Asymmetry of Drosophila ON and OFF motion detectors enhances real-world velocity estimation

Broken time reversal symmetry in visual motion detection

Our intuition suggests that when a movie is played in reverse, our perception of motion in the reversed movie will be perfectly inverted compared to the original. This intuition is also reflected in many classical theoretical and practical models of motion detection. However, here we demonstrate that this symmetry of motion perception upon time reversal is often broken in real visual systems. In this work, we designed a set of visual stimuli to investigate how stimulus symmetries affect time reversal symmetry breaking in the fruit fly Drosophila's well-studied optomotor rotation behavior. We discovered a suite of new stimuli with a wide variety of different properties that can lead to broken time reversal symmetries in fly behavioral responses. We then trained neural network models to predict the velocity of scenes with both natural and artificial contrast distributions. Training with naturalistic contrast distributions yielded models that break time reversal symmetry, even when the training data was time reversal symmetric. We show analytically and numerically that the breaking of time reversal symmetry in the model responses can arise from contrast asymmetry in the training data, but can also arise from other features of the contrast distribution. Furthermore, shallower neural network models can exhibit stronger symmetry breaking than deeper ones, suggesting that less flexible neural networks promote some forms of time reversal symmetry breaking. Overall, these results reveal a surprising feature of biological motion detectors and suggest that it could arise from constrained optimization in natural environments.

Model organisms and systems in neuroethology: one hundred years of history and a look into the future

The Journal of Comparative Physiology lived up to its name in the last 100 years by including more than 1500 different taxa in almost 10,000 publications. Seventeen phyla of the animal kingdom were represented. The honeybee (Apis mellifera) is the taxon with most publications, followed by locust (Locusta migratoria), crayfishes (Cambarus spp.), and fruitfly (Drosophila melanogaster). The representation of species in this journal in the past, thus, differs much from the 13 model systems as named by the National Institutes of Health (USA). We mention major accomplishments of research on species with specific adaptations, specialist animals, for example, the quantitative description of the processes underlying the axon potential in squid (Loligo forbesii) and the isolation of the first receptor channel in the electric eel (Electrophorus electricus) and electric ray (Torpedo spp.). Future neuroethological work should make the recent genetic and technological developments available for specialist animals. There are many research questions left that may be answered with high yield in specialists and some questions that can only be answered in specialists. Moreover, the adaptations of animals that occupy specific ecological niches often lend themselves to biomimetic applications. We go into some depth in explaining our thoughts in the research of motion vision in insects, sound localization in barn owls, and electroreception in weakly electric fish.

Direct comparison reveals algorithmic similarities in fly and mouse visual motion detection

Evolution has equipped vertebrates and invertebrates with neural circuits that selectively encode visual motion. While similarities in the computations performed by these circuits in mouse and fruit fly have been noted, direct experimental comparisons have been lacking. Because molecular mechanisms and neuronal morphology in the two species are distinct, we directly compared motion encoding in these two species at the algorithmic level, using matched stimuli and focusing on a pair of analogous neurons, the mouse ON starburst amacrine cell (ON SAC) and Drosophila T4 neurons. We find that the cells share similar spatiotemporal receptive field structures, sensitivity to spatiotemporal correlations, and tuning to sinusoidal drifting gratings, but differ in their responses to apparent motion stimuli. Both neuron types showed a response to summed sinusoids that deviates from models for motion processing in these cells, underscoring the similarities in their processing and identifying response features that remain to be explained.

Long-timescale anti-directional rotation in Drosophila optomotor behavior

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. Here, we report 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.

How Flies See Motion

How neurons detect the direction of motion is a prime example of neural computation: Motion vision is found in the visual systems of virtually all sighted animals, it is important for survival, and it requires interesting computations with well-defined linear and nonlinear processing steps-yet the whole process is of moderate complexity. The genetic methods available in the fruit fly Drosophila and the charting of a connectome of its visual system have led to rapid progress and unprecedented detail in our understanding of how neurons compute the direction of motion in this organism. The picture that emerged incorporates not only the identity, morphology, and synaptic connectivity of each neuron involved but also its neurotransmitters, its receptors, and their subcellular localization. Together with the neurons' membrane potential responses to visual stimulation, this information provides the basis for a biophysically realistic model of the circuit that computes the direction of visual motion.

