Segregation of object and background motion in the retina

What the salamander eye has been telling the vision scientist's brain

Salamanders have been habitual residents of research laboratories for more than a century, and their history in science is tightly interwoven with vision research. Nevertheless, many vision scientists - even those working with salamanders - may be unaware of how much our knowledge about vision, and particularly the retina, has been shaped by studying salamanders. In this review, we take a tour through the salamander history in vision science, highlighting the main contributions of salamanders to our understanding of the vertebrate retina. We further point out specificities of the salamander visual system and discuss the perspectives of this animal system for future vision research.

Global Motion Processing by Populations of Direction-Selective Retinal Ganglion Cells

Simple stimuli have been critical to understanding neural population codes in sensory systems. Yet it remains necessary to determine the extent to which this understanding generalizes to more complex conditions. To examine this problem, we measured how populations of direction-selective ganglion cells (DSGCs) from the retinas of male and female mice respond to a global motion stimulus with its direction and speed changing dynamically. We then examined the encoding and decoding of motion direction in both individual and populations of DSGCs. Individual cells integrated global motion over ∼200 ms, and responses were tuned to direction. However, responses were sparse and broadly tuned, which severely limited decoding performance from small DSGC populations. In contrast, larger populations compensated for response sparsity, enabling decoding with high temporal precision (<100 ms). At these timescales, correlated spiking was minimal and had little impact on decoding performance, unlike results obtained using simpler local motion stimuli decoded over longer timescales. We use these data to define different DSGC population decoding regimes that use or mitigate correlated spiking to achieve high-spatial versus high-temporal resolution.SIGNIFICANCE STATEMENT ON-OFF direction-selective ganglion cells (ooDSGCs) in the mammalian retina are typically thought to signal local motion to the brain. However, several recent studies suggest they may signal global motion. Here we analyze the fidelity of encoding and decoding global motion in a natural scene across large populations of ooDSGCs. We show that large populations of DSGCs are capable of signaling rapid changes in global motion.

An Annotated Journey through Modern Visual Neuroscience

Recent advances in microscopy, genetics, physiology, and data processing have expanded the scope and accelerated the pace of discovery in visual neuroscience. However, the pace of discovery and the ever increasing number of published articles can present a serious issue for both trainees and senior scientists alike: with each passing year the fog of progress thickens, making it easy to lose sight of important earlier advances. As part of this special issue of the Journal of Neuroscience commemorating the 50th anniversary of SfN, here, we provide a variation on Stephen Kuffler's Oldies but Goodies classic reading list, with the hope that by looking back at highlights in the field of visual neuroscience we can better define remaining gaps in our knowledge and thus guide future work. We also hope that this article can serve as a resource that will aid those new to the field to find their bearings.

Neural circuits in the mouse retina support color vision in the upper visual field

Color vision is essential for an animal’s survival. It starts in the retina, where signals from different photoreceptor types are locally compared by neural circuits. Mice, like most mammals, are dichromatic with two cone types. They can discriminate colors only in their upper visual field. In the corresponding ventral retina, however, most cones display the same spectral preference, thereby presumably impairing spectral comparisons. In this study, we systematically investigated the retinal circuits underlying mouse color vision by recording light responses from cones, bipolar and ganglion cells. Surprisingly, most color-opponent cells are located in the ventral retina, with rod photoreceptors likely being involved. Here, the complexity of chromatic processing increases from cones towards the retinal output, where non-linear center-surround interactions create specific color-opponent output channels to the brain. This suggests that neural circuits in the mouse retina are tuned to extract color from the upper visual field, aiding robust detection of predators and ensuring the animal’s survival.

Mice are able to discriminate colors, at least in the upper visual field. Here, the authors provide a comprehensive characterization of retinal circuits underlying this behavior.

Insect Neurobiology: How a Small Spot Stops a Fly

Animals often respond to small moving features very differently than they do to large moving fields. A new study finds that viewing small spots causes walking fruit flies to stop in their tracks, and identifies the cellular pathway that processes this signal.

Non-canonical Receptive Field Properties and Neuromodulation of Feature-Detecting Neurons in Flies

Several fundamental aspects of motion vision circuitry are prevalent across flies and mice. Both taxa segregate ON and OFF signals. For any given spatial pattern, motion detectors in both taxa are tuned to speed, selective for one of four cardinal directions, and modulated by catecholamine neurotransmitters. These similarities represent conserved, canonical properties of the functional circuits and computational algorithms for motion vision. Less is known about feature detectors, including how receptive field properties differ from the motion pathway or whether they are under neuromodulatory control to impart functional plasticity for the detection of salient objects from a moving background. Here, we investigated 19 types of putative feature selective lobula columnar (LC) neurons in the optic lobe of the fruit fly Drosophila melanogaster to characterize divergent properties of feature selection. We identified LC12 and LC15 as feature detectors. LC15 encodes moving bars, whereas LC12 is selective for the motion of discrete objects, mostly independent of size. Neither is selective for contrast polarity, speed, or direction, highlighting key differences in the underlying algorithms for feature detection and motion vision. We show that the onset of background motion suppresses object responses by LC12 and LC15. Surprisingly, the application of octopamine, which is released during flight, reverses the suppressive influence of background motion, rendering both LCs able to track moving objects superimposed against background motion. Our results provide a comparative framework for the function and modulation of feature detectors and new insights into the underlying neuronal mechanisms involved in visual feature detection.

