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. Here, we examine the functional and spatial relationships of 10 glomeruli using single-neuron calcium imaging. We discover novel detectors for objects smaller than the lens resolution (LC18) and for complex line motion (LC25). We find that 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.

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 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.

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. Here, we show 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 we show that central octopaminergic neurons provide input to Mi4 and increase its excitability. We further show 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.

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain’s volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly’s visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the 53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

Motion detection is a fundamental neural computation performed by many sensory systems. In the fly, local motion computation is thought to occur within the first two layers of the visual system, the lamina and medulla. We constructed specific genetic driver lines for each of the 12 neuron classes in the lamina. We then depolarized and hyperpolarized each neuron type and quantified fly behavioral responses to a diverse set of motion stimuli. We found that only a small number of lamina output neurons are essential for motion detection, while most neurons serve to sculpt and enhance these feedforward pathways. Two classes of feedback neurons (C2 and C3), and lamina output neurons (L2 and L4), are required for normal detection of directional motion stimuli. Our results reveal a prominent role for feedback and lateral interactions in motion processing and demonstrate that motion-dependent behaviors rely on contributions from nearly all lamina neuron classes.

Visual motion perception is critical to many animal behaviors, and flies have emerged as a powerful model system for exploring this fundamental neural computation. Although numerous studies have suggested that fly motion vision is governed by a simple neural circuit [1-3], the implementation of this circuit has remained mysterious for decades. Connectomics and neurogenetics have produced a surge in recent progress, and several studies have shown selectivity for light increments (ON) or decrements (OFF) in key elements associated with this circuit [4-7]. However, related studies have reached disparate conclusions about where this selectivity emerges and whether it plays a major role in motion vision [8-13]. To address these questions, we examined activity in the neuropil thought to be responsible for visual motion detection, the medulla, of Drosophila melanogaster in response to a range of visual stimuli using two-photon calcium imaging. We confirmed that the input neurons of the medulla, the LMCs, are not responsible for light-on and light-off selectivity. We then examined the pan-neural response of medulla neurons and found prominent selectivity for light-on and light-off in layers of the medulla associated with two anatomically derived pathways (L1/L2 associated) [14, 15]. We next examined the activity of prominent interneurons within each pathway (Mi1 and Tm1) and found that these neurons have corresponding selectivity for light-on or light-off. These results provide direct evidence that motion is computed in parallel light-on and light-off pathways, demonstrate that this selectivity emerges in neurons immediately downstream of the LMCs, and specify where crucial elements of motion computation occur.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

•We developed machine-vision methods to broadly and precisely quantify fly behavior•We measured effects of activating 2,204 genetically targeted neuronal populations•We created whole-brain maps of neural substrates of locomotor and social behaviors•We created resources for exploring our results and enabling further investigation

Machine-vision analyses of large behavior and neuroanatomy data reveal whole-brain maps of regions associated with numerous complex behaviors.

The insect mushroom body (MB) is a conserved brain structure that plays key roles in a diverse array of behaviors. The MB is the primary invertebrate model of neural circuits related to memory formation and storage, and its development, morphology, wiring, and function has been extensively studied. MBs consist of intrinsic Kenyon Cells that are divided into three major neuron classes (γ, α'/β' and α/β) and 7 cell subtypes (γd, γm, α'/β'ap, α'/β'm, α/βp, α/βs and α/βc) based on their birth order, morphology, and connectivity. These subtypes play distinct roles in memory processing, however the underlying transcriptional differences are unknown. Here, we used RNA sequencing (RNA-seq) to profile the nuclear transcriptomes of each MB neuronal cell subtypes. We identified 350 MB class- or subtype-specific genes, including the widely used α/β class marker and the α'/β' class marker Immunostaining corroborates the RNA-seq measurements at the protein level for several cases. Importantly, our data provide a full accounting of the neurotransmitter receptors, transporters, neurotransmitter biosynthetic enzymes, neuropeptides, and neuropeptide receptors expressed within each of these cell types. This high-quality, cell type-level transcriptome catalog for the MB provides a valuable resource for the fly neuroscience community.

Animals rely on visual motion for navigating the world, and research in flies has clarified how neural circuits extract information from moving visual scenes. However, the major pathways connecting these patterns of optic flow to behavior remain poorly understood. Using a high-throughput quantitative assay of visually guided behaviors and genetic neuronal silencing, we discovered a region in Drosophila’s protocerebrum critical for visual motion following. We used neuronal silencing, calcium imaging, and optogenetics to identify a single cell type, LPC1, that innervates this region, detects translational optic flow, and plays a key role in regulating forward walking. Moreover, the population of LPC1s can estimate the travelling direction, such as when gaze direction diverges from body heading. By linking specific cell types and their visual computations to specific behaviors, our findings establish a foundation for understanding how the nervous system uses vision to guide navigation.

Visual systems can exploit spatial correlations in the visual scene by using retinotopy. 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 , 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.