Functional organization of visual responses in the octopus optic lobe

Cephalopods are highly visual animals with camera-type eyes, large brains, and a rich repertoire of visually guided behaviors. However, the cephalopod brain evolved independently from those of other highly visual species, such as vertebrates; therefore, the neural circuits that process sensory information are profoundly different. It is largely unknown how their powerful but unique visual system functions, as there have been no direct neural measurements of visual responses in the cephalopod brain. In this study, we used two-photon calcium imaging to record visually evoked responses in the primary visual processing center of the octopus central brain, the optic lobe, to determine how basic features of the visual scene are represented and organized. We found spatially localized receptive fields for light (ON) and dark (OFF) stimuli, which were retinotopically organized across the optic lobe, demonstrating a hallmark of visual system organization shared across many species. An examination of these responses revealed transformations of the visual representation across the layers of the optic lobe, including the emergence of the OFF pathway and increased size selectivity. We also identified asymmetries in the spatial processing of ON and OFF stimuli, which suggest unique circuit mechanisms for form processing that may have evolved to suit the specific demands of processing an underwater visual scene. This study provides insight into the neural processing and functional organization of the octopus visual system, highlighting both shared and unique aspects, and lays a foundation for future studies of the neural circuits that mediate visual processing and behavior in cephalopods.

Serotonin increases population coding of behaviorally relevant stimuli by enhancing responses of ON but not OFF-type sensory neurons

How neural populations encode sensory input to generate behavioral responses remains a central problem in systems neuroscience. Here we investigated how neuromodulation influences population coding of behaviorally relevant stimuli to give rise to behavior in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. We performed multi-unit recordings from ON and OFF sensory pyramidal cells in response to stimuli whose amplitude (i.e., envelope) varied in time, before and after electrical stimulation of the raphe nuclei. Overall, raphe stimulation increased population coding by ON- but not by OFF-type cells, despite both cell types showing similar sensitivities to the stimulus at the single neuron level. Surprisingly, only changes in population coding by ON-type cells were correlated with changes in behavioral responses. Taken together, our results show that neuromodulation differentially affects ON vs. OFF-type cells in order to enhance perception of behaviorally relevant sensory input.

Visual processing in the fly, from photoreceptors to behavior

Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.

Disynaptic inhibition shapes tuning of OFF-motion detectors in Drosophila

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, we 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.

Bioinspired figure-ground discrimination via visual motion smoothing

Flies detect and track moving targets among visual clutter, and this process mainly relies on visual motion. Visual motion is analyzed or computed with the pathway from the retina to T4/T5 cells. The computation of local directional motion was formulated as an elementary movement detector (EMD) model more than half a century ago. Solving target detection or figure-ground discrimination problems can be equivalent to extracting boundaries between a target and the background based on the motion discontinuities in the output of a retinotopic array of EMDs. Individual EMDs cannot measure true velocities, however, due to their sensitivity to pattern properties such as luminance contrast and spatial frequency content. It remains unclear how local directional motion signals are further integrated to enable figure-ground discrimination. Here, we present a computational model inspired by fly motion vision. Simulations suggest that the heavily fluctuating output of an EMD array is naturally surmounted by a lobula network, which is hypothesized to be downstream of the local motion detectors and have parallel pathways with distinct directional selectivity. The lobula network carries out a spatiotemporal smoothing operation for visual motion, especially across time, enabling the segmentation of moving figures from the background. The model qualitatively reproduces experimental observations in the visually evoked response characteristics of one type of lobula columnar (LC) cell. The model is further shown to be robust to natural scene variability. Our results suggest that the lobula is involved in local motion-based target detection.

Long timescale anti-directional rotation in Drosophila optomotor behavior

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. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied D. 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.

