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Visual motion detection is among the best understood neuronal computations. As extensively investigated in tethered flies, visual motion signals are assumed to be crucial to detect and counteract involuntary course deviations. During free flight, however, course changes are also signalled by other sensory systems. Therefore, it is as yet unclear to what extent motion vision contributes to course control. To address this question, we genetically rendered flies motion-blind by blocking their primary motion-sensitive neurons and quantified their free-flight performance. We found that such flies have difficulty maintaining a straight flight trajectory, much like unimpaired flies in the dark. By unilateral wing clipping, we generated an asymmetry in propulsive force and tested the ability of flies to compensate for this perturbation. While wild-type flies showed a remarkable level of compensation, motion-blind animals exhibited pronounced circling behaviour. Our results therefore directly confirm that motion vision is necessary to fly straight under realistic conditions.

Visual motion detection is among the best understood neuronal computations. One assumed behavioural role is to detect self-motion and to counteract involuntary course deviations, extensively investigated in tethered walking or flying flies. In free flight, however, any deviation from a straight course is signalled by both the visual system as well as by proprioceptive mechanoreceptors called ‘halteres’, which are the equivalent of the vestibular system in vertebrates. Therefore, it is yet unclear to what extent motion vision contributes to course control, or whether straight flight is completely controlled by proprioceptive feedback from the halteres. To answer these questions, we genetically rendered flies motion-blind by blocking their primary motion-sensitive neurons and quantified their free-flight performance. We found that such flies have difficulties maintaining a straight flight trajectory, much like control flies in the dark. By unilateral wing clipping, we generated an asymmetry in propulsory force and tested the ability of flies to compensate for this perturbation. While wild-type flies showed a remarkable level of compensation, motion-blind animals exhibited pronounced circling behaviour. Our results therefore unequivocally demonstrate that motion vision is necessary to fly straight under realistic conditions.

Summary 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. Here, we demonstrate that the Drosophila visual system reduces input variability by rapidly adjusting its sensitivity to local contrast conditions. We exhaustively map functional properties of neurons in the motion detection circuit and find that local responses are 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, our work represents a comprehensive mechanistic account of how neural systems attain the robustness to carry out survival-critical tasks in challenging real-world environments.

The reliable estimation of motion across varied surroundings represents a survival-critical task for sighted animals. How neural circuits have adapted to the particular demands of natural environments, however, is not well understood. We explored this question in the visual system of Drosophila melanogaster. Here, as in many mammalian retinas, motion is computed in parallel streams for brightness increments (ON) and decrements (OFF). When genetically isolated, ON and OFF pathways proved equally capable of accurately matching walking responses to realistic motion. To our surprise, detailed characterization of their functional tuning properties through in vivo calcium imaging and electrophysiology revealed stark differences in temporal tuning between ON and OFF channels. We trained an in silico motion estimation model on natural scenes and discovered that our optimized detector exhibited differences similar to those of the biological system. Thus, functional ON-OFF asymmetries in fly visual circuitry may reflect ON-OFF asymmetries in natural environments.

Estimating motion is a fundamental task for the visual system of sighted animals. In Drosophila, 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, we demonstrate 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, we find that all input elements are involved in OFF motion detection to varying degrees. Our 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.

In the Drosophila visual system, T4/T5 neurons represent the first stage of computation of the direction of visual motion. T4 and T5 neurons exist in four subtypes, each responding to motion in one of the four cardinal directions and projecting axons into one of the four lobula plate layers. However, all T4/T5 neurons share properties essential for sensing motion. How T4/T5 neurons acquire their properties during development is poorly understood. We reveal that the transcription factors SoxN and Sox102F control the acquisition of properties common to all T4/T5 neuron subtypes, i.e. the layer specificity of dendrites and axons. Accordingly, adult flies are motion blind after disruption of SoxN or Sox102F in maturing T4/T5 neurons. We further find that the transcription factors Ato and Dac are redundantly required in T4/T5 neuron progenitors for SoxN and Sox102F expression in T4/T5 neurons, linking the transcriptional programmes specifying progenitor identity to those regulating the acquisition of morphological properties in neurons. Our work will help to link structure, function and development in a neuronal type performing a computation that is conserved across vertebrate and invertebrate visual systems., Summary: RNA interference identifies SoxN and Sox102F as key regulators of the morphological properties of a neuronal type of the fruit fly visual system that computes the direction of motion.

The extraction of directional motion information from changing retinal images is one of the earliest and most important processing steps in any visual system. In the fly optic lobe, two parallel processing streams have been anatomically described, leading from two first-order interneurons, L1 and L2, via T4 and T5 cells onto large, wide-field motion-sensitive interneurons of the lobula plate. Therefore, T4 and T5 cells are thought to have a pivotal role in motion processing; however, owing to their small size, it is difficult to obtain electrical recordings of T4 and T5 cells, leaving their visual response properties largely unknown. We circumvent this problem by means of optical recording from these cells in Drosophila, using the genetically encoded calcium indicator GCaMP5 (ref. 2). Here we find that specific subpopulations of T4 and T5 cells are directionally tuned to one of the four cardinal directions; that is, front-to-back, back-to-front, upwards and downwards. Depending on their preferred direction, T4 and T5 cells terminate in specific sublayers of the lobula plate. T4 and T5 functionally segregate with respect to contrast polarity: whereas T4 cells selectively respond to moving brightness increments (ON edges), T5 cells only respond to moving brightness decrements (OFF edges). When the output from T4 or T5 cells is blocked, the responses of postsynaptic lobula plate neurons to moving ON (T4 block) or OFF edges (T5 block) are selectively compromised. The same effects are seen in turning responses of tethered walking flies. Thus, starting with L1 and L2, the visual input is split into separate ON and OFF pathways, and motion along all four cardinal directions is computed separately within each pathway. The output of these eight different motion detectors is then sorted such that ON (T4) and OFF (T5) motion detectors with the same directional tuning converge in the same layer of the lobula plate, jointly providing the input to downstream circuits and motion-driven behaviours.

Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information.In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines.