analysis. The expression of CG15537, however, was restricted to T4 neurons targeting the dorsal third of the medulla and lobula plate layers a/b. The product of CG15537 is predicted to be a cell surface protein with a hormone binding domain. Among the other genes specific to the same subpopulation of T4 neurons are a neuropeptide receptor (TrissinR) and Tbh, a key component of the octopamine biosynthesis pathway (Kurmangaliyev, 2020).

It was hypothesizee that these neurons are a regional T4 subtype with distinct neuromodulatory characteristics. In this way, two cases of transcriptionally defined regional subtypes of neurons with unique spatial distributions were found and validated. The discovery of new subtypes, even for such well-studied cell types as T4/T5 neurons, underscores the power of single-cell sequencing to reveal subtle aspects of cellular diversity Taken together, a comprehensive transcriptional atlas of the developing Drosophila visual system was generated. This atlas covers more than 150 transcriptionally distinct neuronal populations, including most of the known abundant cell types. The one-to-one correspondence between transcriptional clusters and known morphological cell types, the expression of known markers, and the match with the expected proportions of cells indicate that this is a highly reliable resource for following individual cell types at multiple stages of development. 88 neuronal cell types were tracked every 12 h of pupal development from 24 to 96 h APF (minimum of 10 cells in either DGRP replicate). This largely encompasses developmental times preceding synapse formation through the formation of the mature connectome. 22 of these cell types were tracked in the early dataset (both at 0 and 12 h APF) covering all 100 h of pupal development. The cell-type-specific gene expression profiles were highly reproducible across replicates (Kurmangaliyev, 2020).

All neurons share similar properties (e.g., synaptic transmission and membrane excitability). Attempts were made to assess whether there was a common neuronal differentiation pathway giving rise to these features or whether they emerge in different ways in different neurons. To do so, 88 neuronal and 6 glial cell types were followed from 24 to 96 h APF. First, neuron-specific genes were defined that were highly enriched in neurons compared to glia. Genes known to be involved in neuronal development and function were enriched among neuron-specific genes (Kurmangaliyev, 2020).

Neuron-specific genes showed a wide range of temporal dynamics during development. Pearson's correlation coefficients were calculated between expression patterns of each gene across different cell types and averaged using Fisher's Z transformation. Many neuron-specific genes were expressed in a highly coordinated fashion across different cell types. It was hypothesizes that they are part of a common genetic program unfolding in all neurons in parallel (a 'pan-neuronal program'). Pan-neuronal genes were defined using the following criteria: (1) a gene was detected in at least half the neuronal cell types, and (2) the average Pearson's correlation coefficient was >0.75. In total, 200 genes met these criteria. The list of reported genes represents a subset of the top pan-neuronally genes with different levels of coordination. For instance, increasing the requirement for number of cell types in which gene was detected to 80% results in a slightly more conservative list of the genes (182 of 200 reported genes still pass this criterion) (Kurmangaliyev, 2020).

The core components of synapses and membrane excitability were among the most tightly coordinated groups of genes. These include components of the synaptic release machinery (e.g., Snap25 and nSyb), the presynaptic active zone (e.g., Brp and Rbp), and many ion channels (e.g., Cac and Para). Some of the neurotransmitter receptor subunits were also expressed in a highly coordinated fashion in most neurons (e.g., nAChRbeta1 and Rdl), and others were cell-type specific, with diverse temporal dynamics (e.g., nAChRalpha7). A number of genes implicated in synaptic function in more specific contexts were also regulated in a pan-neuronal fashion (e.g., Drep2). These included cell adhesion molecules (CAMs) that could contribute to synaptic adhesion complexes (Nrx-1, Hasp, and Lrp4). Other cell recognition molecules that contribute to connectivity in many contexts (e.g., Dscam1 and CadN) were also expressed pan-neuronally but were not specific to neurons or did not change appreciably during development. Other genes with similar expression patterns may represent uncharacterized constitutive components of synapses (Kurmangaliyev, 2020).

Most of the genes encoding presynaptic proteins were gradually upregulated during mid-pupal stages (36-48 h APF) and peaked 60 h APF. The timing and synchrony of this expression parallels the synchronous onset of patterned stimulus-independent neuronal activity (PSINA) in all neurons at 48 h APF. The gene encoding the gap junction protein ShakB is expressed pan-neuronally and peaks at mid-pupal stages (36- 60 h APF). This would be consistent with a role for gap junctions at early stages of PSINA; indeed, attenuating ShakB activity depresses PSINA throughout the developing visual system. Many other panneuronal genes were expressed transiently at particular stages of development. These patterns may pinpoint undescribed discrete steps in neuronal differentiation in all neurons (Kurmangaliyev, 2020).

The synchrony of pan-neuronal genes suggests a common regulatory mechanism underlying their developmental timing. Several TFs exhibited temporally specific and pan-neuronally coordinated expression patterns. Among these, Blimp-1 and Hr3 are controlled by EcR as part of the ecdysone regulated transcriptional cascade. Other genes in this cascade were also expressed in a tightly coordinated fashion in all neuron types, but they were not specific to neurons. Another steroid hormone receptor, Hr38, was also expressed largely in a pan-neuronal fashion and was restricted to the second half of pupal development. Hr38 is an activityregulated immediate early gen. It has also been shown to be indirectly activated by ecdysteroids, but independently of the EcR pathway. Other genes expressed in a pan-neuronal fashion also suggest a role for intercellular signaling in regulating the pan-neuronal program. These include two G-protein coupled receptors: DopEcR, a receptor for both ecdysone and dopamine, and SIFaR, a receptor for the conserved neuropeptide SIFamide (Kurmangaliyev, 2020).

Pan-neuronal genes can also be regulated at post-transcriptional levels. A number of RNA-binding proteins (RBPs) and long non-coding RNA (lncRNA) genes were pan-neuronally coordinated. For instance, Elav has been shown to function in neuron-specific splicing and lengthening of 30 untranslated regions (3' UTRs) of transcripts. Changes in 3' UTR length have been implicated in the regulation of RNA localization and translation, two prominent features of gene regulation in neurons. Two other Elav paralogs, Fne and Rbp9, implicated in synaptic growth at the larval neuromuscular junction), are also expressed pan-neuronally, peaking at mid-pupal stages. RBPs involved in translational regulation, such as Bol, are also expressed panneuronally. Bol is an inhibitor of axon pruning in the mushroom body but may act more broadly to modulate expression of a core set of neuronal genese (Kurmangaliyev, 2020).

Taken together, these data suggest that the expression of pan-neuronal genes may be regulated at both the transcriptional and post-transcriptional levels and may, in addition, be coordinated by intercellular signaling (Kurmangaliyev, 2020).

Neurons exhibit great diversity in their terminal morphologies, connectivity patterns, and physiological properties. Attempts were made to characterize differences in transcriptomes that could underlie the development of these cell-type-specific features of neurons (Kurmangaliyev, 2020).

To explore these differences, focus was placed on eight cell types representing different morphological and functional classes of neurons. Some of them represent pairs of anatomically related neurons with different connectivity patterns (e.g., C2/C3 and Tm1/Tm2). A set of 362 highly variable genes were defined that were differentially expressed among these cell types. They represented 12% of all expressed genes. Three classes, TFs, CAMs, and receptors with ligands, accounted for 41% of highly variable genes. TFs and CAMs were previously shown to be primary drivers of transcriptional diversity of neurons in different parts of the nervous system, both during development and in adults (Kurmangaliyev, 2020).

The expression patterns of TFs and CAMs were qualitatively different. Most TFs were expressed in a binary fashion across cell types (i.e., on/off manner) and were expressed throughout development. Closely related cell types often expressed similar combinations of TFs (e.g., C2/C3 cells). Each neuron expresses discrete combinations of TFs that define their cell type. CAMs were more volatile in their expression patterns. Many were expressed transiently during development, and their specificity could be switched between cell types at different time points. Moreover, quantitative differences in expression levels between neurons or in the same neuron at different developmental times were common. For example, dpr17 was expressed at higher levels but with different temporal patterns by Tm1 (36-48 h APF) and C3 cells (48-96 h APF) and at lower levels and with similar temporal patterns by Tm2 and Mi4 cells (72-84 h APF). Thus, neurons express repertoires of CAMs with continuous variation in their expression levels. These diverse and rapidly changing cell recognition landscapes mirror the changes in morphology and patterns of synaptic connections elaborated by different neurons (Kurmangaliyev, 2020).