Differences in visual stimulation between reading and walking and implications for myopia development

Visual input plays an important role in the development of myopia (nearsightedness), a visual disorder that blurs vision at far distances. The risk of myopia progression increases with the time spent reading and decreases with outdoor activity for reasons that remain poorly understood. To investigate the stimulus parameters driving this disorder, we compared the visual input to the retina of humans performing two tasks associated with different risks of myopia progression, reading and walking. Human subjects performed the two tasks while wearing glasses with cameras and sensors that recorded visual scenes and visuomotor activity. When compared with walking, reading black text in white background reduced spatiotemporal contrast in central vision and increased it in peripheral vision, leading to a pronounced reduction in the ratio of central/peripheral strength of visual stimulation. It also made the luminance distribution heavily skewed toward negative dark contrast in central vision and positive light contrast in peripheral vision, decreasing the central/peripheral stimulation ratio of ON visual pathways. It also decreased fixation distance, blink rate, pupil size, and head-eye coordination reflexes dominated by ON pathways. Taken together with previous work, these results support the hypothesis that reading drives myopia progression by understimulating ON visual pathways.

Functional organization of visual responses in the octopus optic lobe

Cephalopods are highly visual animals with camera-type eyes, large brains, and a rich repertoire of visually guided behaviors. However, the cephalopod brain evolved independently from that of other highly visual species, such as vertebrates, and therefore the neural circuits that process sensory information are profoundly different. It is largely unknown how their powerful but unique visual system functions, since there have been no direct neural measurements of visual responses in the cephalopod brain. In this study, we used two-photon calcium imaging to record visually evoked responses in the primary visual processing center of the octopus central brain, the optic lobe, to determine how basic features of the visual scene are represented and organized. We found spatially localized receptive fields for light (ON) and dark (OFF) stimuli, which were retinotopically organized across the optic lobe, demonstrating a hallmark of visual system organization shared across many species. Examination of these responses revealed transformations of the visual representation across the layers of the optic lobe, including the emergence of the OFF pathway and increased size selectivity. We also identified asymmetries in the spatial processing of ON and OFF stimuli, which suggest unique circuit mechanisms for form processing that may have evolved to suit the specific demands of processing an underwater visual scene. This study provides insight into the neural processing and functional organization of the octopus visual system, highlighting both shared and unique aspects, and lays a foundation for future studies of the neural circuits that mediate visual processing and behavior in cephalopods.

Highlights

The functional organization and visual response properties of the cephalopod visual system are largely unknownUsing calcium imaging, we performed mapping of visual responses in the octopus optic lobeVisual responses demonstrate localized ON and OFF receptive fields with retinotopic organizationON/OFF pathways and size selectivity emerge across layers of the optic lobe and have distinct properties relative to other species.

Contrast polarity-specific mapping improves efficiency of neuronal computation for collision detection

Neurons receive information through their synaptic inputs, but the functional significance of how those inputs are mapped on to a cell's dendrites remains unclear. We studied this question in a grasshopper visual neuron that tracks approaching objects and triggers escape behavior before an impending collision. In response to black approaching objects, the neuron receives OFF excitatory inputs that form a retinotopic map of the visual field onto compartmentalized, distal dendrites. Subsequent processing of these OFF inputs by active membrane conductances allows the neuron to discriminate the spatial coherence of such stimuli. In contrast, we show that ON excitatory synaptic inputs activated by white approaching objects map in a random manner onto a more proximal dendritic field of the same neuron. The lack of retinotopic synaptic arrangement results in the neuron's inability to discriminate the coherence of white approaching stimuli. Yet, the neuron retains the ability to discriminate stimulus coherence for checkered stimuli of mixed ON/OFF polarity. The coarser mapping and processing of ON stimuli thus has a minimal impact, while reducing the total energetic cost of the circuit. Further, we show that these differences in ON/OFF neuronal processing are behaviorally relevant, being tightly correlated with the animal's escape behavior to light and dark stimuli of variable coherence. Our results show that the synaptic mapping of excitatory inputs affects the fine stimulus discrimination ability of single neurons and document the resulting functional impact on behavior.

Motion vision: Drosophila neural pathways that go with the visual flow

Processing visual motion cues to interpret self-motion, the movement of others, and the environment's structure is vital to all animals, whether prey or predator. A new study in Drosophila identifies multiple pathways likely contributing to visual motion-dependent computations and behaviors.

First-order visual interneurons distribute distinct contrast and luminance information across ON and OFF pathways to achieve stable behavior

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. Here, we show 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.

Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons

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. Here, inspired by the distinctive structures of LPLC2 neurons in the visual system of Drosophila, we build anatomically-constrained shallow neural network models and train 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.

ON and OFF Signaling Pathways in the Retina and the Visual System

Visual processing starts at the retina of the eye, and signals are then transferred primarily to the visual cortex and the tectum. In the retina, multiple neural networks encode different aspects of visual input, such as color and motion. Subsequently, multiple neural streams in parallel convey unique aspects of visual information to cortical and subcortical regions. Bipolar cells, which are the second order neurons of the retina, separate visual signals evoked by light and dark contrasts and encode them to ON and OFF pathways, respectively. The interplay between ON and OFF neural signals is the foundation for visual processing for object contrast which underlies higher order stimulus processing. ON and OFF pathways have been classically thought to signal in a mirror-symmetric manner. However, while these two pathways contribute synergistically to visual perception in some instances, they have pronounced asymmetries suggesting independent operation in other cases. In this review, we summarize the role of the ON-OFF dichotomy in visual signaling, aiming to contribute to the understanding of visual recognition.

Predicting individual neuron responses with anatomically constrained task optimization

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. Here, we investigated how artificial networks predict individual neuron properties in the visual motion circuits of the fruit fly Drosophila. We trained anatomically constrained networks 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.

Predictive encoding of motion begins in the primate retina

Predictive motion encoding is an important aspect of visually guided behavior that allows animals to estimate the trajectory of moving objects. Motion prediction is understood primarily in the context of translational motion, but the environment contains other types of behaviorally salient motion correlation such as those produced by approaching or receding objects. However, the neural mechanisms that detect and predictively encode these correlations remain unclear. We report here that four of the parallel output pathways in the primate retina encode predictive motion information, and this encoding occurs for several classes of spatiotemporal correlation that are found in natural vision. Such predictive coding can be explained by known nonlinear circuit mechanisms that produce a nearly optimal encoding, with transmitted information approaching the theoretical limit imposed by the stimulus itself. Thus, these neural circuit mechanisms efficiently separate predictive information from nonpredictive information during the encoding process.

Synergistic population coding of natural communication stimuli by hindbrain electrosensory neurons

Understanding how neural populations encode natural stimuli with complex spatiotemporal structure to give rise to perception remains a central problem in neuroscience. Here we investigated population coding of natural communication stimuli by hindbrain neurons within the electrosensory system of weakly electric fish Apteronotus leptorhynchus. Overall, we found that simultaneously recorded neural activities were correlated: signal but not noise correlations were variable depending on the stimulus waveform as well as the distance between neurons. Combining the neural activities using an equal-weight sum gave rise to discrimination performance between different stimulus waveforms that was limited by redundancy introduced by noise correlations. However, using an evolutionary algorithm to assign different weights to individual neurons before combining their activities (i.e., a weighted sum) gave rise to increased discrimination performance by revealing synergistic interactions between neural activities. Our results thus demonstrate that correlations between the neural activities of hindbrain electrosensory neurons can enhance information about the structure of natural communication stimuli that allow for reliable discrimination between different waveforms by downstream brain areas.

Image luminance changes contrast sensitivity in visual cortex

Accurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.

Mechanism for analogous illusory motion perception in flies and humans

Visual motion detection is one of the most important computations performed by visual circuits. Yet, we perceive vivid illusory motion in stationary, periodic luminance gradients that contain no true motion. This illusion is shared by diverse vertebrate species, but theories proposed to explain this illusion have remained difficult to test. Here, we demonstrate that in the fruit fly Drosophila, the illusory motion percept is generated by unbalanced contributions of direction-selective neurons' responses to stationary edges. First, we found that flies, like humans, perceive sustained motion in the stationary gradients. The percept was abolished when the elementary motion detector neurons T4 and T5 were silenced. In vivo calcium imaging revealed that T4 and T5 neurons encode the location and polarity of stationary edges. Furthermore, our proposed mechanistic model allowed us to predictably manipulate both the magnitude and direction of the fly's illusory percept by selectively silencing either T4 or T5 neurons. Interestingly, human brains possess the same mechanistic ingredients that drive our model in flies. When we adapted human observers to moving light edges or dark edges, we could manipulate the magnitude and direction of their percepts as well, suggesting that mechanisms similar to the fly's may also underlie this illusion in humans. By taking a comparative approach that exploits Drosophila neurogenetics, our results provide a causal, mechanistic account for a long-known visual illusion. These results argue that this illusion arises from architectures for motion detection that are shared across phyla.

