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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. Here we use 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. We constructed a passive, conductance-based model of a T4 cell 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.

The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize 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. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. 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. We describe examples 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. In the brain, large numbers of different types of neurons connect with each other to form complex networks. In recent years, researchers have made great progress in mapping all the connections between these cells, creating ‘wiring diagrams’ known as connectomes. However, charting the connections between neurons does not give all the answers as to how the brain works; for example, it does not necessarily reveal the nature of the information two connected cells exchange. Assessing which genes are switched on in different neurons can give insight into neuronal properties that are not obvious from physical connections alone. To fill that knowledge gap, Davis, Nern et al. aimed to measure the genes expressed in a well-characterized network of neurons in the fruit fly visual system. First, 100 fly strains were established, each carrying a single type of neuron colored with a fluorescent marker. Then, a biochemical approach was developed to extract the part of the cell that contains the genetic code from the neurons with the marker. Finally, a statistical tool was used to assess which genes were on in each type of neurons. This led to the creation of a database that shows whether 15,000 genes in each neuron type across 100 fly strains were switched on. Combining this information with previous knowledge about the flies’ visual system revealed new information: for example, it helped to understand which chemicals the neurons use to communicate, and whether certain cells activate or inhibit each other. The work by Davis, Nern et al. demonstrates how genetic approaches can complement other methods, and it offers a new tool for other scientists to use in their work. With more advanced genetic methods, it may one day become possible to better grasp how complex brains in other organisms are organized, and how they are disrupted in disease.

The discovery of a visual-looming-sensitive neuron, LPLC2, that provides input to the Drosophila escape pathway, and uses dendrites patterned to integrate directionally selective inputs to selectively encode outward motion. Detecting looming dangers To detect the threat of a looming object, visual systems must integrate many motion cues across the whole visual field, and thus face the challenge of rejecting confounding stimuli, such as large passing objects. The neuronal computation behind this complex response is unknown. Gwyneth Card and colleagues have discovered a cross-shaped neuron in the Drosophila brain, LPLC2, whose dendrites align with the four cardinal directions of motion, as represented along the layers of elementary motion detector neurons T4 and T5 in the lobula plate. A balance of inhibitory and excitatory inputs then ensures that individual LPLC2 neurons respond to outward but not inward motion from the center of the neuron's receptive field, making LPLC2 neurons non-responsive to related patterns of motion such as contraction, wide-field translation or luminance change. Nervous systems combine lower-level sensory signals to detect higher-order stimulus features critical to survival 1 , 2 , 3 , such as the visual looming motion created by an imminent collision or approaching predator 4 . Looming-sensitive neurons have been identified in diverse animal species 5 , 6 , 7 , 8 , 9 . Different large-scale visual features such as looming often share local cues, which means loom-detecting neurons face the challenge of rejecting confounding stimuli. Here we report the discovery of an ultra-selective looming detecting neuron, lobula plate/lobula columnar, type II (LPLC2) 10 in Drosophila , and show how its selectivity is established by radial motion opponency. In the fly visual system, directionally selective small-field neurons called T4 and T5 form a spatial map in the lobula plate, where they each terminate in one of four retinotopic layers, such that each layer responds to motion in a different cardinal direction 11 , 12 , 13 . Single-cell anatomical analysis reveals that each arm of the LPLC2 cross-shaped primary dendrites ramifies in one of these layers and extends along that layer’s preferred motion direction. In vivo calcium imaging demonstrates that, as their shape predicts, individual LPLC2 neurons respond strongly to outward motion emanating from the centre of the neuron’s receptive field. Each dendritic arm also receives local inhibitory inputs directionally selective for inward motion opposing the excitation. This radial motion opponency generates a balance of excitation and inhibition that makes LPLC2 non-responsive to related patterns of motion such as contraction, wide-field rotation or luminance change. As a population, LPLC2 neurons densely cover visual space and terminate onto the giant fibre descending neurons, which drive the jump muscle motor neuron to trigger an escape take off. Our findings provide a mechanistic description of the selective feature detection that flies use to discern and escape looming threats.

Visual projection neurons (VPNs) provide an anatomical connection between early visual processing and higher brain regions. Here we characterize lobula columnar (LC) cells, a class of Drosophila VPNs that project to distinct central brain structures called optic glomeruli. We anatomically describe 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. Our results indicate that LC neurons convey information on the presence and location of visual features relevant for specific behaviors. Many animals rely heavily on what they can see to interact with the world around them. But how does the brain use such visual information to guide behavior? Light-sensitive neurons in the eye cannot distinguish between the visual signals associated with, say, an approaching predator or a source of food. Yet the brain can make this distinction. Networks of neurons in the brain perform computations to extract information from a visual scene that indicates the need for a particular behavior, such as an escape response. These networks are found in regions of the brain that communicate closely with the eyes. Cells known as visual projection neurons then relay the output of these networks to more central parts of the brain. By studying visual projection neurons, it is possible to work out what the eye tells the brain, and how the brain uses this information to control behavior. The fruit fly Drosophila is a suitable model organism in which to study these phenomena. This insect shows a range of behavioral responses to visual stimuli, and can be studied using sophisticated genetic tools. Wu, Nern et al. set out to explore how a group of visual projection neurons known as lobula columnar cells help fruit flies respond appropriately to visual stimuli. Experiments revealed that individual subtypes of lobula columnar cells convey information about the presence and general location of specific visual features. Wu, Nern et al. identified a number of lobular columnar subtypes involved in triggering escape responses to specific stimuli – such as walking backwards or taking off in flight – as well as others that can trigger the flies to approach a target. A next step is to map the circuits of neurons that act upstream and downstream of lobula columnar cells. This can help to reveal how these neurons detect specific visual features and how the fly then chooses and executes an appropriate behavior in response. Such studies in flies can provide insights into general principles of how brains use sensory information to guide behavior.

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. Here, we demonstrate an unexpected, additional contribution of color to motion vision in Drosophila . We show that behavioral ON-motion responses are more sensitive to UV than for OFF-motion, and we identify cellular pathways 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 we show how this could generalize for systems with ON- and OFF-motion pathways. Our results provide a computational and circuit basis for how color enhances motion vision to favor the detection of saliently colored objects. Motion vision in many animals is split into pathways for bright (ON) and dark (OFF) edges, driven by luminance changes. Here the authors show how in Drosophila color selectively contributes to ON-motion, enhancing detection of saliently colored objects.

To pursue a more mechanistic understanding of the neural control of behavior, many neuroethologists study animal behavior in controlled laboratory environments. One popular approach is to measure the movements of restrained animals while presenting controlled sensory stimulation. This approach is especially powerful when applied to genetic model organisms, such as Drosophila melanogaster , where modern genetic tools enable unprecedented access to the nervous system for activity monitoring or targeted manipulation. While there is a long history of measuring the behavior of body- and head-fixed insects walking on an air-supported ball, the methods typically require complex setups with many custom components. Here we present a compact, simplified setup for these experiments that achieves high-performance at low cost. The simplified setup integrates existing hardware and software solutions with new component designs. We replaced expensive optomechanical and custom machined components with off-the-shelf and 3D-printed parts, and built the system around a low-cost camera that achieves 180 Hz imaging and an inexpensive tablet computer to present view-angle-corrected stimuli updated through a local network. We quantify the performance of the integrated system and characterize the visually guided behavior of flies in response to a range of visual stimuli. In this paper, we thoroughly document the improved system; the accompanying repository incorporates CAD files, parts lists, source code, and detailed instructions. We detail a complete ~$300 system, including a cold-anesthesia tethering stage, that is ideal for hands-on teaching laboratories. This represents a nearly 50-fold cost reduction as compared to a typical system used in research laboratories, yet is fully featured and yields excellent performance. We report the current state of this system, which started with a 1-day teaching lab for which we built seven parallel setups and continues toward a setup in our lab for larger-scale analysis of visual-motor behavior in flies. Because of the simplicity, compactness, and low cost of this system, we believe that high-performance measurements of tethered insect behavior should now be widely accessible and suitable for integration into many systems. This access enables broad opportunities for comparative work across labs, species, and behavioral paradigms.

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. Our 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. Here we 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, we found no amplifying responses, only suppressive responses to the non-preferred motion direction. We then evaluated passive, biophysical models and found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli.

This technique allows functional imaging of neurons in head-fixed Drosophila while the fly walks on an air-supported ball. Using a genetically encoded calcium sensor, the activity of motion-sensitive neurons in the fly optic lobe was recorded while the flies were presented with visual stimuli. Activity in these cells correlated with robust optomotor behavior in the walking flies. Drosophila melanogaster is a model organism rich in genetic tools to manipulate and identify neural circuits involved in specific behaviors. Here we present a technique for two-photon calcium imaging in the central brain of head-fixed Drosophila walking on an air-supported ball. The ball's motion is tracked at high resolution and can be treated as a proxy for the fly's own movements. We used the genetically encoded calcium sensor, GCaMP3.0, to record from important elements of the motion-processing pathway, the horizontal-system lobula plate tangential cells (LPTCs) in the fly optic lobe. We presented motion stimuli to the tethered fly and found that calcium transients in horizontal-system neurons correlated with robust optomotor behavior during walking. Our technique allows both behavior and physiology in identified neurons to be monitored in a genetic model organism with an extensive repertoire of walking behaviors.

Visual systems can exploit spatial correlations in the visual scene by using retinotopy, the organizing principle by which neighboring cells encode neighboring spatial locations. However, retinotopy is often lost, such as when visual pathways are integrated with other sensory modalities. How is spatial information processed outside of strictly visual brain areas? Here, we focused on visual looming responsive LC6 cells in Drosophila , a population whose dendrites collectively cover the visual field, but whose axons form a single glomerulus—a structure without obvious retinotopic organization—in the central brain. We identified multiple cell types downstream of LC6 in the glomerulus and found that they more strongly respond to looming in different portions of the visual field, unexpectedly preserving spatial information. Through EM reconstruction of all LC6 synaptic inputs to the glomerulus, we found that LC6 and downstream cell types form circuits within the glomerulus that enable spatial readout of visual features and contralateral suppression—mechanisms that transform visual information for behavioral control.

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, we have systematically reconstructed the synaptic targets of the photoreceptors specialized to detect color and skylight polarization in Drosophila , and we have used light microscopy to confirm many of our findings. We identified known and novel downstream targets that are selective for different wavelengths or polarized light, and followed their projections to other areas in the optic lobes and the central brain. Our 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. Our 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.