Object-Displacement-Sensitive Visual Neurons Drive Freezing in Drosophila

Visual systems are often equipped with neurons that detect small moving objects, which may represent prey, predators, or conspecifics. Although the processing properties of those neurons have been studied in diverse organisms, links between the proposed algorithms and animal behaviors or circuit mechanisms remain elusive. Here, we have investigated behavioral function, computational algorithm, and neurochemical mechanisms of an object-selective neuron, LC11, in Drosophila. With genetic silencing and optogenetic activation, we show that LC11 is necessary for a visual object-induced stopping behavior in walking flies, a form of short-term freezing, and its activity can promote stopping. We propose a new quantitative model for small object selectivity based on the physiology and anatomy of LC11 and its inputs. The model accurately reproduces LC11 responses by pooling fast-adapting, tightly size-tuned inputs. Direct visualization of neurotransmitter inputs to LC11 confirmed the model conjectures about upstream processing. Our results demonstrate how adaptation can enhance selectivity for behaviorally relevant, dynamic visual features.

Increased Connexin36 Phosphorylation in AII Amacrine Cell Coupling of the Mouse Myopic Retina

Myopia is a substantial public health problem worldwide. In the myopic retina, distant images are focused in front of the photoreceptors. The cells and mechanisms for retinal signaling that account either for emmetropization (i.e., normal refraction) or for refractive errors have remained elusive. Gap junctions play a key component in enhancement of signal transmission in visual pathways. AII amacrine cells (ACs), coupled by connexin36, segregate signals into ON and OFF pathways. Coupling between AII ACs is actively modulated through phosphorylation at serine 293 via dopamine in the mouse retina. In this study, form deprivation mouse myopia models were used to evaluate the expression patterns of connexin36-positive plaques (structural assay) and the state of connexin36 phosphorylation (functional assay) in AII ACs, which was green fluorescent protein-expressing in the Fam81a mouse line. Single-cell RNA sequencing showed dopaminergic synapse and gap junction pathways of AII ACs were downregulated in the myopic retina, although Gjd2 mRNA expression remained the same. Compared with the normal refractive eye, phosphorylation of connexin36 was increased in the myopic retina, but expression of connexin36 remained unchanged. This increased phosphorylation of Cx36 could indicate increased functional gap junction coupling of AII ACs in the myopic retina, a possible adaptation to adjust to the altered noisy signaling status.

Clustering of Neural Activity: A Design Principle for Population Codes

We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a "learnable" neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.

Self-motion trajectories can facilitate orientation-based figure-ground segregation

Segregation of objects from the background is a basic and essential property of the visual system. We studied the neural detection of objects defined by orientation difference from background in barn owls (Tyto alba). We presented wide-field displays of densely packed stripes with a dominant orientation. Visual objects were created by orienting a circular patch differently from the background. In head-fixed conditions, neurons in both tecto- and thalamofugal visual pathways (optic tectum and visual Wulst) were weakly responsive to these objects in their receptive fields. However, notably, in freely viewing conditions, barn owls occasionally perform peculiar side-to-side head motions (peering) when scanning the environment. In the second part of the study we thus recorded the neural response from head-fixed owls while the visual displays replicated the peering conditions; i.e., the displays (objects and backgrounds) were shifted along trajectories that induced a retinal motion identical to sampled peering motions during viewing of a static object. These conditions induced dramatic neural responses to the objects, in the very same neurons that where unresponsive to the objects in static displays. By reverting to circular motions of the display, we show that the pattern of the neural response is mostly shaped by the orientation of the background relative to motion and not the orientation of the object. Thus our findings provide evidence that peering and/or other self-motions can facilitate orientation-based figure-ground segregation through interaction with inhibition from the surround.NEW & NOTEWORTHY Animals frequently move their sensory organs and thereby create motion cues that can enhance object segregation from background. We address a special example of such active sensing, in barn owls. When scanning the environment, barn owls occasionally perform small-amplitude side-to-side head movements called peering. We show that the visual outcome of such peering movements elicit neural detection of objects that are rotated from the dominant orientation of the background scene and which are otherwise mostly undetected. These results suggest a novel role for self-motions in sensing objects that break the regular orientation of elements in the scene.