A minimal synaptic model for direction selective neurons in Drosophila

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. Here we use 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. We show how this model relates mathematically to classical models of motion detection, including the Hassenstein-Reichardt correlator model. We used numerical simulation 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 our understanding must be improved.

Functional Specialization of ON and OFF Cortical Pathways for Global-Slow and Local-Fast Vision

Visual information is processed in the cortex by ON and OFF pathways that respond to light and dark stimuli. Responses to darks are stronger, faster, and driven by a larger number of cortical neurons than responses to lights. Here, we demonstrate that these light-dark cortical asymmetries reflect a functional specialization of ON and OFF pathways for different stimulus properties. We show that large long-lasting stimuli drive stronger cortical responses when they are light, whereas small fast stimuli drive stronger cortical responses when they are dark. Moreover, we show that these light-dark asymmetries are preserved under a wide variety of luminance conditions that range from photopic to low mesopic light. Our results suggest that ON and OFF pathways extract different spatiotemporal information from visual scenes, making OFF local-fast signals better suited to maximize visual acuity and ON global-slow signals better suited to guide the eye movements needed for retinal image stabilization.

SPARC enables genetic manipulation of precise proportions of cells

Many experimental approaches rely on controlling gene expression in select subsets of cells within an individual animal. However, reproducibly targeting transgene expression to specific fractions of a genetically defined cell type is challenging. We developed Sparse Predictive Activity through Recombinase Competition (SPARC), a generalizable toolkit that can express any effector in precise proportions of post-mitotic cells in Drosophila. Using this approach, we demonstrate targeted expression of many effectors in several cell types and apply these tools to calcium imaging of individual neurons and optogenetic manipulation of sparse cell populations in vivo.

A Neural Representation of Naturalistic Motion-Guided Behavior in the Zebrafish Brain

All animals must transform ambiguous sensory data into successful behavior. This requires sensory representations that accurately reflect the statistics of natural stimuli and behavior. Multiple studies show that visual motion processing is tuned for accuracy under naturalistic conditions, but the sensorimotor circuits extracting these cues and implementing motion-guided behavior remain unclear. Here we show that the larval zebrafish retina extracts a diversity of naturalistic motion cues, and the retinorecipient pretectum organizes these cues around the elements of behavior. We find that higher-order motion stimuli, gliders, induce optomotor behavior matching expectations from natural scene analyses. We then image activity of retinal ganglion cell terminals and pretectal neurons. The retina exhibits direction-selective responses across glider stimuli, and anatomically clustered pretectal neurons respond with magnitudes matching behavior. Peripheral computations thus reflect natural input statistics, whereas central brain activity precisely codes information needed for behavior. This general principle could organize sensorimotor transformations across animal species.

Heterogeneous Temporal Contrast Adaptation in Drosophila Direction-Selective Circuits

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. Here, we 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.

Dynamic Signal Compression for Robust Motion Vision in Flies

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.

Asymmetric ON-OFF processing of visual motion cancels variability induced by the structure of natural scenes

Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here, we used systematic behavioral measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we show that the triplet correlations are useful only when images have light-dark asymmetries that mimic natural ones. This suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.

Dynamic nonlinearities enable direction opponency in Drosophila elementary motion detectors

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. Here, we report 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.

Image statistics of the environment surrounding freely behaving hoverflies

Natural scenes are not as random as they might appear, but are constrained in both space and time. The 2-dimensional spatial constraints can be described by quantifying the image statistics of photographs. Human observers perceive images with naturalistic image statistics as more pleasant to view, and both fly and vertebrate peripheral and higher order visual neurons are tuned to naturalistic image statistics. However, for a given animal, what is natural differs depending on the behavior, and even if we have a broad understanding of image statistics, we know less about the scenes relevant for particular behaviors. To mitigate this, we here investigate the image statistics surrounding Episyrphus balteatus hoverflies, where the males hover in sun shafts created by surrounding trees, producing a rich and dense background texture and also intricate shadow patterns on the ground. We quantified the image statistics of photographs of the ground and the surrounding panorama, as the ventral and lateral visual field is particularly important for visual flight control, and found differences in spatial statistics in photos where the hoverflies were hovering compared to where they were flying. Our results can, in the future, be used to create more naturalistic stimuli for experimenter-controlled experiments in the laboratory.

