Common circuit design in fly and mammalian motion vision

Lamina feedback neurons regulate the bandpass property of the flicker-induced orientation response in Drosophila

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

Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding

Attentional selectivity tends to follow events considered as interesting stimuli. Indeed, the motion of visual stimuli present in the environment attract our attention and allow us to react and interact with our surroundings. Extracting relevant motion information from the environment presents a challenge with regards to the high information content of the visual input. In this work we propose a novel integration between an eccentric down-sampling of the visual field, taking inspiration from the varying size of receptive fields (RFs) in the mammalian retina, and the Spiking Elementary Motion Detector (sEMD) model. We characterize the system functionality with simulated data and real world data collected with bio-inspired event driven cameras, successfully implementing motion detection along the four cardinal directions and diagonally.

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.

Differentiation of Theta Visual Motion from Fourier Motion Requires LC16 and R18C12 Neurons in Drosophila

Many animals perceive features of higher-order visual motion that are beyond the spatiotemporal correlations of luminance defined in first-order motion. Although the neural mechanisms of first-order motion detection have become understood in recent years, those underlying higher-order motion perception remain unclear. Here, we established a paradigm to assess the detection of theta motion—a type of higher-order motion—in freely walking Drosophila. Behavioral screening using this paradigm identified two clusters of neurons in the central brain, designated as R18C12, which were required for perception of theta motion but not for first-order motion. Furthermore, theta motion-activated R18C12 neurons were structurally and functionally located downstream of visual projection neurons in lobula, lobula columnar cells LC16, which activated R18C12 neurons via interactions of acetylcholine (ACh) and muscarinic acetylcholine receptors (mAChRs). The current study provides new insights into LC neurons and the neuronal mechanisms underlying visual information processing in complex natural scenes.

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Perception of theta motion requires LC16 and R18C12 neurons

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R18C12 neurons are activated by theta motion

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R18C12 neurons form synaptic connections with LC16 neurons

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LC16 neurons activate R18C12 neurons through ACh acting on mAChR

Thalamocortical Circuit Motifs: A General Framework

The role of the thalamus in cortical sensory transmission is well known, but its broader role in cognition is less appreciated. Recent studies have shown thalamic engagement in dynamic regulation of cortical activity in attention, executive control, and perceptual decision-making, but the circuit mechanisms underlying such functionality are unknown. Because the thalamus is composed of excitatory neurons that are devoid of local recurrent excitatory connectivity, delineating long-range, input-output connectivity patterns of single thalamic neurons is critical for building functional models. We discuss this need in relation to existing organizational schemes such as core versus matrix and first-order versus higher-order relay nuclei. We propose that a new classification is needed based on thalamocortical motifs, where structure naturally informs function. Overall, our synthesis puts understanding thalamic organization at the forefront of existing research in systems and computational neuroscience, with both basic and translational applications.

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.

Neuronal Morphology and Synapse Count in the Nematode Worm

The somatic nervous system of the nematode worm Caenorhabditis elegans is a model for understanding the physical characteristics of the neurons and their interconnections. Its neurons show high variation in morphological attributes. This study investigates the relationship of neuronal morphology to the number of synapses per neuron. Morphology is also examined for any detectable association with neuron cell type or ganglion membership.

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is a fundamental component of visual computation, essential for survival of the animal. However, at the level of individual photoreceptors, the direction in which the image is shifting is not explicitly represented. Rather, directional motion information needs to be extracted from the photoreceptor array by comparing the signals of neighboring units over time. The exact nature of this process as implemented in the visual system of the fruit fly Drosophila melanogaster has been studied in great detail, and much progress has recently been made in determining the neural circuits giving rise to directional motion information. The results reveal the following: (1) motion information is computed in parallel ON and OFF pathways. (2) Within each pathway, T4 (ON) and T5 (OFF) cells are the first neurons to represent the direction of motion. Four subtypes of T4 and T5 cells exist, each sensitive to one of the four cardinal directions. (3) The core process of direction selectivity as implemented on the dendrites of T4 and T5 cells comprises both an enhancement of signals for motion along their preferred direction as well as a suppression of signals for motion along the opposite direction. This combined strategy ensures a high degree of direction selectivity right at the first stage where the direction of motion is computed. (4) At the subsequent processing stage, tangential cells spatially integrate direct excitation from ON and OFF-selective T4 and T5 cells and indirect inhibition from bi-stratified LPi cells activated by neighboring T4/T5 terminals, thus generating flow-field-selective responses.

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.