Selectivity to approaching motion in retinal inputs to the dorsal visual pathway

To efficiently navigate through the environment and avoid potential threats, an animal must quickly detect the motion of approaching objects. Current models of primate vision place the origins of this complex computation in the visual cortex. Here, we report that detection of approaching motion begins in the retina. Several ganglion cell types, the retinal output neurons, show selectivity to approaching motion. Synaptic current recordings from these cells further reveal that this preference for approaching motion arises in the interplay between presynaptic excitatory and inhibitory circuit elements. These findings demonstrate how excitatory and inhibitory circuits interact to mediate an ethologically relevant neural function. Moreover, the elementary computations that detect approaching motion begin early in the visual stream of primates.

Cell types and cell circuits in human and non-human primate retina

This review summarizes our current knowledge of primate including human retina focusing on bipolar, amacrine and ganglion cells and their connectivity. We have two main motivations in writing. Firstly, recent progress in non-invasive imaging methods to study retinal diseases mean that better understanding of the primate retina is becoming an important goal both for basic and for clinical sciences. Secondly, genetically modified mice are increasingly used as animal models for human retinal diseases. Thus, it is important to understand to which extent the retinas of primates and rodents are comparable. We first compare cell populations in primate and rodent retinas, with emphasis on how the fovea (despite its small size) dominates the neural landscape of primate retina. We next summarise what is known, and what is not known, about the postreceptoral neurone populations in primate retina. The inventories of bipolar and ganglion cells in primates are now nearing completion, comprising ~12 types of bipolar cell and at least 17 types of ganglion cell. Primate ganglion cells show clear differences in dendritic field size across the retina, and their morphology differs clearly from that of mouse retinal ganglion cells. Compared to bipolar and ganglion cells, amacrine cells show even higher morphological diversity: they could comprise over 40 types. Many amacrine types appear conserved between primates and mice, but functions of only a few types are understood in any primate or non-primate retina. Amacrine cells appear as the final frontier for retinal research in monkeys and mice alike.

Response Latency Tuning by Retinal Circuits Modulates Signal Efficiency

In the visual system, retinal ganglion cells (RGCs) of various subtypes encode preprocessed photoreceptor signals into a spike output which is then transmitted towards the brain through parallel feature pathways. Spike timing determines how each feature signal contributes to the output of downstream neurons in visual brain centers, thereby influencing efficiency in visual perception. In this study, we demonstrate a marked population-wide variability in RGC response latency that is independent of trial-to-trial variability and recording approach. RGC response latencies to simple visual stimuli vary considerably in a heterogenous cell population but remain reliable when RGCs of a single subtype are compared. This subtype specificity, however, vanishes when the retinal circuitry is bypassed via direct RGC electrical stimulation. This suggests that latency is primarily determined by the signaling speed through retinal pathways that provide subtype specific inputs to RGCs. In addition, response latency is significantly altered when GABA inhibition or gap junction signaling is disturbed, which further supports the key role of retinal microcircuits in latency tuning. Finally, modulation of stimulus parameters affects individual RGC response delays considerably. Based on these findings, we hypothesize that retinal microcircuits fine-tune RGC response latency, which in turn determines the context-dependent weighing of each signal and its contribution to visual perception.

Global Jitter Motion of the Retinal Image Dynamically Alters the Receptive Field Properties of Retinal Ganglion Cells

Fixational eye movements induce aperiodic motion of the retinal image. However, it is not yet fully understood how fixational eye movements affect retinal information processing. Here we show that global jitter motion, simulating the image motion during fixation, alters the spatiotemporal receptive field properties of retinal ganglion cells. Using multi-electrode and whole-cell recording techniques, we investigated light-evoked responses from ganglion cells in the isolated goldfish retina. Ganglion cells were classified into six groups based on the filtering property of light stimulus, the membrane properties, and the cell morphology. The spatiotemporal receptive field profiles of retinal ganglion cells were estimated by the reverse correlation method, where the dense noise stimulus was applied on the dark or random-dot background. We found that the jitter motion of the random-dot background elongated the receptive filed along the rostral-caudal axis and temporally sensitized in a specific group of ganglion cells: Fast-transient ganglion cells. At the newly emerged regions of the receptive field local light stimulation evoked excitatory postsynaptic currents with large amplitude and fast kinetics without changing the properties of inhibitory postsynaptic currents. Pharmacological experiments suggested two presynaptic mechanisms underlying the receptive field alteration: (i) electrical coupling between bipolar cells, which expands the receptive field in all directions; (ii) GABAergic presynaptic inhibition from amacrine cells, which reduces the dorsal and ventral regions of the expanded receptive field, resulting in elongation along the rostral-caudal axis. Our study demonstrates that the receptive field of Fast-transient ganglion cells is not static but dynamically altered depending on the visual inputs. The receptive field elongation during fixational eye movements may contribute to prompt firing to a target in the succeeding saccade.

Neural sensitization improves encoding fidelity in the primate retina

An animal’s motion through the environment can induce large and frequent fluctuations in light intensity on the retina. These fluctuations pose a major challenge to neural circuits tasked with encoding visual information, as they can cause cells to adapt and lose sensitivity. Here, we report that sensitization, a short-term plasticity mechanism, solves this difficult computational problem by maintaining neuronal sensitivity in the face of these fluctuations. The numerically dominant output pathway in the macaque monkey retina, the midget (parvocellular-projecting) pathway, undergoes sensitization under specific conditions, including simulated eye movements. Sensitization is present in the excitatory synaptic inputs from midget bipolar cells and is mediated by presynaptic disinhibition from a wide-field mechanism extending >0.5 mm along the retinal surface. Direct physiological recordings and a computational model indicate that sensitization in the midget pathway supports accurate sensory encoding and prevents a loss of responsiveness during dynamic visual processing.

Light intensity on the retina can fluctuate rapidly during natural vision, posing a challenge for encoding visual information. Here, the authors report that mechanisms of sensitization/facilitation maintain the sensitivity of the numerically dominant neural pathway in the primate retina during dynamic vision.

Unusual Physiological Properties of Smooth Monostratified Ganglion Cell Types in Primate Retina

The functions of the diverse retinal ganglion cell types in primates and the parallel visual pathways they initiate remain poorly understood. Here, unusual physiological and computational properties of the ON and OFF smooth monostratified ganglion cells are explored. Large-scale multi-electrode recordings from 48 macaque retinas revealed that these cells exhibit irregular receptive field structure composed of spatially segregated hotspots, quite different from the classic center-surround model of retinal receptive fields. Surprisingly, visual stimulation of different hotspots in the same cell produced spikes with subtly different spatiotemporal voltage signatures, consistent with a dendritic contribution to hotspot structure. Targeted visual stimulation and computational inference demonstrated strong nonlinear subunit properties associated with each hotspot, supporting a model in which the hotspots apply nonlinearities at a larger spatial scale than bipolar cells. These findings reveal a previously unreported nonlinear mechanism in the output of the primate retina that contributes to signaling spatial information.

Spatial suppression promotes rapid figure-ground segmentation of moving objects

Segregation of objects from their backgrounds is a fundamental visual function and one that is particularly effective when objects are in motion. Theoretically, suppressive center-surround mechanisms are well suited for accomplishing motion segregation. This longstanding hypothesis, however, has received limited empirical support. We report converging correlational and causal evidence that spatial suppression of background motion signals is critical for rapid segmentation of moving objects. Motion segregation ability is strongly predicted by both individual and stimulus-driven variations in spatial suppression strength. Moreover, aging-related superiority in perceiving background motion is associated with profound impairments in motion segregation. This segregation deficit is alleviated via perceptual learning, but only when motion segregation training also causes decreased sensitivity to background motion. We argue that perceptual insensitivity to large moving stimuli effectively implements background subtraction, which, in turn, enhances the visibility of moving objects and accounts for the observed link between spatial suppression and motion segregation.

The visual system excels at segregating moving objects from their backgrounds, a key visual function hypothesized to be driven by suppressive centre-surround mechanisms. Here, the authors show that spatial suppression of background motion signals is critical for rapid segmentation of moving objects.

Neural mechanisms of contextual modulation in the retinal direction selective circuit

Contextual modulation of neuronal responses by surrounding environments is a fundamental attribute of sensory processing. In the mammalian retina, responses of On–Off direction selective ganglion cells (DSGCs) are modulated by motion contexts. However, the underlying mechanisms are unknown. Here, we show that posterior-preferring DSGCs (pDSGCs) are sensitive to discontinuities of moving contours owing to contextually modulated cholinergic excitation from starburst amacrine cells (SACs). Using a combination of synapse-specific genetic manipulations, patch clamp electrophysiology and connectomic analysis, we identified distinct circuit motifs upstream of On and Off SACs that are required for the contextual modulation of pDSGC activity for bright and dark contrasts. Furthermore, our results reveal a class of wide-field amacrine cells (WACs) with straight, unbranching dendrites that function as “continuity detectors” of moving contours. Therefore, divergent circuit motifs in the On and Off pathways extend the information encoding of On-Off DSGCs beyond their direction selectivity during complex stimuli.

The mechanisms of contextual modulation in direction selective ganglion cells in the retina remain unclear. Here, the authors find that that On-Off direction-selective ganglion cells are differentially sensitive to discontinuities of dark and bright moving edges in the visual environment and, using synapse-specific genetic manipulations with functional measurements, reveal the microcircuits underlying this contextual sensitivity.

A Space-Variant Visual Pathway Model for Data Efficient Deep Learning

We present an investigation into adopting a model of the retino-cortical mapping, found in biological visual systems, to improve the efficiency of image analysis using Deep Convolutional Neural Nets (DCNNs) in the context of robot vision and egocentric perception systems. This work has now enabled DCNNs to process input images approaching one million pixels in size, in real time, using only consumer grade graphics processor (GPU) hardware in a single pass of the DCNN.

Simulated Saccadic Stimuli Suppress ON-Type Direction-Selective Retinal Ganglion Cells via Glycinergic Inhibition

Two types of mammalian direction-selective ganglion cells (DSGCs), ON and ONOFF, operate over different speed ranges. The directional axes of the ON-DSGCs are thought to align with the axes of the vestibular system and provide sensitivity at rotational velocities that are too slow to activate the semicircular canals. ONOFF-DSGCs respond to faster image velocities. Using natural images that simulate the natural visual inputs to freely moving animals, we show that simulated visual saccades suppress responses in ON-DSGCs but not ONOFF-DSGCs recorded in retinas of domestic rabbits of either gender. Analysis of the synaptic inputs shows that this saccadic suppression results from glycinergic inputs that are specific to ON-DSGCs and are absent in ONOFF-DSGCs. When this glycinergic input is blocked, both cell types respond similarly to visual saccades and display essentially identical speed tuning. The results demonstrate that glycinergic circuits within the retina can produce saccadic suppression of retinal ganglion cell activity. The cell-type-specific targeting of the glycinergic circuits further supports the proposed physiological roles of ON-DSGCs in retinal-image stabilization and of ONOFF-DSGCs in detecting local object motion and signaling optical flow.SIGNIFICANCE STATEMENT In the mammalian retina, ON direction-selective ganglion cells (DSGCs) respond preferentially to slow image motion, whereas ONOFF-DSGCs respond better to rapid motion. The mechanisms producing this different speed tuning remain unclear. Here we show that simulated visual saccades suppress ON-DSGCs, but not ONOFF-DSGCs. This selective saccadic suppression is because of the selective targeting of glycinergic inhibitory synaptic inputs to ON-DSGCs. The different saccadic suppression in the two cell types points to different physiological roles, consistent with their projections to distinct areas within the brain. ON-DSGCs may be critical for providing the visual feedback signals that contribute to stabilizing the image on the retina, whereas ONOFF-DSGCs may be important for detecting the onset of saccades or for signaling optical flow.

Probing Computation in the Primate Visual System at Single-Cone Resolution

Daylight vision begins when light activates cone photoreceptors in the retina, creating spatial patterns of neural activity. These cone signals are then combined and processed in downstream neural circuits, ultimately producing visual perception. Recent technical advances have made it possible to deliver visual stimuli to the retina that probe this processing by the visual system at its elementary resolution of individual cones. Physiological recordings from nonhuman primate retinas reveal the spatial organization of cone signals in retinal ganglion cells, including how signals from cones of different types are combined to support both spatial and color vision. Psychophysical experiments with human subjects characterize the visual sensations evoked by stimulating a single cone, including the perception of color. Future combined physiological and psychophysical experiments focusing on probing the elementary visual inputs are likely to clarify how neural processing generates our perception of the visual world.

Activity Correlations between Direction-Selective Retinal Ganglion Cells Synergistically Enhance Motion Decoding from Complex Visual Scenes

Neurons in sensory systems are often tuned to particular stimulus features. During complex naturalistic stimulation, however, multiple features may simultaneously affect neuronal responses, which complicates the readout of individual features. To investigate feature representation under complex stimulation, we studied how direction-selective ganglion cells in salamander retina respond to texture motion where direction, velocity, and spatial pattern inside the receptive field continuously change. We found that the cells preserve their direction preference under this stimulation, yet their direction encoding becomes ambiguous due to simultaneous activation by luminance changes. The ambiguities can be resolved by considering populations of direction-selective cells with different preferred directions. This gives rise to synergistic motion decoding, yielding more information from the population than the summed information from single-cell responses. Strong positive response correlations between cells with different preferred directions amplify this synergy. Our results show how correlated population activity can enhance feature extraction in complex visual scenes.

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Direction-selective ganglion cells respond to motion as well as luminance changes

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This obscures the readout of direction from single cells under complex texture motion

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Population decoding improves direction readout supralinearly over individual cells

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Strong spike correlations further enhance readout through increased synergy

Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, it remains unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex, naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently. Interestingly, these properties were observed throughout different populations of RGCs. Thus, our results reveal that the response at the level of RGCs is modulated by the naturalness of the stimulus - in particular for motion - which suggests that the tuning to the statistics of natural images already emerges at the level of the retina.

Contrast sensitivity reveals an oculomotor strategy for temporally encoding space

The contrast sensitivity function (CSF), how sensitivity varies with the frequency of the stimulus, is a fundamental assessment of visual performance. The CSF is generally assumed to be determined by low-level sensory processes. However, the spatial sensitivities of neurons in the early visual pathways, as measured in experiments with immobilized eyes, diverge from psychophysical CSF measurements in primates. Under natural viewing conditions, as in typical psychophysical measurements, humans continually move their eyes even when looking at a fixed point. Here, we show that the resulting transformation of the spatial scene into temporal modulations on the retina constitutes a processing stage that reconciles human CSF and the response characteristics of retinal ganglion cells under a broad range of conditions. Our findings suggest a fundamental integration between perception and action: eye movements work synergistically with the spatio-temporal sensitivities of retinal neurons to encode spatial information.