Acute sleep loss induces signs of visual discomfort in young men

Acute sleep loss influences visual processes in humans, such as recognizing facial emotions. However, to the best of our knowledge, no study till date has examined whether acute sleep loss alters visual comfort when looking at images. One image statistic that can be used to investigate the level of visual comfort experienced under visual encoding is the slope of the amplitude spectrum, also referred to as the slope constant. The slope constant describes the spatial distribution of pixel intensities and deviations from the natural slope constant can induce visual discomfort. In the present counterbalanced crossover design study, 11 young men with normal or corrected‐to‐normal vision participated in two experimental conditions: one night of sleep loss and one night of sleep. In the morning after each intervention, subjects performed a computerized psychophysics task. Specifically, they were required to adjust the slope constant of images depicting natural landscapes and close‐ups with a randomly chosen initial slope constant until they perceived each image as most natural looking. Subjects also rated the pleasantness of each selected image. Our analysis showed that following sleep loss, higher slope constants were perceived as most natural looking when viewing images of natural landscapes. Images with a higher slope constant are generally perceived as blurrier. The selected images were also rated as less pleasant after sleep loss. No such differences between the experimental conditions were noted for images of close‐ups. The results suggest that sleep loss induces signs of visual discomfort in young men. Possible implications of these findings are discussed.

Visual Control of Walking Speed in Drosophila

An animal's self-motion generates optic flow across its retina, and it can use this visual signal to regulate its orientation and speed through the world. While orientation control has been studied extensively in Drosophila and other insects, much less is known about the visual cues and circuits that regulate translational speed. Here, we show that flies regulate walking speed with an algorithm that is tuned to the speed of visual motion, causing them to slow when visual objects are nearby. This regulation does not depend strongly on the spatial structure or the direction of visual stimuli, making it algorithmically distinct from the classic computation that controls orientation. Despite the different algorithms, the visual circuits that regulate walking speed overlap with those that regulate orientation. Taken together, our findings suggest that walking speed is controlled by a hierarchical computation that combines multiple motion detectors with distinct tunings. VIDEO ABSTRACT.

Characterization of Retinal Functionality at Different Eccentricities in a Diurnal Rodent

Although the properties of the neurons of the visual system that process central and peripheral regions of the visual field have been widely researched in the visual cortex and the LGN, they have scarcely been documented for the retina. The retina is the first step in integrating optical signals, and despite considerable efforts to functionally characterize the different types of retinal ganglion cells (RGCs), a clear account of the particular functionality of cells with central vs. peripheral fields is still wanting. Here, we use electrophysiological recordings, gathered from retinas of the diurnal rodent Octodon degus, to show that RGCs with peripheral receptive fields (RF) are larger, faster, and have shorter transient responses. This translates into higher sensitivity at high temporal frequencies and a full frequency bandwidth when compared to RGCs with more central RF. We also observed that imbalances between ON and OFF cell populations are preserved with eccentricity. Finally, the high diversity of functional types of RGCs highlights the complexity of the computational strategies implemented in the early stages of visual processing, which could inspire the development of bio-inspired artificial systems.

The Neuronal Basis of an Illusory Motion Percept Is Explained by Decorrelation of Parallel Motion Pathways

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. Here, we show 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.

Glutamate Signaling in the Fly Visual System

For a proper understanding of neural circuit function, it is important to know which signals neurons relay to their downstream partners. Calcium imaging with genetically encoded calcium sensors like GCaMP has become the default approach for mapping these responses. How well such measurements represent the true neurotransmitter output of any given cell, however, remains unclear. Here, we demonstrate the viability of the glutamate sensor iGluSnFR for 2-photon in vivo imaging in Drosophila melanogaster and prove its usefulness for estimating spatiotemporal receptive fields in the visual system. We compare the results obtained with iGluSnFR with the ones obtained with GCaMP6f and find that the spatial aspects of the receptive fields are preserved between indicators. In the temporal domain, however, measurements obtained with iGluSnFR reveal the underlying response properties to be much faster than those acquired with GCaMP6f. Our approach thus offers a more accurate description of glutamatergic neurons in the fruit fly.

Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation

Shaping the collision selectivity in vision-based artificial collision-detecting systems is still an open challenge. This paper presents a novel neuron model of a locust looming detector, i.e. the lobula giant movement detector (LGMD1), in order to provide effective solutions to enhance the collision selectivity of looming objects over other visual challenges. We propose an approach to model the biologically plausible mechanisms of ON and OFF pathways and a biophysical mechanism of spike frequency adaptation (SFA) in the proposed LGMD1 visual neural network. The ON and OFF pathways can separate both dark and light looming features for parallel spatiotemporal computations. This works effectively on perceiving a potential collision from dark or light objects that approach; such a bio-plausible structure can also separate LGMD1's collision selectivity to its neighbouring looming detector - the LGMD2. The SFA mechanism can enhance the LGMD1's collision selectivity to approaching objects rather than receding and translating stimuli, which is a significant improvement compared with similar LGMD1 neuron models. The proposed framework has been tested using off-line tests of synthetic and real-world stimuli, as well as on-line bio-robotic tests. The enhanced collision selectivity of the proposed model has been validated in systematic experiments. The computational simplicity and robustness of this work have also been verified by the bio-robotic tests, which demonstrates potential in building neuromorphic sensors for collision detection in both a fast and reliable manner.

Linear Summation Underlies Direction Selectivity in Drosophila

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. We have 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, we measure the emergence of direction-selective (DS) voltage signals in the Drosophila OFF pathway. Our 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. VIDEO ABSTRACT.

Elementary Motion Detection in Drosophila: Algorithms and Mechanisms

Motion in the visual world provides critical information to guide the behavior of sighted animals. Furthermore, as visual motion estimation requires comparisons of signals across inputs and over time, it represents a paradigmatic and generalizable neural computation. Focusing on the Drosophila visual system, where an explosion of technological advances has recently accelerated experimental progress, we review our understanding of how, algorithmically and mechanistically, motion signals are first computed.

Motion changes response balance between ON and OFF visual pathways

Humans are faster at detecting dark than light stationary stimuli, a temporal difference that originates early in the visual pathway. Here we show that this difference reverses when stimuli move, making detection faster for moving lights than darks. Human subjects judged the direction of moving edges and bars, and made faster and more accurate responses for light than for dark stimuli. This light/dark asymmetry is greatest at low speeds and disappears at high speeds. In parallel experiments, we recorded responses in the cat visual cortex for moving bars and again find that responses are faster for light bars than for dark bars moving at low speeds. We show that differences in the luminance-response function between ON and OFF pathways can reproduce these findings, and may explain why ON pathways are used for slow-motion image stabilization in many species.

A Novel Form of Stereo Vision in the Praying Mantis

Stereopsis is the ability to estimate distance based on the different views seen in the two eyes [1-5]. It is an important model perceptual system in neuroscience and a major area of machine vision. Mammalian, avian, and almost all machine stereo algorithms look for similarities between the luminance-defined images in the two eyes, using a series of computations to produce a map showing how depth varies across the scene [3, 4, 6-14]. Stereopsis has also evolved in at least one invertebrate, the praying mantis [15-17]. Mantis stereopsis is presumed to be simpler than vertebrates' [15, 18], but little is currently known about the underlying computations. Here, we show that mantis stereopsis uses a fundamentally different computational algorithm from vertebrate stereopsis-rather than comparing luminance in the two eyes' images directly, mantis stereopsis looks for regions of the images where luminance is changing. Thus, while there is no evidence that mantis stereopsis works at all with static images, it successfully reveals the distance to a moving target even in complex visual scenes with targets that are perfectly camouflaged against the background in terms of texture. Strikingly, these insects outperform human observers at judging stereoscopic distance when the pattern of luminance in the two eyes does not match. Insect stereopsis has thus evolved to be computationally efficient while being robust to poor image resolution and to discrepancies in the pattern of luminance between the two eyes. VIDEO ABSTRACT.

Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila

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.

Parallel Computations in Insect and Mammalian Visual Motion Processing

Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world.

Neural mechanisms underlying sensitivity to reverse-phi motion in the fly

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. Here, we map 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, we demonstrate 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, we suggest that similar mechanisms may apply even to human psychophysics.