The Role of Landscapes and Landmarks in Bee Navigation: A Review

The ability of animals to explore landmarks in their environment is essential to their fitness. Landmarks are widely recognized to play a key role in navigation by providing information in multiple sensory modalities. However, what is a landmark? We propose that animals use a hierarchy of information based upon its utility and salience when an animal is in a given motivational state. Focusing on honeybees, we suggest that foragers choose landmarks based upon their relative uniqueness, conspicuousness, stability, and context. We also propose that it is useful to distinguish between landmarks that provide sensory input that changes ("near") or does not change ("far") as the receiver uses these landmarks to navigate. However, we recognize that this distinction occurs on a continuum and is not a clear-cut dichotomy. We review the rich literature on landmarks, focusing on recent studies that have illuminated our understanding of the kinds of information that bees use, how they use it, potential mechanisms, and future research directions.

Differential Tuning to Visual Motion Allows Robust Encoding of Optic Flow in the Dragonfly

Visual cues provide an important means for aerial creatures to ascertain their self-motion through the environment. In many insects, including flies, moths, and bees, wide-field motion-sensitive neurons in the third optic ganglion are thought to underlie such motion encoding; however, these neurons can only respond robustly over limited speed ranges. The task is more complicated for some species of dragonflies that switch between extended periods of hovering flight and fast-moving pursuit of prey and conspecifics, requiring motion detection over a broad range of velocities. Since little is known about motion processing in these insects, we performed intracellular recordings from hawking, emerald dragonflies (Hemicordulia spp.) and identified a diverse group of motion-sensitive neurons that we named lobula tangential cells (LTCs). Following prolonged visual stimulation with drifting gratings, we observed significant differences in both temporal and spatial tuning of LTCs. Cluster analysis of these changes confirmed several groups of LTCs with distinctive spatiotemporal tuning. These differences were associated with variation in velocity tuning in response to translated, natural scenes. LTCs with differences in velocity tuning ranges and optima may underlie how a broad range of motion velocities are encoded. In the hawking dragonfly, changes in LTC tuning over time are therefore likely to support their extensive range of behaviors, from hovering to fast-speed pursuits.SIGNIFICANCE STATEMENT Understanding how animals navigate the world is an inherently difficult and interesting problem. Insects are useful models for understanding neuronal mechanisms underlying these activities, with neurons that encode wide-field motion previously identified in insects, such as flies, hawkmoths, and butterflies. Like some Dipteran flies, dragonflies exhibit complex aerobatic behaviors, such as hovering, patrolling, and aerial combat. However, dragonflies lack halteres that support such diverse behavior in flies. To understand how dragonflies might address this problem using only visual cues, we recorded from their wide-field motion-sensitive neurons. We found these differ strongly in the ways they respond to sustained motion, allowing them collectively to encode the very broad range of velocities experienced during diverse behavior.

Spatiotemporally Asymmetric Excitation Supports Mammalian Retinal Motion Sensitivity

The detection of visual motion is a fundamental function of the visual system. How motion speed and direction are computed together at the cellular level, however, remains largely unknown. Here, we suggest a circuit mechanism by which excitatory inputs to direction-selective ganglion cells in the mouse retina become sensitive to the motion speed and direction of image motion. Electrophysiological, imaging, and connectomic analyses provide evidence that the dendrites of ON direction-selective cells receive spatially offset and asymmetrically filtered glutamatergic inputs along motion-preference axis from asymmetrically wired bipolar and amacrine cell types with distinct release dynamics. A computational model shows that, with this spatiotemporal structure, the input amplitude becomes sensitive to speed and direction by a preferred direction enhancement mechanism. Our results highlight the role of an excitatory mechanism in retinal motion computation by which feature selectivity emerges from non-selective inputs.

Muscarinic acetylcholine receptor signaling generates OFF selectivity in a simple visual circuit

ON and OFF selectivity in visual processing is encoded by parallel pathways that respond to either light increments or decrements. Despite lacking the anatomical features to support split channels, Drosophila larvae effectively perform visually-guided behaviors. To understand principles guiding visual computation in this simple circuit, we focus on investigating the physiological properties and behavioral relevance of larval visual interneurons. We find that the ON vs. OFF discrimination in the larval visual circuit emerges through light-elicited cholinergic signaling that depolarizes a cholinergic interneuron (cha-lOLP) and hyperpolarizes a glutamatergic interneuron (glu-lOLP). Genetic studies further indicate that muscarinic acetylcholine receptor (mAchR)/Gαo signaling produces the sign-inversion required for OFF detection in glu-lOLP, the disruption of which strongly impacts both physiological responses of downstream projection neurons and dark-induced pausing behavior. Together, our studies identify the molecular and circuit mechanisms underlying ON vs. OFF discrimination in the Drosophila larval visual system.