Genes encoding neurotransmitter-specific functions exhibited interesting temporal patterns of expression. For example, the vesicular acetylcholine transporter (VAChT) is expressed exclusively by cholinergic neurons. However, the temporal dynamics and expression levels of VAChT are tightly coordinated between different types of cholinergic neurons. Thus, the choice of neurotransmitter is strictly cell-type specific, but the temporal patterns of expression are shared between different cell types (Kurmangaliyev, 2020).

Neurons also expressed genes involved in neuronal circuit functions in cell-type specific ways. These included genes encoding neurotransmitter biosynthetic enzymes, transporters, receptors, and ion channel subunits. These genes can define functional diversity of neurons in adult circuits. For example, different combinations of neurotransmitter receptor subunits could result in distinct properties of synaptic connections. Interestingly, some of the genes that control synaptic communication and membrane excitability in the adult were also expressed during development. They may regulate intercellular communication underlying circuit assembly, most notably during the second half of pupal development in which each neuron type exhibits unique patterns of neuronal activity (i.e., PSINA) (Kurmangaliyev, 2020).

Neuronal diversity is further shaped by differential expression of genes involved in post-transcriptional and post-translational mechanisms of gene regulation. This study identified a number of cell-type specific RBPs and lncRNAs. These genes could contribute to cell identity at post-transcriptional levels. For example, expression of the cell-type-specific RBP Muscleblind (Mbl) regulates alternative splicing of pan-neuronally expressed Dscam2 to generate isoforms with different recognition specificities. This study also identified several genes involved in post-translational modification of proteins, including glycosylation and proteolytic enzymes, which are expressed in a cell-type-specific fashion (Kurmangaliyev, 2020).

In summary, genetic programs of neuronal development comprise a common program of neuronal maturation regulating synapse formation and membrane excitability properties, overlaid by cell-type-specific programs encoding dramatically changing repertoires of cell surface proteins. These proteins regulate interactions between different neuron types controlling the assembly and specificity of connections between neurons (Kurmangaliyev, 2020).

Each neuron type in the fly visual system has a unique set of synaptic inputs and outputs and each of the different neuron types profiled expresses cell recognition proteins with different combinations, levels, and temporal profiles. This transcriptional atlas, in combination with the connectome, provides a resource to explore synaptic specificity mechanisms by combining it with developmental, biochemical, and genetic studies. As an example of how this approach may be applied, this study considered a simpler part of the connectome in the lamina and use a retrospective analysis to correlate expression data with previously described biochemical and genetic studies (Kurmangaliyev, 2020).

During early pupal development, L4 neurons extend dendrites that contact L1 and L2 axons. Later in pupal development, L4 selectively forms synapses with L2. Previous genetic studies have shown that three different families of cell recognition molecules of the immunoglobulin superfamily regulate this process. Dscam2 and Dscam4 are required for targeting of L4 dendrites to a bundled pair of L1 and L2 axons. Kirre, a synaptic adhesion protein, is required for synapses between L4 and L2, and DIP-β promotes L2 specificity. Previous biochemical studies have reported the binding specificities of Dscam2 and Dscam 4, the affinities of different Dprs for binding to DIP-beta, and the interaction of Kirre with its ligands (Kurmangaliyev, 2020).

The temporal expression patterns of these genes in L4 are predictive of their function. Dscam2 and Dscam4 are expressed during targeting. As their levels decrease, there is a concomitant increase in the expression of Kirre and DIP-β just prior to the onset of synaptogenesis. As the Kirre ligands (Kirre itself, Rst, and Hbs) are expressed in a similar way in L1 and L2, it is unlikely that Kirre alone determines specificity. By contrast, the expression patterns of DIP-β ligands (i.e., Dprs) and their affinities, however, would favor interactions between L4 and L2, consistent with the decrease in the fidelity of synaptic specificity seen in DIP-β mutants. As both the Kirre and DIP-b synaptic phenotypes are partial, other recognition molecules must act in a redundant fashion during synapse formation (Kurmangaliyev, 2020).

Attempts were made to address whether the atlas provides candidates for these redundant functions. The dataset is a rich resource for doing so, allowing following of the expression of cell surface proteins expressed by these neurons during synapse formation or, of course, in an analogous way for many other synaptic partners in the visual system. At the onset of synapse formation, eight pairs of recognition proteins (both heterophilic and homophilic) are expressed in both the synaptic partner L2/ L4 pair and in the L1/L4 pair, which do not form synapses. Several of these proteins are associated with synapse formation in flies and mice (e.g., N-cadherin), the L1 family protein Nrg, and the mammalian Igsf9b ortholog turtle, and these may act in a redundant way with Kirre to regulate synapse formation. As different Beat/Side protein family pairs are differentially expressed in L1 and L2, they may work in parallel to DIP/Dpr interactions to determine specificity. These interactions may promote synapses between L2/L4, inhibit inappropriate L1/L4 synapses, or both. These data support the idea that multiple protein pairs on opposing membranes may contribute to synapse formation between these neurons, and it is the differences in the cell-type-specific expression of highly diversified protein families (i.e., DIPs/Dprs and Beats/Sides) that bias the specificity of synapse formation toward L2 (Kurmangaliyev, 2020).

A similar level of complexity was observed in the potential interactions between cell surface proteins expressed by other neurons. For example, there are over a hundred neuron types that extend axons and dendrites into the medulla neuropil and they must discriminate between one another to select appropriate synaptic partners. Each neuron expresses many dozens of cell recognition molecules in a cell-type-specific and temporally dynamic fashion. These findings provide a molecular correlate for the modest phenotypes seen in many wiring mutants, alluding to marked redundancy. It raises the possibility that specificity may largely reflect biases in connectivity rather than all or none selectivity. That is, neurons may select synaptic partners not by a mechanism demanding an 'exact match' between cell surface molecules but rather by selecting the 'best match' among potential partners. This may confer robustness to the establishment of a complex stereotyped connectome. Unravelling the precise mechanisms by which this complexity in cellular recognition landscapes translates into highly specific patterns of synaptic connectivity will rely on future studies incorporating biochemical, genetic, and computational analyses (Kurmangaliyev, 2020).

Understanding how patterns of synaptic connections are determined during development remains a central question in neural development. This study followed the development of 88 different neuron types at many time points spanning synaptogenesis. In so doing, a pan-neuronal program was found running synchronously in all neurons regardless of cell type. This program was enriched, in particular, for synaptic proteins and proteins regulating electrically excitable membranes. The neuron-specific expression of pan-neuronal genes may be controlled by a common gene regulatory program. Alternatively, each cell type may regulate expression of these genes independently through a myriad of cell-type specific regulatory programs. The coordinated expression of these genes in all neurons may be controlled by a separate timing mechanism. Whatever the underlying mechanisms are for regulating this process in a similar way in all neurons, it is speculated that this program endows neurons with a broad potential to form synapses (Kurmangaliyev, 2020).

By contrast to the pan-neuronal program of development, the expression of cell recognition molecules is profoundly asynchronous and highly cell-type specific. This diversity mirrors cell-type-specific morphological changes of axons and dendrites in the target region and the complexity and specificity of synaptic connectivity each neuron elaborates. These rapid changes may be necessary to choreograph the specific interactions between the processes of many different neuron types as they seek to discriminate between appropriate and inappropriate synaptic partners and determine the number and distribution of synapses between them. Whether the dynamic expression pattern observed is largely cell autonomous in nature or whether it reflects a dialog between neurons during circuit assembly is unknown. Many signaling pathway components, including those regulated by neural activity, growth factors, neuropeptides, and ecdysteroids, all of which can influence gene expression, are expressed in these neurons (Kurmangaliyev, 2020).

Traditionally, circuit assembly has been studied genetically one gene at a time. Molecules to study were selected based upon their patterns of expression or phenotypes of mutants. In these approaches, investigators were largely ignorant of other recognition molecules expressed in these neurons. Phenotypes in almost all cases were incomplete and often embarrassingly mild, supporting a general consensus in the field that this reflects substantial redundancy, which is advantageous for constructing a robust circuit but a bane to researchers. Transcriptome profiling revealed that each neuron expresses a unique combination of a vast number of cell recognition molecules, many of which have the potential to promote interactions between them. These findings and many genetic studies underscore the confound of redundancy in studying the logic and molecular mechanisms underlying specificity. In a broad sense it is speculated that these proteins act together through common mechanisms and variations on them to sculpt circuitry. Uncovering the molecular logic of synaptic specificity will rely on combining transcriptomics with detailed studies of specific synapses using an increasingly sophisticated tool kit of genetic, biochemical, physiological, and imaging approaches (Kurmangaliyev, 2020).