Functional Diversity in the Retina Improves the Population Code

Within a given brain region, individual neurons exhibit a wide variety of different feature selectivities. Here, we investigated the impact of this extensive functional diversity on the population neural code. Our approach was to build optimal decoders to discriminate among stimuli using the spiking output of a real, measured neural population and compare its performance against a matched, homogeneous neural population with the same number of cells and spikes. Analyzing large populations of retinal ganglion cells, we found that the real, heterogeneous population can yield a discrimination error lower than the homogeneous population by several orders of magnitude and consequently can encode much more visual information. This effect increases with population size and with graded degrees of heterogeneity. We complemented these results with an analysis of coding based on the Chernoff distance, as well as derivations of inequalities on coding in certain limits, from which we can conclude that the beneficial effect of heterogeneity occurs over a broad set of conditions. Together, our results indicate that the presence of functional diversity in neural populations can enhance their coding fidelity appreciably. A noteworthy outcome of our study is that this effect can be extremely strong and should be taken into account when investigating design principles for neural circuits.

The dynamic receptive fields of retinal ganglion cells

Retinal ganglion cells (RGCs) were one of the first classes of sensory neurons to be described in terms of a receptive field (RF). Over the last six decades, our understanding of the diversity of RGC types and the nuances of their response properties has grown exponentially. We will review the current understanding of RGC RFs mostly from studies in mammals, but including work from other vertebrates as well. We will argue for a new paradigm that embraces the fluidity of RGC RFs with an eye toward the neuroethology of vision. Specifically, we will focus on (1) different methods for measuring RGC RFs, (2) RF models, (3) feature selectivity and the distinction between fluid and stable RF properties, and (4) ideas about the future of understanding RGC RFs.

A single retinal circuit model for multiple computations

Vision is dependent on extracting intricate features of the visual information from the outside world, and complex visual computations begin to take place as soon as at the retinal level. In multiple studies on salamander retinas, the responses of a subtype of retinal ganglion cells, i.e., fast/biphasic-OFF ganglion cells, have been shown to be able to realize multiple functions, such as the segregation of a moving object from its background, motion anticipation, and rapid encoding of the spatial features of a new visual scene. For each of these visual functions, modeling approaches using extended linear-nonlinear cascade models suggest specific preceding retinal circuitries merging onto fast/biphasic-OFF ganglion cells. However, whether multiple visual functions can be accommodated together in a certain retinal circuitry and how specific mechanisms for each visual function interact with each other have not been investigated. Here, we propose a physiologically consistent, detailed computational model of the retinal circuit based on the spatiotemporal dynamics and connections of each class of retinal neurons to implement object motion sensitivity, motion anticipation, and rapid coding in the same circuit. Simulations suggest that multiple computations can be accommodated together, thereby implying that the fast/biphasic-OFF ganglion cell has potential to output a train of spikes carrying multiple pieces of information on distinct features of the visual stimuli.

Neural Mechanisms of Motion Processing in the Mammalian Retina

Visual motion on the retina activates a cohort of retinal ganglion cells (RGCs). This population activity encodes multiple streams of information extracted by parallel retinal circuits. Motion processing in the retina is best studied in the direction-selective circuit. The main focus of this review is the neural basis of direction selectivity, which has been investigated in unprecedented detail using state-of-the-art functional, connectomic, and modeling methods. Mechanisms underlying the encoding of other motion features by broader RGC populations are also discussed. Recent discoveries at both single-cell and population levels highlight the dynamic and stimulus-dependent engagement of multiple mechanisms that collectively implement robust motion detection under diverse visual conditions.

Inferring hidden structure in multilayered neural circuits

A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.

Computation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuit’s output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapped onto individual circuit components inside the retina, namely bipolar cells and the bipolar-to-ganglion cell synaptic threshold. This work demonstrates how combining simple prior knowledge of circuit properties with partial experimental recordings of a neural circuit’s output can yield interpretable models of the entire circuit computation, including parts of the circuit that are hidden or not directly observed in neural recordings.

Visual Cortex Limits Pop-Out in the Superior Colliculus of Awake Mice

We detect objects more readily if they differ from their surroundings in motion, color, or texture. This increased saliency is thought to be related to increased responses in the visual cortex. The superior colliculus is another brain area involved in vision and especially in directing gaze and attention. In this study, we show that differences in texture orientation also increase responses in the superficial layers of the superior colliculus that receive retinal and cortical input. We found that gratings evoke more neural response when surrounded by orthogonal gratings than when surrounded by parallel gratings, particularly in the awake mouse. This pop-out is not originating from the visual cortex, and silencing visual cortex increased the relative difference in response. A model shows that this can result from retinotopically matched excitation from visual cortex to the superior colliculus. We suggest that the perceptual saliency of a stimulus differing from its surround in a low-level feature like grating orientation could depend on visual processing in the superior colliculus.