Drosophila larvae are able to perform visually-guided behaviours yet the molecular and circuit mechanisms for discriminating changes in light intensity are not known. Here, the authors report that ON versus OFF discrimination results from opposing cholinergic and glutamatergic mechanisms.

Analysis of the genetically tractable crustacean Parhyale hawaiensis reveals the organisation of a sensory system for low-resolution vision

BACKGROUND

Arthropod eyes have diversified during evolution to serve multiple needs, such as finding mates, hunting prey and navigating in complex surroundings under varying light conditions. This diversity is reflected in the optical apparatus, photoreceptors and neural circuits that underpin vision. Yet our ability to genetically manipulate the visual system to investigate its function is largely limited to a single species, the fruit fly Drosophila melanogaster. Here, we describe the visual system of Parhyale hawaiensis, an amphipod crustacean for which we have established tailored genetic tools.

RESULTS

Adult Parhyale have apposition-type compound eyes made up of ~ 50 ommatidia. Each ommatidium contains four photoreceptor cells with large rhabdomeres (R1-4), expected to be sensitive to the polarisation of light, and one photoreceptor cell with a smaller rhabdomere (R5). The two types of photoreceptors express different opsins, belonging to families with distinct wavelength sensitivities. Using the cis-regulatory regions of opsin genes, we established transgenic reporters expressed in each photoreceptor cell type. Based on these reporters, we show that R1-4 and R5 photoreceptors extend axons to the first optic lobe neuropil, revealing striking differences compared with the photoreceptor projections found in related crustaceans and insects. Investigating visual function, we show that Parhyale have a positive phototactic response and are capable of adapting their eyes to different levels of light intensity.

CONCLUSIONS

We propose that the visual system of Parhyale serves low-resolution visual tasks, such as orientation and navigation, based on broad gradients of light intensity and polarisation. Optic lobe structure and photoreceptor projections point to significant divergence from the typical organisation found in other malacostracan crustaceans and insects, which could be associated with a shift to low-resolution vision. Our study provides the foundation for research in the visual system of this genetically tractable species.

Visual experience drives sleep need in Drosophila

Sleep optimizes waking behavior, however, waking experience may also influence sleep. We used the fruit fly Drosophila melanogaster to investigate the relationship between visual experience and sleep in wild-type and mutant flies. We found that the classical visual mutant, optomotor-blind (omb), which has undeveloped horizontal system/vertical system (HS/VS) motion-processing cells and are defective in motion and visual salience perception, showed dramatically reduced and less consolidated sleep compared to wild-type flies. In contrast, optogenetic activation of the HS/VS motion-processing neurons in wild-type flies led to an increase in sleep following the activation, suggesting an increase in sleep pressure. Surprisingly, exposing wild-type flies to repetitive motion stimuli for extended periods did not increase sleep pressure. However, we observed that exposing flies to more complex image sequences from a movie led to more consolidated sleep, particularly when images were randomly shuffled through time. Our results suggest that specific forms of visual experience that involve motion circuits and complex, nonrepetitive imagery, drive sleep need in Drosophila.

Infrared Imaging: A Motion Detection Circuit for Rattlesnake Thermal Vision

Pit vipers detect moving warm-blooded prey with infrared receptors in their pit organs. Neurons in two brain nuclei extract the direction of prey motion by lateral inhibition circuits similar to those known from visual systems.

Comparisons between the ON- and OFF-edge motion pathways in the Drosophila brain

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

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

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.

Multi-Beam Scanning Electron Microscopy for High-Throughput Imaging in Connectomics Research

Major progress has been achieved in recent years in three-dimensional microscopy techniques. This applies to the life sciences in general, but specifically the neuroscientific field has been a main driver for developments regarding volume imaging. In particular, scanning electron microscopy offers new insights into the organization of cells and tissues by volume imaging methods, such as serial section array tomography, serial block-face imaging or focused ion beam tomography. However, most of these techniques are restricted to relatively small tissue volumes due to the limited acquisition throughput of most standard imaging techniques. Recently, a novel multi-beam scanning electron microscope technology optimized to the imaging of large sample areas has been developed. Complemented by the commercialization of automated sample preparation robots, the mapping of larger, cubic millimeter range tissue volumes at high-resolution is now within reach. This Mini Review will provide a brief overview of the various approaches to electron microscopic volume imaging, with an emphasis on serial section array tomography and multi-beam scanning electron microscopic imaging.

The Circuit Motif as a Conceptual Tool for Multilevel Neuroscience

Modern neuroscientific techniques that specifically manipulate and measure neuronal activity in behaving animals now allow bridging of the gap from the cellular to the behavioral level. However, in doing so, they also pose new challenges. Research using incompletely defined manipulations in a high-dimensional space without clear hypotheses is likely to suffer from multiple well-known conceptual and statistical problems. In this context it is essential to develop hypotheses with testable implications across levels. Here we propose that a focus on circuit motifs can help achieve this goal. Viewing neural structures as an assembly of circuit motif building blocks is not new. However, recent tool advances have made it possible to extensively map, specifically manipulate, and quantitatively investigate circuit motifs and thereby reexamine their relevance to brain function.

Bi-directional Control of Walking Behavior by Horizontal Optic Flow Sensors

Moving animals experience constant sensory feedback, such as panoramic image shifts on the retina, termed optic flow. Underlying neuronal signals are thought to be important for exploratory behavior by signaling unintended course deviations and by providing spatial information about the environment [1, 2]. Particularly in insects, the encoding of self-motion-related optic flow is well understood [1-5]. However, a gap remains in understanding how the associated neuronal activity controls locomotor trajectories. In flies, visual projection neurons belonging to two groups encode panoramic horizontal motion: horizontal system (HS) cells respond with depolarization to front-to-back motion and hyperpolarization to the opposite direction [6, 7], and other neurons have the mirror-symmetrical response profile [6, 8, 9]. With primarily monocular sensitivity, the neurons' responses are ambiguous for different rotational and translational self-movement components. Such ambiguities can be greatly reduced by combining signals from both eyes [10-12] to determine turning and movement speed [13-16]. Here, we explore the underlying functional logic by optogenetic HS cell manipulation in tethered walking Drosophila. We show that de- and hyperpolarization evoke opposite turning behavior, indicating that both direction-selective signals are transmitted to descending pathways for course control. Further experiments reveal a negative effect of bilaterally symmetric de- and hyperpolarization on walking velocity. Our results are therefore consistent with a functional architecture in which the HS cells' membrane potential influences walking behavior bi-directionally via two decelerating pathways.

Insect Neurobiology: Optical Illusions at the Cellular Level

The reverse-phi illusion has been one key to understanding the use of correlation for motion perception in humans and many other animals. A new study finds the source of this illusion at the cellular level in fruit flies.

Insect-inspired neuromorphic computing

The steady improvement in the performance of computing systems seen for many decades is levelling off as the miniaturization of semiconducting technology approaches the atomic limit, facing severe physical and technological issues. Neuromorphic computing is an emerging solution which makes use of silicon technology in a different way, inline with the computational principles observed in animal nervous systems. In this article, we argue that the nervous systems of insects in particular offer themselves as an ideal starting point for incorporation into realistic neuromorphic systems and review research in developing insect-inspired neuromorphic systems. We conclude with an exciting yet tangible vision of a full neuromorphic sensory-motor system where a liquid state machine modelling the function of the insect mushroom body links input to output and allows for amalgamation of the work discussed in a hierarchical framework of a full system inspired by the concept of how information flows through insects.

Full reconstruction of large lobula plate tangential cells in Drosophila from a 3D EM dataset

With the advent of neurogenetic methods, the neural basis of behavior is presently being analyzed in more and more detail. This is particularly true for visually driven behavior of Drosophila melanogaster where cell-specific driver lines exist that, depending on the combination with appropriate effector genes, allow for targeted recording, silencing and optogenetic stimulation of individual cell-types. Together with detailed connectomic data of large parts of the fly optic lobe, this has recently led to much progress in our understanding of the neural circuits underlying local motion detection. However, how such local information is combined by optic flow sensitive large-field neurons is still incompletely understood. Here, we aim to fill this gap by a dense reconstruction of lobula plate tangential cells of the fly lobula plate. These neurons collect input from many hundreds of local motion-sensing T4/T5 neurons and connect them to descending neurons or central brain areas. We confirm all basic features of HS and VS cells as published previously from light microscopy. In addition, we identified the dorsal and the ventral centrifugal horizontal, dCH and vCH cell, as well as three VSlike cells, including their distinct dendritic and axonal projection area.

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.