The development of high-throughput single-cell technologies has led to an exponential increase in transcriptional studies in many different systems. Profiling of multiple experimental conditions with internal controls for technical variation in a single experiment contributes to these advances. The pooled strategy, described in this study, can be easily adapted for a variety of experimental designs, including comparative studies in different mutant backgrounds or in altered environments. In principle, similar approaches could be used in many other systems, where the genotype information could be obtained in parallel to transcriptional profiling. The rapid and economically feasible generation of temporally resolved transcriptional datasets can be used to tackle a variety of longstanding questions in developmental biology (Kurmangaliyev, 2020).

Spatial patterning of neural stem cell populations is a powerful mechanism by which to generate neuronal diversity. In the developing Drosophila medulla, the symmetrically dividing neuroepithelial cells of the outer proliferation center (OPC) crescent are spatially patterned by the non-overlapping expression of three transcription factors: Vsx1 in the center, Optix in the adjacent arms, and Rx in the tips. These spatial genes compartmentalize the OPC and, together with the temporal patterning of neuroblasts, act to diversify medulla neuronal fates. The observation that the dorsal and ventral halves of the OPC also grow as distinct compartments, together with the fact that a subset of neuronal types are generated from only one half of the crescent, suggests that additional transcription factors spatially pattern the OPC along the dorsal-ventral (D-V) axis. This study identified the spalt (salm and salr) and disco (discoand disco-r) genes as the D-V patterning transcription factors of the OPC. Spalt and Disco are differentially expressed in the dorsal and ventral OPC from the embryo through to the third instar larva, where they cross-repress each other to form a sharp D-V boundary. hedgehog is necessary for Disco expression in the embryonic optic placode and that disco is subsequently required for the development of the ventral OPC and its neuronal progeny. It was further demonstrated that this D-V patterning axis acts independently of Vsx1-Optix-Rx and thus propose that Spalt and Disco represent a third OPC patterning axis that may act to further diversify medulla fates (Valentino, 2022).

Neural circuit formation and function require that diverse neurons are specified in appropriate numbers. Known strategies for controlling neuronal numbers involve regulating either cell proliferation or survival. This study used the Drosophila visual system to probe how neuronal numbers are set. Photoreceptors from the eye-disc induce their target field, the lamina, such that for every unit eye there is a corresponding lamina unit (column). Although each column initially contains ~6 post-mitotic lamina precursors, only 5 differentiate into neurons, called L1-L5; the 'extra' precursor, which is invariantly positioned above the L5 neuron in each column, undergoes apoptosis. This study showed that a glial population called the outer chiasm giant glia (xg(O)), which resides below the lamina, secretes multiple ligands to induce L5 differentiation in response to epidermal growth factor (EGF) from photoreceptors. By forcing neuronal differentiation in the lamina, it was uncovered that though fated to die, the 'extra' precursor is specified as an L5. Therefore, two precursors are specified as L5s but only one differentiates during normal development. It was found that the row of precursors nearest to xg(O) differentiate into L5s and, in turn, antagonise differentiation signalling to prevent the 'extra' precursors from differentiating, resulting in their death. Thus, an intricate interplay of glial signals and feedback from differentiating neurons defines an invariant and stereotyped pattern of neuronal differentiation and programmed cell death to ensure that lamina columns each contain exactly one L5 neuron (Prasad, 2022).

Visual circuit development is characterized by subdivision of neuropils into layers that house distinct sets of synaptic connections. This study found that in the Drosophila medulla, this layered organization depends on the axon guidance regulator Plexin A. In plexin A null mutants, synaptic layers of the medulla neuropil and arborizations of individual neurons are wider and less distinct than in controls. Analysis of Semaphorin function indicates that Semaphorin 1a, provided by cells that include Tm5 neurons, is the primary partner for Plexin A in medulla lamination. Removal of the cytoplasmic domain of endogenous Plexin A does not disrupt the formation of medulla layers; however, both null and cytoplasmic domain deletion mutations of plexin A result in an altered overall shape of the medulla neuropil. These data suggest that Plexin A acts as a receptor to mediate morphogenesis of the medulla neuropil, and as a ligand for Semaphorin 1a to subdivide it into layers. Its two independent functions illustrate how a few guidance molecules can organize complex brain structures by each playing multiple roles. It is concluded that the axon guidance molecule Plexin A has two functions in Drosophila medulla development; morphogenesis of the neuropil requires its cytoplasmic domain, but establishing synaptic layers through Semaphorin 1a does not (Bustillo, 2023).

Cell fate and growth require one-carbon units for the biosynthesis of nucleotides, methylation reactions, and redox homeostasis, provided by one-carbon metabolism. Consistently, defects in one-carbon metabolism lead to severe developmental defects, such as neural tube defects. However, the role of this pathway during brain development and in neural stem cell regulation is poorly understood. To better understand the role of one carbon metabolism this study focused on the enzyme Serine hydroxymethyl transferase (Shmt), a key player of the one-carbon cycle, during Drosophila brain development. Although loss of shmt does not cause obvious defects in the central brain, it leads to severe phenotypes in the optic lobe. The shmt mutants have smaller optic lobe neuroepithelia partly justified by increased apoptosis. Additionally, shmt mutant neuroepithelia have morphological defects, failing to form a lamina furrow likely explaining the observed absence of lamina neurons. These findings show that one-carbon metabolism is critical for the normal development of neuroepithelia, and consequently for the generation of neural progenitor cells and neurons. These results propose a mechanistic role for one-carbon during brain development (Silva, 2023).

Notch-dependent binary fate choice between sister neurons is one of the mechanisms to generate neural diversity. How these upstream neural fate specification programs regulate downstream effector genes to control axon targeting and neuropil assembly remains less well understood. This tudy reports that Notch-dependent binary fate choice in Drosophila medulla neurons is required to regulate the Netrin axon guidance pathway, which controls targeting of transmedullary (Tm) neurons to lobula. In medulla neurons of Notch-on hemilineage composed of mostly lobula-targeting neurons, Notch signaling is required to activate the expression of Netrin-B and repress the expression of its repulsive receptor Unc-5. Turning off Unc-5 is necessary for Tm neurons to target lobula. Furthermore, Netrin-B provided by Notch-on medulla neurons is required for correct targeting of Tm axons from later-generated medulla columns. Thus, the coordinate regulation of Netrin pathway components by Notch signaling ensures correct targeting of Tm axons and contributes to the neuropil assembly (Zhang, 2023).

The Drosophila visual center shows columnar structures, basic structural and functional units of the brain, that are shared with the mammalian cerebral cortex. Visual information received in the ommatidia in the compound eye is transmitted to the columns in the brain. However, the developmental mechanisms of column formation are largely unknown. The Irre Cell Recognition Module (IRM) proteins are a family of immunoglobulin cell adhesion molecules. The four Drosophila IRM proteins are localized to the developing columns, the structure of which is affected in IRM mutants, suggesting that IRM proteins are essential for column formation. Since IRM proteins are cell adhesion molecules, they may regulate cell adhesion between columnar neurons. To test this possibility, this study specifically knocked down IRM genes in columnar neurons and examined the defects in column formation. A system was developed that automatically extracts the individual column images and quantifies the column shape. Using this system, it was demonstrated that IRM genes play critical roles in regulating column shape in a core columnar neuron, Mi1. It was also shown that their expression in the other columnar neurons, Mi4 and T4/5, is essential, suggesting that the interactions between IRM proteins and multiple neurons shape the columns in the fly brain (Lee, 2022).