Behavioral Evidence and Neural Correlates of Perceptual Grouping by Motion in the Barn Owl

Perceiving an object as salient from its surround often requires a preceding process of grouping the object and background elements as perceptual wholes. In humans, motion homogeneity provides a strong cue for grouping, yet it is unknown to what extent this occurs in nonprimate species. To explore this question, we studied the effects of visual motion homogeneity in barn owls of both genders, at the behavioral as well as the neural level. Our data show that the coherency of the background motion modulates the perceived saliency of the target object. An object moving in an odd direction relative to other objects attracted more attention when the other objects moved homogeneously compared with when moved in a variety of directions. A possible neural correlate of this effect may arise in the population activity of the intermediate/deep layers of the optic tectum. In these layers, the neural responses to a moving element in the receptive field were suppressed when additional elements moved in the surround. However, when the surrounding elements all moved in one direction (homogeneously moving), they induced less suppression of the response compared with nonhomogeneously moving elements. Moreover, neural responses were more sensitive to the homogeneity of the background motion than to motion-direction contrasts between the receptive field and the surround. The findings suggest similar principles of saliency-by-motion in an avian species as in humans and show a locus in the optic tectum where the underlying neural circuitry may exist.SIGNIFICANCE STATEMENT A critical task of the visual system is to arrange incoming visual information to a meaningful scene of objects and background. In humans, elements that move homogeneously are grouped perceptually to form a categorical whole object. We discovered a similar principle in the barn owl's visual system, whereby the homogeneity of the motion of elements in the scene allows perceptually distinguishing an object from its surround. The novel findings of these visual effects in an avian species, which lacks neocortical structure, suggest that our basic visual perception shares more universal principles across species than presently thought, and shed light on possible brain mechanisms for perceptual grouping.

Bidirectional encoding of motion contrast in the mouse superior colliculus

Detection of salient objects in the visual scene is a vital aspect of an animal's interactions with its environment. Here, we show that neurons in the mouse superior colliculus (SC) encode visual saliency by detecting motion contrast between stimulus center and surround. Excitatory neurons in the most superficial lamina of the SC are contextually modulated, monotonically increasing their response from suppression by the same-direction surround to maximal potentiation by an oppositely-moving surround. The degree of this potentiation declines with depth in the SC. Inhibitory neurons are suppressed by any surround at all depths. These response modulations in both neuronal populations are much more prominent to direction contrast than to phase, temporal frequency, or static orientation contrast, suggesting feature-specific saliency encoding in the mouse SC. Together, our findings provide evidence supporting locally generated feature representations in the SC, and lay the foundations towards a mechanistic and evolutionary understanding of their emergence.

Visual brain plasticity induced by central and peripheral visual field loss

Disorders that specifically affect central and peripheral vision constitute invaluable models to study how the human brain adapts to visual deafferentation. We explored cortical changes after the loss of central or peripheral vision. Cortical thickness (CoTks) and resting-state cortical entropy (rs-CoEn), as a surrogate for neural and synaptic complexity, were extracted in 12 Stargardt macular dystrophy, 12 retinitis pigmentosa (tunnel vision stage), and 14 normally sighted subjects. When compared to controls, both groups with visual loss exhibited decreased CoTks in dorsal area V3d. Peripheral visual field loss also showed a specific CoTks decrease in early visual cortex and ventral area V4, while central visual field loss in dorsal area V3A. Only central visual field loss exhibited increased CoEn in LO-2 area and FG1. Current results revealed biomarkers of brain plasticity within the dorsal and the ventral visual streams following central and peripheral visual field defects.

Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within RGCs can be derived by quantitatively fitting the sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear-nonlinear approaches.

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.

Neuroscience: Peeking Under the Sombrero

A recent study has introduced a new analytical approach to understanding neural circuits which has revealed previously hidden neural interactions in a large population of cells in the primate retina. The neural circuit described likely contributes to encoding visual motion.

Inhibitory Control of Feature Selectivity in an Object Motion Sensitive Circuit of the Retina

Object motion sensitive (OMS) W3-retinal ganglion cells (W3-RGCs) in mice respond to local movements in a visual scene but remain silent during self-generated global image motion. The excitatory inputs that drive responses of W3-RGCs to local motion were recently characterized, but which inhibitory neurons suppress W3-RGCs' responses to global motion, how these neurons encode motion information, and how their connections are organized along the excitatory circuit axis remains unknown. Here, we find that a genetically identified amacrine cell (AC) type, TH2-AC, exhibits fast responses to global motion and slow responses to local motion. Optogenetic stimulation shows that TH2-ACs provide strong GABA_A receptor-mediated input to W3-RGCs but only weak input to upstream excitatory neurons. Cell-type-specific silencing reveals that temporally coded inhibition from TH2-ACs cancels W3-RGC spike responses to global but not local motion stimuli and, thus, controls the feature selectivity of OMS signals sent to the brain.