The transcription factor SoxD controls neuronal guidance in the Drosophila visual system

Precise control of neurite guidance during development is essential to ensure proper formation of neuronal networks and correct function of the central nervous system (CNS). How neuronal projections find their targets to generate appropriate synapses is not entirely understood. Although transcription factors are key molecules during neurogenesis, we do not know their entire function during the formation of networks in the CNS. Here, we used the Drosophila melanogaster optic lobe as a model for understanding neurite guidance during development. We assessed the function of Sox102F/SoxD, the unique Drosophila orthologue of the vertebrate SoxD family of transcription factors. SoxD is expressed in immature and mature neurons in the larval and adult lobula plate ganglia (one of the optic lobe neuropils), but is absent from glial cells, neural stem cells and progenitors of the lobula plate. SoxD RNAi knockdown in all neurons results in a reduction of the lobula plate neuropil, without affecting neuronal fate. This morphological defect is associated with an impaired optomotor response of adult flies. Moreover, knocking down SoxD only in T4/T5 neuronal types, which control motion vision, affects proper neurite guidance into the medulla and lobula. Our findings suggest that SoxD regulates neurite guidance, without affecting neuronal fate.

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.

Differential tuning of excitation and inhibition shapes direction selectivity in ferret visual cortex

To encode specific sensory inputs, cortical neurons must generate selective responses for distinct stimulus features. In principle, a variety of factors contribute to a cortical neuron’s response selectivity: the tuning and strength of excitatory^1–3 and inhibitory synaptic inputs^4–6, dendritic nonlinearities^7–9, and spike threshold^10,11. Here we employ a combination of techniques including in vivo whole-cell recording, synaptic and cellular resolution in vivo two photon calcium imaging, and GABAergic-selective optogenetic manipulation to dissect the factors contributing to direction selective responses of layer 2/3 neurons in ferret visual cortex (V1). Two-photon calcium imaging of dendritic spines^12,13 revealed that each neuron receives a mixture of excitatory synaptic inputs selective for the somatic preferred or null direction of motion. The relative number of preferred- and null-tuned excitatory inputs predicted a neuron’s somatic direction preference, but failed to account for the degree of direction selectivity. In contrast, in vivo whole-cell patch clamp recordings revealed a striking degree of direction selectivity in subthreshold responses that was significantly correlated with spiking direction selectivity. Subthreshold direction selectivity was predicted by the magnitude and variance of the response to the null direction of motion, and several lines of evidence including conductance measurements demonstrate that differential tuning of excitation and inhibition suppresses responses to the null direction of motion. Consistent with this idea, optogenetic inactivation of GABAergic neurons in layer 2/3 reduced direction selectivity by enhancing responses to the null direction. Furthermore, using a new technique to optogenetically map connections of inhibitory neurons in layer 2/3 in vivo, we find that layer 2/3 inhibitory neurons make long-range, intercolumnar projections to excitatory neurons that prefer the opposite direction of motion. We conclude that intracortical inhibition exerts a major influence on the degree of direction selectivity in layer 2/3 of ferret V1 by suppressing responses to the null direction of motion.

Spiking Elementary Motion Detector in Neuromorphic Systems

Apparent motion of the surroundings on an agent's retina can be used to navigate through cluttered environments, avoid collisions with obstacles, or track targets of interest. The pattern of apparent motion of objects, (i.e., the optic flow), contains spatial information about the surrounding environment. For a small, fast-moving agent, as used in search and rescue missions, it is crucial to estimate the distance to close-by objects to avoid collisions quickly. This estimation cannot be done by conventional methods, such as frame-based optic flow estimation, given the size, power, and latency constraints of the necessary hardware. A practical alternative makes use of event-based vision sensors. Contrary to the frame-based approach, they produce so-called events only when there are changes in the visual scene. We propose a novel asynchronous circuit, the spiking elementary motion detector (sEMD), composed of a single silicon neuron and synapse, to detect elementary motion from an event-based vision sensor. The sEMD encodes the time an object's image needs to travel across the retina into a burst of spikes. The number of spikes within the burst is proportional to the speed of events across the retina. A fast but imprecise estimate of the time-to-travel can already be obtained from the first two spikes of a burst and refined by subsequent interspike intervals. The latter encoding scheme is possible due to an adaptive nonlinear synaptic efficacy scaling. We show that the sEMD can be used to compute a collision avoidance direction in the context of robotic navigation in a cluttered outdoor environment and compared the collision avoidance direction to a frame-based algorithm. The proposed computational principle constitutes a generic spiking temporal correlation detector that can be applied to other sensory modalities (e.g., sound localization), and it provides a novel perspective to gating information in spiking neural networks.