How the human brain generates diverse neuron types that assemble into precise neural circuits remains unclear. Using Drosophila lamina neuron types (L1-L5), this study showed that the primary homeodomain transcription factor (HDTF) brain-specific homeobox (Bsh) is initiated in progenitors and maintained in L4/L5 neurons to adulthood. Bsh activates secondary HDTFs Ap (L4) and Pdm3 (L5) and specifies L4/L5 neuronal fates while repressing the HDTF Zfh1 to prevent ectopic L1/L3 fates (control: L1-L5; Bsh-knockdown: L1-L3), thereby generating lamina neuronal diversity for normal visual sensitivity. Subsequently, in L4 neurons, Bsh and Ap function in a feed-forward loop to activate the synapse recognition molecule DIP-β, thereby bridging neuronal fate decision to synaptic connectivity. Expression of a Bsh:Dam, specifically in L4, reveals Bsh binding to the DIP-β locus and additional candidate L4 functional identity genes. It is proposed that HDTFs function hierarchically to coordinate neuronal molecular identity, circuit formation, and function. Hierarchical HDTFs may represent a conserved mechanism for linking neuronal diversity to circuit assembly and function (Xu, 2024a).

HDTFs are evolutionarily conserved factors in specifying neuron-type specific structure and function. In C. elegans, some HDTFs function as terminal selectors, controlling the expression of all neuronal identity genes and diversifying neuronal subtypes, while other HDTFs act downstream of terminal selectors to activate a subset of identity genes. This study shows that the Bsh primary HDTF functions for L4/L5 fate specification by promoting expression of the Ap and Pdm3 secondary HDTFs and suppressing the HDTF Zfh1 to inhibit ectopic L1/L3 fate, thereby generating lamina neuronal diversity. In L4, Bsh and Ap act in a feed-forward loop to drive the expression of synapse recognition molecule DIP-β, thereby bridging neuronal fate decision to synaptic connectivity. DamID data provides support for several hundred Bsh direct binding targets that also show enriched expression in L4 neurons; these Bsh targets include predicted and known L4 identity genes as well as pan-neuronal genes, similar to the regulatory logic first observed in C. elegans. HDTFs are widely expressed in the nervous system in flies, worms, and mammals. By characterizing primary and secondary HDTFs according to their initiation order, it may be possible decode conserved mechanisms for generating diverse neuron types with precise circuits assembly (Xu, 2024a).

How can a single primary HDTF Bsh activate two different secondary HDTFs and specify two distinct neuron fates: L4 and L5? In an accompanying work (Xu, 2024b), Notch signaling was shown to be activated in newborn L4 but not in L5. This is not due to an asymmetric partition of a Notch pathway component between sister neurons, as is common in most regions of the brain, but rather due to L4 being exposed to Delta ligand in the adjacent L1 neurons; L5 is not in contact with the Delta+ L1 neurons and thus does not have active Notch signaling. While Notch signaling and Bsh expression are mutually independent, Notch is necessary and sufficient for Bsh to specify L4 fate over L5. The NotchON L4, compared to NotchOFF L5, has a distinct open chromatin landscape which allows Bsh to bind distinct genomic loci, leading to L4-specific identity gene transcription. It is proposed that Notch signaling and HDTF function are integrated to diversify neuronal types (Xu, 2024a).

DamID (this work) and a scRNAseq dataset were used to identify genomic loci containing both Bsh direct binding sites and L4-enriched expression. Genes that have Bsh:Dam binding peaks but are not detected in L4 scRNA sequencing data at 48h or 60h APF might be due to the following reasons: they are transcribed later, at 60h - 76h APF; the algorithm that was used to detect Bsh:Dam peaks and call the corresponding genes is not 100% accurate; some regulatory regions are outside the stringent +/− 1 kb association with genes; Bsh may act as transcription repressor; TFs generally act combinatorially as opposed to alone and that many required specific cooperative partner TFs to also be bound at an enhancer for gene activation; and scRNAseq data is not 100% accurate for representing gene transcription (Xu, 2024a).

Does the primary HDTF Bsh control all L4 neuronal identity genes? It seems likely, as Bsh:Dam shows binding to L4-transcribed genes that could regulate L4 neuronal structure and function, including the functionally validated synapse recognition molecule DIP-β. Furthermore, Bsh and Ap were found to form a feed-forward loop to control DIP-β expression in L4 neurons. Similarly, in C. elegans, terminal selectors UNC-86 and PAG-3 form a feed-forward loop with HDTF CEH-14 to control the expression of neuropeptide FLP-10, NLP-1 and NLP-15 in BDU neurons, suggesting an evolutionarily conserved approach, using feed-forward loops, for terminal selectors to activate neuronal identity genes. An important future direction would be testing whether Bsh controls the expression of all L4 identity genes via acting with Ap in a feed-forward loop. One intriguing approach would be profiling the Ap genome-binding targets in L4 during the synapse formation window and characterizing the unique and sharing genome-binding targets of Bsh and Ap in L4 neurons. Further, it would be interesting to test whether the primary HDTF Bsh functions with Ap to maintain neuron type-specific morphology, connectivity, and function properties in adults (Xu, 2024a).

Newborn neurons are molecularly distinct prior to establishing their characteristic morphological or functional attributes. The primary HDTF Bsh was discovered to be specifically expressed in newborn L4 and L5 neurons and is required to specify L4 and L5 fates, suggesting that identifying differentially expressed factors in newborn neurons is essential to decoding neuron type specification. It is noted that primary and secondary TFs may be HDTFs as well as non-HDTFs. For example, the primary HDTF Zfh1 is required to activate Svp in L1 and Erm in L3, neither of which are HDTF, though Erm has a significant function in L3 axon targeting. This suggests that the primary HDTF can activate non-HDTFs to initiate neuron identity features. Recent work in Drosophila medulla found that a unique combination of TFs (a mix of HDTFs and non-HDTFs) is required to control neuron identity features. It would be important to dissect whether there is hierarchical expression and function within these TF combinations and to test whether HDTFs activate non-HDTFs (Xu, 2024a).

Evolution can drive a coordinated increase in neuronal diversity and functional complexity. It is hypothesized that there was an evolutionary path promoting increased neuronal diversity by the addition of primary HDTF Bsh expression. This is based on the finding that the loss of a single HDTF (Bsh) results in reduced lamina neuron diversity (only L1-3), which may represent a simpler ancestral brain. A similar observation was described in C. elegans where the loss of a single terminal selector caused two different neuron types to become identical, which was speculated to be the ancestral ground state, suggesting phylogenetically conserved principles observed in highly distinct species. An interesting possibility is that evolutionarily primitive insects, such as silverfish, lack Bsh expression and L4/L5 neurons, retaining only the core motion detection L1-L3 neurons. These findings provide a testable model whereby neural circuits evolve more complexity by adding the expression of a primary HDTF (Xu, 2024a).

This study investigated 19 types of putative feature selective lobula columnar (LC) neurons in the optic lobe of the fruit fly Drosophila melanogaster to characterize divergent properties of feature selection. LC12 and LC15 were identified as feature detectors. LC15 encodes moving bars, whereas LC12 is selective for the motion of discrete objects, mostly independent of size. Neither is selective for contrast polarity, speed, or direction, highlighting key differences in the underlying algorithms for feature detection and motion vision. The onset of background motion was shown to suppress object responses by LC12 and LC15. Surprisingly, the application of octopamine, which is released during flight, reverses the suppressive influence of background motion, rendering both LCs able to track moving objects superimposed against background motion. These results provide a comparative framework for the function and modulation of feature detectors and new insights into the underlying neuronal mechanisms involved in visual feature detection (Stadele, 2020).

Visual systems of many animals, including the fruit fly Drosophila, represent the surrounding space as 2D maps, formed by populations of neurons. Advanced genetic tools make the fly visual system especially well accessible. However, in typical in vivo preparations for two-photon calcium imaging, relatively few neurons can be recorded at the same time. This study presents an extension to a conventional two-photon microscope, based on remote focusing, which enables real-time rotation of the imaging plane, and thus flexible alignment to cellular structures, without resolution or speed trade-off. This study simultaneously recorded from over 100 neighboring cells spanning the 2D retinotopic map. The map representation of moving natural images, which is found is comparable to noise predictions, was characterized. This method increases throughput 10-fold and allows visualization of a significant fraction of the fly's visual field. Furthermore, this system can be applied in general for a more flexible investigation of neural circuits (Schuetzenberger, 2020).

Topographic maps, the systematic spatial ordering of neurons by response tuning, are common across species. In Drosophila, the lobula columnar (LC) neuron types project from the optic lobe to the central brain, where each forms a glomerulus in a distinct position. However, the advantages of this glomerular arrangement are unclear. This study examine the functional and spatial relationships of 10 glomeruli using single-neuron calcium imaging. Novel detectors were discovered for objects smaller than the lens resolution (LC18) and for complex line motion (LC25). Glomeruli are spatially clustered by selectivity for looming versus drifting object motion and ordered by size tuning to form a topographic visual feature map. Furthermore, connectome analysis shows that downstream neurons integrate from sparse subsets of possible glomeruli combinations, which are biased for glomeruli encoding similar features. LC neurons are thus an explicit example of distinct feature detectors topographically organized to facilitate downstream circuit integration (Klapoetke, 2022).

Topographic maps, where neurons form ordered representation of the external world, have been long observed in many diverse brain regions. The prevalence of these maps has been suggested to support efficient organization of local circuit computation. In the early visual system, the vertebrate retina and insect optic lobe share many anatomic and functional similarities in encoding visual information retinotopically, and both output information to higher-order brain regions via visual projection neurons. The LC visual projection cells of flies, similar to retinal ganglion cells in vertebrates, have been hypothesized to encode behaviorally relevant local visual features. In Drosophila melanogaster, there are ∼20 anatomically distinct LC types (including LC-like visual projection cell types called lobula plate lobula columnar, or LPLC, neurons, each comprising a population of between 30 and 220 neurons. The dendrites of individual member neurons within an LC type tile retinotopic space in the optic lobe, but the axons of a given type collectively project to a target region in the fly's central brain called an optic glomerulus. The nonoverlapping optic glomeruli form a highly ordered, anatomically identifiable set in the central brain. However, the visual feature sensitivities of the different glomeruli, and whether the stereotyped anatomical glomerular arrangement relates to functional tuning, is not well understood. To date, feature encoding properties and behavioral links have been identified for only a handful of LC types. This study systematically explored the diversity of visual feature encoding by a broad set of LC neurons and further explored their connectivity onto downstream target neurons in order to establish how the anatomical arrangement of the glomeruli relates to their function and readout (Klapoetke, 2022).

Systematic survey of LC visual projection neuron response properties reveals a topographic visual feature map in the fly central brain. LC axon terminals bundle into discrete central brain glomeruli by cell type, and each glomerulus is distinctly tuned to a visual moving object class, with neighboring glomeruli more similarly tuned than distant ones. Indeed, the physical arrangement of the glomeruli is largely recapitulated by sorting LC neuron responses by their first two principal components, which roughly correspond to selectivity for objects versus looming motion and object size tuning (Klapoetke, 2022).

The LCs that were measured all have distinct visual feature tunings across object size and speed, with each cell type's preferred object size generally eliciting large responses across one order of magnitude of movement speeds. Whether size selectivity is preserved across different speeds in LC11 and LC18 responses was further examined, and it was observed that both cell types retain selectivity to moving small objects, but the peak size sensitivity shifts to smaller sizes at slower speeds and to bigger sizes at higher speed. It is noteworthy that this trend for peak sensitivity shifting to slightly larger sizes at faster motion speed may have a simple mechanistic basis (a larger, faster moving object can have a similar stimulus dwell time as a smaller, slower moving object). This peak sensitivity shift is also consistent with the typical perceived size-speed relationship at different depths (such as motion parallax cues) because at a distance, an object will appear to be smaller and to move across the retina more slowly but will become both faster and larger as it approaches the observer (Klapoetke, 2022).

A minimal model was generated that recapitulates LC18's size tuning to objects smaller than an individual ommatidium's lens resolution using common circuit motifs such as differing contrast responses to ON and OFF signals and crossover inhibition between the ON and OFF pathways. Because the hemibrain connectome is truncated in the optic lobe, it was not possible to systematically examine the inputs to LC18 to identify whether the propoaws circuit motifs exist in the connectome. Previous studies have indicated that LC11 neurons, which also detect small objects, receive inputs from T2 cells. The T2 neurons have distinctive axonal terminal morphology, which allowed identification of individual putative T2 neurons as inputs to both LC11 and LC18 in the hemibrain. Interestingly, these same putative T2 neurons are strong inputs to LC4 and LPLC1, which have size tunings distinct from both LC11 and LC18. These findings suggest that the diverse feature tuning observed across LC types is unlikely to be inherited directly from the properties of the input neurons. Understanding the cell-type specific integration of distinct combinations of input cell types, including inhibitory inputs, will be required to account for the size and speed tuning differences between LC11, LC18, and other LCs (Klapoetke, 2022).

What can the visual feature map that this study uncovered tell about what Drosophila perceive? Eemarkably precise encoding of particular features was found including looming detection (LPLC2), very small objects (LC18), and thin lines (LC25). It is unlikely that these features, especially in isolation, can be meaningfully interpreted to drive a behavioral response. Indeed, activation of LC18 and LC25 neurons did not produce any detectable behavior change in a previous study (although LPLC2 activation drives a takeoff jump). Rather, it may be necessary to integrate multiple visual features to generate a more reliable estimate of external moving objects. For example, LPLC2's signals are integrated together with LC4's by a downstream descending neuron that serves to detect approaching objects, such as predators, and mediates a visual-looming-evoked takeoff jump (Klapoetke, 2022).

Consistent with this expectation, the readout across LC neurons by downstream cells was examined in the central brain, and half of these strongly connected targets integrated their inputs from more than one LC type. These downstream neurons typically integrate from only a small fraction of the possible combinations of glomeruli, and these combinations are highly biased to neighboring glomeruli, with similar feature encoding. The downstream neurons are a highly diverse set, with cells that output to other central brain regions, provide feedback to the optic lobe, project to the contralateral hemisphere, or even directly project to the ventral nerve cord. Furthermore, many downstream targets that only integrate from a single LC type project contralaterally, suggesting that binocular interactions are a substantial feature of the glomeruli, as they are for circuits downstream of LC6, which have previously been explored (Klapoetke, 2022).

Although this analysis focused exclusively on the projection neurons from the lobula to the ventrolateral central brain, there is another prominent lobula visual projection, the LC10 neurons, that target a more dorsal central brain area called the AOTU. LC10 neurons have distinct, but overlapping, sensitivity to visual features, especially for moving objects, from the ventrolateral-projecting LCs. The LC10 axon terminals also feature prominent retinotopic organization within the AOTU, which is quite different from the other optic glomeruli. Furthermore, LC10 neurons are critical for visual tracking during courtship behavior, whereas other LCs are involved in locomotor behaviors such as walking forward, turning, freezing, or escape takeoff. Future work should evaluate whether LC10 pathways ultimately converge with neurons downstream of the other LCs to coordinate diverse behaviors guided by these different visual pathways (Klapoetke, 2022).

Circuit level analysis of feature integration has been limited by the lack of detailed connectivity information between neurons with known feature encoding. In the fly brain, most central projections of visual pathways are not yet characterized. By functionally characterizing a large set of spatially adjacent visual projection neuron types and then taking advantage of the recent central brain connectome, this study found that the integration of LC visual features is highly selective, with 33% of possible feature pairs observed. Furthermore, these feature combinations are highly biased for nearby glomeruli, suggesting local circuit wiring economy as a likely driving force for the glomeruli spatial positions. Such structured integration serves as an exemplar for how brains can organize sensory information into ordered feature maps to compose diverse pathways that represent critical conduits between vision and behavioral controls (Klapoetke, 2022).

Tetrad synapses are formed between the retina photoreceptor terminals and postsynaptic cells in the first optic neuropil (lamina) of Drosophila. They are remodelled in the course of the day and show distinct functional changes during activity and sleep. These changes result from fast degradation of the presynaptic scaffolding protein Bruchpilot (BRP) by Cryptochrome (CRY) in the morning and depend on BRP-170, one of two BRP isoforms. This process also affects the number of synaptic vesicles, both clear and dense-core, delivered to the presynaptic elements. In cry⁰¹ mutants lacking CRY and in brp^Δ170, the number of synaptic vesicles is lower in the morning peak of activity than during night-sleep while in wild-type flies the number of synaptic vesicles is similar at these two time points. CRY may also set phase of the circadian rhythm in plasticity of synapses. The process of synapse remodelling stimulates the formation of clear synaptic vesicles in the morning. They carry histamine, a neurotransmitter in tetrad synapses and seem to be formed from glial capitate projections inside the photoreceptor terminals. In turn dense-core vesicles probably carry synaptic proteins building the tetrad presynaptic element (Damulewicz, 2020).