A biophysical mechanism for preferred direction enhancement in fly motion vision

Seeing the direction of motion is essential for survival of all sighted animals. Consequently, nerve cells that respond to visual stimuli moving in one but not in the opposite direction, so-called 'direction-selective' neurons, are found abundantly. In general, direction selectivity can arise by either signal amplification for stimuli moving in the cell's preferred direction ('preferred direction enhancement'), signal suppression for stimuli moving along the opposite direction ('null direction suppression'), or a combination of both. While signal suppression can be readily implemented in biophysical terms by a hyperpolarization followed by a rectification corresponding to the nonlinear voltage-dependence of the Calcium channel, the biophysical mechanism for signal amplification has remained unclear so far. Taking inspiration from the fly, I analyze a neural circuit where a direction-selective ON-cell receives inhibitory input from an OFF cell on the preferred side of the dendrite, while excitatory ON-cells contact the dendrite centrally. This way, an ON edge moving along the cell's preferred direction suppresses the inhibitory input, leading to a release from inhibition in the postsynaptic cell. The benefit of such a two-fold signal inversion lies in the resulting increase of the postsynaptic cell's input resistance, amplifying its response to a subsequent excitatory input signal even with a passive dendrite, i.e. without voltage-gated ion channels. A motion detector implementing this mechanism together with null direction suppression shows a high degree of direction selectivity over a large range of temporal frequency, narrow directional tuning, and a large signal-to-noise ratio.

Of what use is connectomics? A personal perspective on the Drosophila connectome

The brain is a network of neurons and its biological output is behaviour. This is an exciting age, with a growing acknowledgement that the comprehensive compilation of synaptic circuits densely reconstructed in the brains of model species is now both technologically feasible and a scientifically enabling possibility in neurobiology, much as 30 years ago genomics was in molecular biology and genetics. Implemented by huge advances in electron microscope technology, especially focused ion beam-scanning electron microscope (FIB-SEM) milling (see Glossary), image capture and alignment, and computer-aided reconstruction of neuron morphologies, enormous progress has been made in the last decade in the detailed knowledge of the actual synaptic circuits formed by real neurons, in various brain regions of the fly Drosophila It is useful to distinguish synaptic pathways that are major, with 100 or more presynaptic contacts, from those that are minor, with fewer than about 10; most neurites are both presynaptic and postsynaptic, and all synaptic sites have multiple postsynaptic dendrites. Work on Drosophila has spearheaded these advances because cell numbers are manageable, and neuron classes are morphologically discrete and genetically identifiable, many confirmed by reporters. Recent advances are destined within the next few years to reveal the complete connectome in an adult fly, paralleling advances in the larval brain that offer the same prospect possibly within an even shorter time frame. The final amendment and validation of segmented bodies by human proof-readers remains the most time-consuming step, however. The value of a complete connectome in Drosophila is that, by targeting to specific neurons transgenes that either silence or activate morphologically identified circuits, and then identifying the resulting behavioural outcome, we can determine the causal mechanism for behaviour from its loss or gain. More importantly, the connectome reveals hitherto unsuspected pathways, leading us to seek novel behaviours for these. Circuit information will eventually be required to understand how differences between brains underlie differences in behaviour, and especially to herald yet more advanced connectomic strategies for the vertebrate brain, with an eventual prospect of understanding cognitive disorders having a connectomic basis. Connectomes also help us to identify common synaptic circuits in different species and thus to reveal an evolutionary progression in candidate pathways.

Non-uniform weighting of local motion inputs underlies dendritic computation in the fly visual system

The fly visual system offers a unique opportunity to explore computations performed by single neurons. Two previous studies characterized, in vivo, the receptive field (RF) of the vertical system (VS) cells of the blowfly (calliphora vicina), both intracellularly in the axon, and, independently using Ca²⁺ imaging, in hundreds of distal dendritic branchlets. We integrated this information into detailed passive cable and compartmental models of 3D reconstructed VS cells. Within a given VS cell type, the transfer resistance (TR) from different branchlets to the axon differs substantially, suggesting that they contribute unequally to the shaping of the axonal RF. Weighting the local RFs of all dendritic branchlets by their respective TR yielded a faithful reproduction of the axonal RF. The model also predicted that the various dendritic branchlets are electrically decoupled from each other, thus acting as independent local functional subunits. The study suggests that single neurons in the fly visual system filter dendritic noise and compute the weighted average of their inputs.

Neural Mechanisms Mediating Motion Sensitivity in Parasol Ganglion Cells of the Primate Retina

Considerable theoretical and experimental effort has been dedicated to understanding how neural circuits detect visual motion. In primates, much is known about the cortical circuits that contribute to motion processing, but the role of the retina in this fundamental neural computation is poorly understood. Here, we used a combination of extracellular and whole-cell recording to test for motion sensitivity in the two main classes of output neurons in the primate retina-midget (parvocellular-projecting) and parasol (magnocellular-projecting) ganglion cells. We report that parasol, but not midget, ganglion cells are motion sensitive. This motion sensitivity is present in synaptic excitation and disinhibition from presynaptic bipolar cells and amacrine cells, respectively. Moreover, electrical coupling between neighboring bipolar cells and the nonlinear nature of synaptic release contribute to the observed motion sensitivity. Our findings indicate that motion computations arise far earlier in the primate visual stream than previously thought.

Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors

In order to safely navigate and orient in their local surroundings autonomous systems need to rapidly extract and persistently track visual features from the environment. While there are many algorithms tackling those tasks for traditional frame-based cameras, these have to deal with the fact that conventional cameras sample their environment with a fixed frequency. Most prominently, the same features have to be found in consecutive frames and corresponding features then need to be matched using elaborate techniques as any information between the two frames is lost. We introduce a novel method to detect and track line structures in data streams of event-based silicon retinae [also known as dynamic vision sensors (DVS)]. In contrast to conventional cameras, these biologically inspired sensors generate a quasicontinuous stream of vision information analogous to the information stream created by the ganglion cells in mammal retinae. All pixels of DVS operate asynchronously without a periodic sampling rate and emit a so-called DVS address event as soon as they perceive a luminance change exceeding an adjustable threshold. We use the high temporal resolution achieved by the DVS to track features continuously through time instead of only at fixed points in time. The focus of this work lies on tracking lines in a mostly static environment which is observed by a moving camera, a typical setting in mobile robotics. Since DVS events are mostly generated at object boundaries and edges which in man-made environments often form lines they were chosen as feature to track. Our method is based on detecting planes of DVS address events in x-y-t-space and tracing these planes through time. It is robust against noise and runs in real time on a standard computer, hence it is suitable for low latency robotics. The efficacy and performance are evaluated on real-world data sets which show artificial structures in an office-building using event data for tracking and frame data for ground-truth estimation from a DAVIS240C sensor.

Fruit flies are multistable geniuses

Our sensory systems have evolved to provide us with information about the external world. Such information is useful only insofar as it leads to actions that enhance fitness, and thus, the link between sensation and action has been thoroughly studied in many species. In insects, for example, specific visual stimuli lead to highly stereotyped responses. In contrast, humans can exhibit a wide range of responses to the same stimulus, as occurs most notably in the phenomenon of multistable perception. On this basis, one might think that humans have a fundamentally different way of generating actions from sensory inputs, but Toepfer et al. show that flies show evidence of multistable perception as well. Specifically, when confronted with a sensory stimulus that can yield different motor responses, flies switch from one response to another with temporal dynamics that are similar to those of humans and other animals. This suggests that the mechanisms that give rise to the rich repertoire of sensory experience in humans have correlates in much simpler nervous systems.

Architecture of a mammalian glomerular domain revealed by novel volume electroporation using nanoengineered microelectrodes

Dense microcircuit reconstruction techniques have begun to provide ultrafine insight into the architecture of small-scale networks. However, identifying the totality of cells belonging to such neuronal modules, the "inputs" and "outputs," remains a major challenge. Here, we present the development of nanoengineered electroporation microelectrodes (NEMs) for comprehensive manipulation of a substantial volume of neuronal tissue. Combining finite element modeling and focused ion beam milling, NEMs permit substantially higher stimulation intensities compared to conventional glass capillaries, allowing for larger volumes configurable to the geometry of the target circuit. We apply NEMs to achieve near-complete labeling of the neuronal network associated with a genetically identified olfactory glomerulus. This allows us to detect sparse higher-order features of the wiring architecture that are inaccessible to statistical labeling approaches. Thus, NEM labeling provides crucial complementary information to dense circuit reconstruction techniques. Relying solely on targeting an electrode to the region of interest and passive biophysical properties largely common across cell types, this can easily be employed anywhere in the CNS.

Local motion adaptation enhances the representation of spatial structure at EMD arrays

Neuronal representation and extraction of spatial information are essential for behavioral control. For flying insects, a plausible way to gain spatial information is to exploit distance-dependent optic flow that is generated during translational self-motion. Optic flow is computed by arrays of local motion detectors retinotopically arranged in the second neuropile layer of the insect visual system. These motion detectors have adaptive response characteristics, i.e. their responses to motion with a constant or only slowly changing velocity decrease, while their sensitivity to rapid velocity changes is maintained or even increases. We analyzed by a modeling approach how motion adaptation affects signal representation at the output of arrays of motion detectors during simulated flight in artificial and natural 3D environments. We focused on translational flight, because spatial information is only contained in the optic flow induced by translational locomotion. Indeed, flies, bees and other insects segregate their flight into relatively long intersaccadic translational flight sections interspersed with brief and rapid saccadic turns, presumably to maximize periods of translation (80% of the flight). With a novel adaptive model of the insect visual motion pathway we could show that the motion detector responses to background structures of cluttered environments are largely attenuated as a consequence of motion adaptation, while responses to foreground objects stay constant or even increase. This conclusion even holds under the dynamic flight conditions of insects.