The results confirmed earlier studies that the presynaptic element (T-bar) of tetrad synapses is remodelled during the day and night and this rhythm is regulated by light and circadian clock (Gorska-Andrzejak, 2013; Woznicka, 2015). In the present study, it was found additionally that cyclic changes occur in the T-bar ultrastructure and its volume. Ultrastructural changes in T-bars and synaptic vesicles were possible because of using high resolution electron microscope tomography (EMT). In the present study it was also found that the number of synaptic vesicles cycles during the day, but this rhythm is masked by light. This result indicates that in the case of tetrad synapses, which are sites of fast neurotransmission between photoreceptors and the first order interneurons, intense neurotransmission occurs during the morning peak of locomotor activity, when more synaptic vesicles are attached to the T-bar platform than during sleep (ZT16) and are transported from capitate projections located next to the T-bars. At ZT16, tetrad synapses are ready for neurotransmission, since the total number of synaptic vesicles near tetrad synapses is similar at ZT1 and ZT16, but it is in a standby mode with lower frequency of synapses and vesicles, which are not attached to the T-bar platform and are not delivered from glial cells, respectively. However, transmission can be activated and is efficient because synapses during sleep have larger volume, and synaptic vesicles can be transported to the T-bar platform, if necessary, in response to an unexpected light pulse. Cyclic remodelling of tetrad presynaptic sites depends on BRP, which must be delivered to T-bars and degraded after light exposure after binding to CRY. This fast remodelling of synapses affects the number of vesicles transported to the presynaptic element (Damulewicz, 2020).

The present study was carried out only in light/dark (LD 12:12) conditions because in earlier studies it was found that the rhythm of the changes in BRP abundance in tetrad synapses of D. melanogaster is circadian. The rhythm is maintained in constant darkness (DD) and abolished in the per⁰¹ clock null mutant. On the basis of these results, it is assumed that rhythmic changes during the 24 h cycle reported is this paper are also circadian; however, in DD, the morning peak in BRP is not present because it depends on light (Damulewicz, 2020).

Electron microscope tomography (EMT) used in this study showed that synaptic vesicles are attached to the platform of the T-bar with filamentous proteins that are reduced in brp^Δ170 and brp^Δ190 mutants, which have fewer synaptic vesicles compared with wild-type flies. Two types of vesicles, clear and dense-core were detected. Dense-core synaptic vesicles were less numerous than clear ones. It is known that synaptic vesicles of tetrad synapses contain histamine, while the content of dense-core vesicles is unknown. It is possible that they carry presynaptic proteins to the T-bar. The comparison of ultrastructure of tetrad synapses in the morning peak of activity (ZT1) and during sleep (ZT16) indicates that more vesicles are attached to the T-bar platform and to capitate projections at ZT1, but this pattern is not present in brpΔ170, brp^Δ190 and cry⁰¹ . This result confirmed an earlier study that both high motor activity in the morning and light exposure increase activity of the visual system. In the morning, there is intense transport of synaptic vesicles to T-bars and delivery of histamine in clear vesicles from the epithelial glial cells through capitate projections. The evidence for a role of capitate projections in neurotransmitter recycling has already been reported and now this study showed, using EMT, that vesicles are produced from capitate projections and directly delivered to T-bars. This intense transport is damaged in all mutants studied, suggesting that both BRP isoforms and CRY are needed for this process. In addition, in the brp^Δ190 mutant lacking BRP-190, the T-bar structure is less dense than in the other strains studied. In a previous study, it was found that there are approximately 50% fewer synapses in brp^Δ190 than in Canton S and brp^Δ170. Another study reported that BRP isoforms are important for the formation of T-bars in neuromuscular junctions, and in brp mutants T-bars are smaller than in controls. T-bar height was reduced in brp^Δ190, whereas the widths of pedestal and platform were reduced in both mutants. They also decrease transmission since the active zone was smaller in both mutants and the number of synaptic vesicles was reduced. These ultrastructural changes are correlated with cell physiology since the amplitude of evoked excitatory junctional current was decreased in both mutants with a stronger effect in brp^Δ190 (Damulewicz, 2020).

The obtained reconstructions of tetrad T-bars from TEM serial sections of the lamina showed that although there were fewer synapses during the night (ZT16), the volume of the T-bar was larger at that time than in the morning (ZT1), while the total number of synaptic vesicles was similar. In contrast, a day/night difference (ZT1 vs. ZT16) in the number of vesicles was observed in brp^Δ170 and cry⁰¹ . This suggests that the CRY protein and BRP-170 are responsible for an increase in the number of synaptic vesicles during the morning peak of activity. Since CRY co-localizes with BRP and is involved in BRP degradation in the morning, CRY is probably also involved in the degradation of other proteins of synaptic vesicle organization in the morning since the number of vesicles in cry⁰¹ was low in the morning but high at night (ZT16). It seems that in cry⁰¹, synaptic vesicles are not delivered to the photoreceptor terminals from capitate projections and tethered to the cytomatrix in the morning. It is also interesting that the daily rhythm in the number of vesicles is not maintained in BRP mutants, which confirms an earlier study, and the BRP N-terminus, which lacks brp^Δ190, is necessary to maintain daily remodelling of the T-bar structure. Although both isoforms participate in building the cytomatrix their functions seem to be different in the course of the day. It is also possible that CRY is not only responsible for degradation of synaptic proteins but also as a protein, what is known, affecting the clock. In another cell types, in clock neurons l-LNvs, CRY, except interaction with TIM, is responsible for blue light response and firing of the l-LNvs. In the lamina it was found that in cry⁰¹ mutant the daily rhythm in synapse number and their remodelling was delayed in phase and the day/night difference in Canton S increased when peaks in the number of synapses were shift to ZT4 and ZT16. In result the difference between ZT1 and ZT16 was increased in cry⁰¹ (Damulewicz, 2020).

BRP is also responsible for rapid remodelling of the presynaptic active zone (AZ), and as reported in Drosophila NMJ, presynaptic homeostatic potentiation increases the number of BRP molecules and other AZ proteins, Unc13A and RBP, within minutes (Damulewicz, 2020).

When synaptic vesicles were counted at two different distances from the T-bar, to 200 nm and above 200 nm, there were differences between clear vesicles containing histamine and dense-core ones located in both areas. More clear vesicles were located near the T-bar and fewer above 200 nm. In the case of dense-core vesicles, their number was similar in both areas. This difference was not so striking in mutants in the case of vesicles located next to the platform, but in brp^Δ170 and cry⁰¹, there were more dense-core vesicles at ZT16 in the distance above 200 nm than closer to the T-bar. This result indicates that BRP-170 and CRY are important for the distribution of clear synaptic vesicles next to the T-bar as well as dense-core vesicles located above 200 nm from the presynaptic element. It is possible that dense-core vesicles contain T-bar proteins, probably BRP. When transport along the actin cytoskeleton is disrupted, the number of tetrad synapses decreases, and rapid AZ remodelling also fails (Damulewicz, 2020).

The above mentioned ultrastructural changes depend on the level of the presynaptic scaffolding protein BRP, which changes in abundance during the day and night. These changes are controlled by the daily expression of CRY, which seems to have many functions in photoreceptors in addition to being the circadian clock photoreceptor. In an earlier study, it was found that CRY interacts with BRP but only during light exposure and leads to the degradation of BRP during the day/light phase of the 24 h cycle. This seems to be responsible for the decrease in BRP level in the middle of the day after its peak at the beginning of the day. The lack of CRY in cry⁰¹ mutants changes the pattern of the tetrad presynaptic profile frequency during the day and the size of the T-bar. However, the rhythm is not completely abolished, which indicates that other proteins are also involved in the daily remodelling of tetrad synapses. Since CRY plays several functions in photoreceptors, changes in the number of tetrad synapse and T-bar size in cry⁰¹ may result from different processes and lack of interactions of CRY with TIM and BRP. CRY is a component of the molecular clock and interacts with TIM, and this may affect daily changes in the number and size of T-bars. In addition, light-activated CRY binds BRP and targets it to degradation. Previous work showed that flies with constitutively active CRY have low BRP level. In turn, cry⁰¹ mutants show changes in the pattern of BRP expression, with higher BRP level during the day (ZT4), at the time when wild-type flies have minimum of BRP expression. The pattern of BRP expression is similar to the pattern of daily changes in tetrad synapse number, so it is possible that the number of synapses or T-bar size is directly dependent on the amount of BRP which oscillates during the day. However, CRY in the retina photoreceptors binds also actin and is involved in the organization of phototransduction cascade of proteins in rhabdomeres38. This may be also involved in the regulation of T-bar structure. The differences in T-bar size of cry⁰¹ are shown at time when in Canton S CRY is active (during the day) or its level increases (ZT16). At the beginning of the night the level of CRY is very low, so there was no effect on T-bar structure and no difference between CS and cry⁰¹ was observed (Damulewicz, 2020).

The epithelial glial cells are important for many processes during phototransduction and in recycling neurotransmitters and other compounds during the night. Glia take up histamine and metabolize it to carcinin, which is next delivered to the photoreceptor terminals, and capitate projections are involved in this process. Activity of glial cells is also controlled by the circadian clock. During the night, glial cells seem to be more active than neurons to recycle neurotransmitters, and many proteins, including proteins of ion pumps, are found at higher concentrations at that time. The high number of synaptic vesicles near the tetrad T-bar during the morning peak of activity in Drosophila seems to depend on capitate projections invaginating from the epithelial glia to the photoreceptor terminals in the lamina of Drosophila (Damulewicz, 2020).

Although the presynaptic cytomatrix can be rapidly remodelled with transmission strength, it is also affected by motor and visual system activity, external factors, such as light in the case of the visual system, and the circadian clock, showing plasticity and correlation to changes in behaviour during the day/night cycle. As was shown in the present study synaptic plasticity and synapse remodelling during the day is a complex process which involves presynaptic proteins of the T-bar as well as two types of synaptic vesicles, clear and dense-core, and glial cells. It was also found that fast degradation of proteins involved in transmission is as important as pre- and postsynaptic protein synthesis (Damulewicz, 2020).

Mesencephalic Astrocyte-derived Neurotrophic Factor (MANF) is one of a few neurotrophic factors described in Drosophila melanogaster (DmMANF) but its function is still poorly characterized. The present study found that DmMANF is expressed in different clusters of clock neurons. In particular, the PDF-positive large (l-LNv) and small (s-LNv) ventral lateral neurons, the CRYPTOCHROME-positive dorsal lateral neurons (LNd), the group 1 dorsal neurons posterior (DN1p) and different tim-positive cells in the fly's visual system. Importantly, DmMANF expression in the ventral lateral neurons is not controlled by the clock nor it affects its molecular mechanism. However, silencing DmMANF expression in clock neurons affects the rhythm of locomotor activity in light:dark and constant darkness conditions. Such phenotypes correlate with abnormal morphology of the dorsal projections of the s-LNv and with reduced arborizations of the l-LNv in the medulla of the optic lobe. Additionally, it was shown that DmMANF is important for normal morphology of the L2 interneurons in the visual system and for the circadian rhythm in the topology of their dendritic tree. These results indicate that DmMANF is important not only for the development of neurites but also for maintaining circadian plasticity of neurons (Krzeptowski, 2021).

One hallmark of the visual system is a strict retinotopic organization from the periphery toward the central brain, where functional imaging in Drosophila revealed a spatially accurate representation of visual cues in the central complex. This raised the question how, on a circuit level, the topographic features are implemented, as the majority of visual neurons enter the central brain converge in optic glomeruli. This study discovered a spatial segregation of topographic versus nontopographic projections of distinct classes of medullo-tubercular (MeTu) neurons into a specific visual glomerulus, the anterior optic tubercle (AOTU). These parallel channels synapse onto different tubercular-bulbar (TuBu) neurons, which in turn relay visual information onto specific central complex ring neurons in the bulb neuropil. Hence, these results provide the circuit basis for spatially accurate representation of visual information and highlight the AOTU's role as a prominent relay station for spatial information from the retina to the central brain (Timaeus, 2020).

Like various other sensory modalities for which spatial information is critical, neural circuits in the visual system of many animals are organized in a topographic fashion to maintain the neighboring relationship of adjacent pixels detected by photoreceptors in the periphery, along the visual pathways into the central brain. The topographic representation of different kinds of sensory information within the central brain of Drosophila is currently being investigated using molecular genetic tools in combination with cell-type-specific driver lines. Although it is well known that spatially patterned visual stimuli induce coherent activity bumps in the Drosophila CX, the pathway translating peripheral visual information into central activity patterns remains poorly understood (Timaeus, 2020).

This study has shown that medulla inputs to the AOTU fall into two morphological types regarding their arborization patterns: broad innervation versus spatially restricted axon terminals. In both cases, only a single domain within the AOTU is targeted. Although the topographic representation from the lobula neuropil is mostly lost in the broad innervation pattern of converging and intermingling LC projection neurons onto the majority of optic glomeruli, a unique spatial organization could be identified for the output channel from the medulla. Topographic representation of the medulla (at least its dorsal half, where most driver lines used in this study are expressed) is maintained in the SU of the AOTU, which is spatially separated from lobula representation within the AOTU (the LU). Interestingly, a strict topographic correlation only exists between the a-p position of the dendritic fields of MeTu projection neurons in the medulla and their restricted axon termination along the d-v axis within distinct domains of the SU in the AOTU. No such topography exists along the d-v axis in the medulla. These neurons are therefore well suited for filtering out specific visual information (such as landmarks or celestial bodies) for guiding heading decisions during visually guided navigation (Timaeus, 2020).

Based on their morphology, as well as their molecular identity, three principle types of MeTu neurons provide input into the AOTU, with overlapping dendritic fields within the medulla but segregated axon terminals to distinct AOTU (sub-)domains. MeTu-l and -c classes have a similar neuronal morphology with dendrite arborization restricted to a single medulla layer (M6) and spatially narrow axon termination areas in four separate AOTU subdomains (SU-la, -lp, -ca, and -cp), thereby building several pathways arranged in parallel. The nomenclature of the SU subdomain organization differs slightly from previous studies, because it is now based on the expression patterns of different cell surface molecules, which might reflect the functional organization of these structures. Because of this new classification, both lateral and central domains (but not the medial domain) of the SU become further subdivided into anterior (SU-la and SU-cp) and posterior halves (SU-l_p and SU-cp). Nevertheless, it should be noted that the total number of subdomains remains the same in both nomenclatures, with the major difference being the posterior-lateral subdomain ('lp'), which has been attributed to the central domain (SU-cp) in this study, as part of the Connectin-positive central neuropil. Based on the connectome reconstruction of the hemibrain dataset, which reports in a total number of 347 MeTu neurons ('MC61-type'), it is estimated ∼60 MeTu neurons per topographic class (and twice that for MeTu-m cells), assuming an equal innervation of SU subdomains of similar volume. Because 8-12 TuBu neurons were counted from three independent expression lines, a convergence ratio from MeTu to TuBu neurons of about 8:1 to 5:1. Only the organization of the MeTu-l and MeTu-c neurons clearly enables a spatial projection of visual information from the columnar organization in the medulla to the corresponding AOTU domains, which seems well suited to relay topographic information along one spatial axis toward the central brain (Timaeus, 2020).

The borders of the SU compartments are respected by molecularly defined populations of TuBu neurons, thereby defining the next synaptic elements in the parallel pathways toward the bulb neuropil. Although this neuropil with its afferent (TuBu) and efferent (R neurons) channels has been intensively studied in recent years, there still remains a gap in knowledge concerning how precise synaptic connections convey topographic information to the central complex. Four major findings of the TuBu->EB circuit are revealed by study. First, the topographic position of TuBu dendrites in the SU is not translated into a defined position within the bulb but instead exhibits a targeting plasticity within a restricted bulb area. Secondly, although the recent dissection of the AOTU->EB pathways described the bulb as a tripartite structure including both afferent and efferent neurons, this picture can now be refined by highlighting that although this analysis of TuBu-neurons is mainly restricted to only two representative TuBu classes (one in the SU-l_p and the other in the SU-ca domain), both these classes target to areas within the superior bulb (BU_s). More broadly expressed driver lines revealed exclusive TuBu neuron innervation of the BU_s, indicating that additional TuBu classes target to this bulb area. Thus, at least four different classes of TuBu neurons are expected to exclusively innervate the BU_s (TuBu-l_a, TuBu-l_p, TuBu-c_a, and TuBu-c_s), each of them connecting to a different set of output neurons, indicating an even more complex organization of the bulb, in particular the BU_s. Thirdly, TuBu classes project onto dendritic areas of R neuron classes (so called “sectors”) within the bulb, and specific connections are formed between TuBu neurons and R neuron classes. Although it was not possible to identify three R neuron classes within the BU_s, there probably exists a much higher diversity of connections within this small area of the bulb, reaching beyond the scope of this study. For instance, the postsynaptic partners of one subset of TuBu-ca neurons as well as neurons contacted by R2 and R5 dendrites remain to be identified. Additional postsynaptic partners other than R neurons are contacted by TuBu neurons, like contralaterally projecting neurons described in the locust and the bumblebee, which connect the AOTU units of both hemispheres (TuTu neurons) (Timaeus, 2020).