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.

Efficient encoding of motion is mediated by gap junctions in the fly visual system

Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly’s ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system’s readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.

Understanding sensory stimuli from the environment and deciding how best to respond to it behaviorally is essential for survival. What makes organisms efficient in encoding these sensory stimuli? This study provides a novel view on this unresolved issue using the visual system of the fly. We show that a specific synaptic connectivity manifested via gap junctions (GJs) among axons in the Vertical System (VS) network leads to particularly high encoding efficiency of the axis of rotation of the fly’s ego motion. Due to these GJs, triplets of VS neurons (the VS5-6-7 triplet), which connect to a downstream motor system, encode motion stimuli at an efficiency close to the physical limit; this efficient encoding is necessary for evasive maneuvers that are critical for the fly to escape predators. We then suggest why GJs in the VS network enable such high encoding efficiency.

Learning from connectomics on the fly

Parallels between invertebrates and vertebrates in nervous system development, organisation and circuits are powerful reasons to use insects to study the mechanistic basis of behaviour. The last few years have seen the generation in Drosophila melanogaster of very large light microscopy data sets, genetic driver lines and tools to report or manipulate neural activity. These resources in conjunction with computational tools are enabling large scale characterisation of neuronal types and their functional properties. These are complemented by 3D electron microscopy, providing synaptic resolution data. A whole brain connectome of the fly larva is approaching completion based on manual reconstruction of electron-microscopy data. An adult whole brain dataset is already publicly available and focussed reconstruction is under way, but its 40× greater volume would require ∼500-5000 person-years of manual labour. Nevertheless rapid technical improvements in imaging and especially automated segmentation will likely deliver a complete adult connectome in the next 5 years. To enhance our understanding of the circuit basis of behaviour, light and electron microscopy outputs must be integrated with functional and physiological information into comprehensive databases. We review presently available data, tools and opportunities in Drosophila. We then consider the limits and potential of future progress and how this may impact neuroscience in rich model systems provided by larger insects and vertebrates.

Dissection and Immunofluorescent Staining of Mushroom Body and Photoreceptor Neurons in Adult Drosophila melanogaster Brains

Nervous system development involves a sequential series of events that are coordinated by several signaling pathways and regulatory networks. Many of the proteins involved in these pathways are evolutionarily conserved between mammals and other eukaryotes, such as the fruit fly Drosophila melanogaster, suggesting that similar organizing principles exist during the development of these organisms. Importantly, Drosophila has been used extensively to identify cellular and molecular mechanisms regulating processes that are required in mammals including neurogenesis, differentiation, axonal guidance, and synaptogenesis. Flies have also been used successfully to model a variety of human neurodevelopmental diseases. Here we describe a protocol for the step-by-step microdissection, fixation, and immunofluorescent localization of proteins within the adult Drosophila brain. This protocol focuses on two example neuronal populations, mushroom body neurons and retinal photoreceptors, and includes optional steps to trace individual mushroom body neurons using Mosaic Analysis with a Repressible Cell Marker (MARCM) technique. Example data from both wild-type and mutant brains are shown along with a brief description of a scoring criteria for axonal guidance defects. While this protocol highlights two well-established antibodies for investigating the morphology of mushroom body and photoreceptor neurons, other Drosophila brain regions and the localization of proteins within other brain regions can also be investigated using this protocol.

Optogenetic Neuronal Silencing in Drosophila during Visual Processing

Optogenetic channels and ion pumps have become indispensable tools in neuroscience to manipulate neuronal activity and thus to establish synaptic connectivity and behavioral causality. Inhibitory channels are particularly advantageous to explore signal processing in neural circuits since they permit the functional removal of selected neurons on a trial-by-trial basis. However, applying these tools to study the visual system poses a considerable challenge because the illumination required for their activation usually also stimulates photoreceptors substantially, precluding the simultaneous probing of visual responses. Here, we explore the utility of the recently discovered anion channelrhodopsins GtACR1 and GtACR2 for application in the visual system of Drosophila. We first characterized their properties using a larval crawling assay. We further obtained whole-cell recordings from cells expressing GtACR1, which mediated strong and light-sensitive photocurrents. Finally, using physiological recordings and a behavioral readout, we demonstrate that GtACR1 enables the fast and reversible silencing of genetically targeted neurons within circuits engaged in visual processing.