It appears therefore that topography is conserved within the AOTU output neuron projections toward the bulb and ring neurons, which is in good agreement with their physiological responses to visual stimuli, like bright objects. All ring neurons of the same type occupy the same ring layer within the ellipsoid body, raising the question of how topographic information is integrated within central complex neuropils. Interestingly, different MeTu neuron types with similar receptive fields may innervate different AOTU domains and thereby connect to different TuBu neuron populations forming parallel channels that then diverge within the bulb regions, where SU-l_p and SU-ca efferents were found mapping onto separate ring neurons (R4d versus R2). Hence, at least two distinct topographic MeTu channels into the central brain could be defined. Although functional differences between the BUi and BU_s have been described, functional studies have not yet compared the physiological responses of different TuBu classes or the responses of R neurons within the BU_s. Based on the data presented in this study, it would be expected that retinotopic information in the BU_s remains represented in the respective sector that is associated with their TuBu class (Timaeus, 2020).

A morphologically distinct class of MeTu cells is formed by MeTu-m cells. One distinguishing feature in respect to other MeTu cell types is that many cells arborize broadly in their respective AOTU domain. Axon terminals of single MeTu-m neurons invariably spread across the a-p axis of their SU-domain, whereas in the d-v axis they either covered their domain completely or partially-the former case being reminiscent of the afferent organization of LC neurons from the lobula within optic glomeruli in the PVLP regions, whereas the latter case is similarly described for lobula neurons innervating the AOTU's large unit (LU), where the topography of LC10 neurons in the LU has been analyzed, resulting in the distinction of four different LC10-classes. It remains to be seen whether MeTu-m neurons also could be divided into such classes. Those cells innervating the complete SU-m are well suited to form a nontopographic channel to the central brain. Interestingly, although topographic MeTu-l and -c neurons form dendritic fields within a single medulla layer, MeTu-m neurons integrate from three different medulla layers, reminiscent and in fact similar to some lobular LC neuron types, the main afferents of the AOTU large unit, for which a comparable rough topography along the dorsoventral axis has previously been found. Furthermore, only MeTu-m neurons form a collateral arborization in the lobula, indicating that this pathway could directly integrate visual information from both the medulla and lobula. The observation that MeTu-m neurons contact a population of TuBu neurons that projects into the inferior bulb area (Bui) separated from other TuBu neurons further suggests a different role for this pathway. A contralateral inhibition mediated by the Bui has been described, supporting a model in which the SU-m pathway is involved in suppressing ipsilateral stimuli with the expense of reduced spatial resolution (Timaeus, 2020).

Taken together, topographic and nontopographic afferents generate an interesting assembly of adjacent domains within the AOTU, from exclusively topographic medulla input in SU-l and SU-c domains, nontopographic medullar (and potentially also lobular) input in SU-m, and another large area of nontopographic input exclusively from the lobula in the LU. Thus, this study has identified multiple parallel topographic pathways separated from a parallel nontopographic channel (Timaeus, 2020).

This principle visual pathway involving the AOTU as a central relay station between medullar/lobular inputs and the central brain is widely shared among different insect taxa, where homologous structures can be found, e.g., orthopterans, hymenopterans, and beetles. The stimuli conveyed by this 'anterior visual pathway' have been addressed in only a few insect species so far. Most prominently, the AOTU has been associated with celestial orientation using polarized skylight in several species or in chromatic processing. Dorsal rim ommatidia harboring polarization-sensitive photoreceptors for polarized light vision are crucial for the sky-compass orientation and exist in most insects analyzed, such as locusts. However, it remains unknown whether MeTu neurons receive direct or indirect input from modality-specific cell types located in the DRA. In addition, processing of chromatic information was also shown to be accomplished via the AOTU in several insects. This study has now identified inputs to this pathway, by identifying direct connections between MeTu cells and UV-sensitive R7 photoreceptor cells in medulla layer M6 (Timaeus, 2020).

Furthermore, the molecular markers used in this study can serve as future tools to reveal the molecular mechanisms that underlie the formation of the LC-optic glomeruli network across species. Because Drosophila is among the smallest species for which the AOTU has been characterized and is believed to be a behavioral generalist, even more sophisticated architectures of the SU-homologue could exist in other insect taxa. On the anatomical and functional level, optic glomeruli share many features with the synaptic neuropil within the antennal lobe, which led to the postulation that the glomerular organization in the protocerebrum (optic glomeruli) and the deutocerebrum (olfactory glomeruli) are in fact homologous structures. Indeed, molecular characteristics in the PVLP and AOTU were found that resemble the combinatorial code of cell-surface proteins in the olfactory system (e.g. expression patterns of Ten-m, Con, Caps, and Sema1a in both systems). However, future developmental studies of mutant LC and MeTu neurons are needed to test to what extent common mechanisms of glomerular circuit assembly exist in both sensory systems. Although the idea of a serial homology of glomerular organized neural system is far from being resolved, it will be intriguing for further studies to analyze the developmental mechanisms that underlie the circuit formation of these parallel AOTU pathways and optic glomeruli circuits as well as to compare them with known molecular functions during olfactory system maturation (Timaeus, 2020).

The fact cannot be excluded that the SU of the AOTU might consist of additional functional units that so far have not been identified and that neurons were missed in this analysis due to the lack of expression lines to visualize them. Populations of neurons that were classified as a single type might turn out to be different enough (by morphology and/or synapse partners) to justify the establishment of further pathways, and these cell types might have been missed in the single cell labeling experiment, as this method involves random events where scarcer neurons can easily remain unnoticed. In vivo experiments measuring neuronal activity and responses to visual stimuli were beyond the scope of ths study but will be an essential part for understanding the functional features of the circuit. The wealth of genetic tools and their manifold combinations in Drosophila certainly provide capabilities of detailed analyses. As the driver lines used for this study to unravel the components of the visual pathway are publicly available and could be used to measure and manipulate neuronal activity, it is hoped that this study paves the way for future studies of components of this visual circuit (Timaeus, 2020).

The brain perceives visual information and controls behavior depending on its underlying neural circuits. How UV information is represented and processed in the brain remains poorly understood. In Drosophila melanogaster, UV light is detected by the R7 photoreceptor that project exclusively into the medulla layer 6 (M(6)). This study imaged 28,768 single neurons and identified 238 visual projection neurons linking M(6) to the central brain. Based on morphology and connectivity, these visual projection neurons were systematically classified into 94 cell types belonging to 12 families. Three tracts connected M(6) in each optic lobe to the central brain: One dorsal tract linking to the ipsilateral lateral anterior optic tubercle (L-AOTU) and two medial tracts linking to the ipsilateral ventral medial protocerebrum (VMP) and the contralateral VMP. The M(6) information was primarily represented in the L-AOTU. Each L-AOTU consisted of four columns that each contained three glomeruli. Each L-AOTU glomerulus received inputs from M(6) subdomains and gave outputs to a glomerulus within the ellipsoid body dendritic region, suggesting specific processing of spatial information through the dorsal pathway. Furthermore, the middle columns of the L-AOTUs of both hemispheres were connected via the intertubercle tract, suggesting information integration between the two eyes. In contrast, an ascending neuron linked each VMP to all glomeruli in the bulb and the L-AOTU, bilaterally, suggesting general processing of information through the ventral pathway. Altogether, these diverse morphologies of the visual projection neurons suggested multi-dimensional processing of UV information through parallel and bilateral circuits in the Drosophila brain (Tai, 2020).

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