Closed-Loop Estimation of Retinal Network Sensitivity by Local Empirical Linearization

Understanding how sensory systems process information depends crucially on identifying which features of the stimulus drive the response of sensory neurons, and which ones leave their response invariant. This task is made difficult by the many nonlinearities that shape sensory processing. Here, we present a novel perturbative approach to understand information processing by sensory neurons, where we linearize their collective response locally in stimulus space. We added small perturbations to reference stimuli and tested if they triggered visible changes in the responses, adapting their amplitude according to the previous responses with closed-loop experiments. We developed a local linear model that accurately predicts the sensitivity of the neural responses to these perturbations. Applying this approach to the rat retina, we estimated the optimal performance of a neural decoder and showed that the nonlinear sensitivity of the retina is consistent with an efficient encoding of stimulus information. Our approach can be used to characterize experimentally the sensitivity of neural systems to external stimuli locally, quantify experimentally the capacity of neural networks to encode sensory information, and relate their activity to behavior.

Local processing in neurites of VGluT3-expressing amacrine cells differentially organizes visual information

Neurons receive synaptic inputs on extensive neurite arbors. How information is organized across arbors and how local processing in neurites contributes to circuit function is mostly unknown. Here, we used two-photon Ca²⁺ imaging to study visual processing in VGluT3-expressing amacrine cells (VG3-ACs) in the mouse retina. Contrast preferences (ON vs. OFF) varied across VG3-AC arbors depending on the laminar position of neurites, with ON responses preferring larger stimuli than OFF responses. Although arbors of neighboring cells overlap extensively, imaging population activity revealed continuous topographic maps of visual space in the VG3-AC plexus. All VG3-AC neurites responded strongly to object motion, but remained silent during global image motion. Thus, VG3-AC arbors limit vertical and lateral integration of contrast and location information, respectively. We propose that this local processing enables the dense VG3-AC plexus to contribute precise object motion signals to diverse targets without distorting target-specific contrast preferences and spatial receptive fields.

Joint Encoding of Object Motion and Motion Direction in the Salamander Retina

The processing of motion in visual scenes is important for detecting and tracking moving objects as well as for monitoring self-motion through the induced optic flow. Specialized neural circuits have been identified in the vertebrate retina for detecting motion direction or for distinguishing between object motion and self-motion, although little is known about how information about these distinct features of visual motion is combined. The salamander retina, which is a widely used model system for analyzing retinal function, contains object-motion-sensitive (OMS) ganglion cells, which strongly respond to local motion signals but are suppressed by global image motion. Yet, direction-selective (DS) ganglion cells have been conspicuously absent from characterizations of the salamander retina, despite their ubiquity in other model systems. We here show that the retina of axolotl salamanders contains at least two distinct classes of DS ganglion cells. For one of these classes, the cells display a strong preference for local over global motion in addition to their direction selectivity (OMS-DS cells) and thereby combine sensitivity to two distinct motion features. The OMS-DS cells are further distinct from standard (non-OMS) DS cells by their smaller receptive fields and different organization of preferred motion directions. Our results suggest that the two classes of DS cells specialize to encode motion direction of local and global motion stimuli, respectively, even for complex composite motion scenes. Furthermore, although the salamander DS cells are OFF-type, there is a strong analogy to the systems of ON and ON-OFF DS cells in the mammalian retina.

SIGNIFICANCE STATEMENT

The retina contains specialized cells for motion processing. Among the retinal ganglion cells, which form the output neurons of the retina, some are known to report the direction of a moving stimulus (direction-selective cells), and others distinguish the motion of an object from a moving background. But little is known about how information about local object motion and information about motion direction interact. Here, we report that direction-selective ganglion cells can be identified in the salamander retina, where their existence had been unclear. Furthermore, there are two independent systems of direction-selective cells, and one of these combines direction selectivity with sensitivity to local motion. The output of these cells could assist in tracking moving objects and estimating their future position.

Active Dendritic Properties and Local Inhibitory Input Enable Selectivity for Object Motion in Mouse Superior Colliculus Neurons

Neurons respond to specific features of sensory stimuli. In the visual system, for example, some neurons respond to motion of small but not large objects, whereas other neurons prefer motion of the entire visual field. Separate neurons respond equally to local and global motion but selectively to additional features of visual stimuli. How and where does response selectivity emerge? Here, we show that wide-field (WF) cells in retino-recipient layers of the mouse superior colliculus (SC) respond selectively to small moving objects. Moreover, we identify two mechanisms that contribute to this selectivity. First, we show that input restricted to a small portion of the broad dendritic arbor of WF cells is sufficient to trigger dendritic spikes that reliably propagate to the soma/axon. In vivo whole-cell recordings reveal that nearly every action potential evoked by visual stimuli has characteristics of spikes initiated in dendrites. Second, inhibitory input from a different class of SC neuron, horizontal cells, constrains the range of stimuli to which WF cells respond. Horizontal cells respond preferentially to the sudden appearance or rapid movement of large stimuli. Optogenetic reduction of their activity reduces movement selectivity and broadens size tuning in WF cells by increasing the relative strength of responses to stimuli that appear suddenly or cover a large region of space. Therefore, strongly propagating dendritic spikes enable small stimuli to drive spike output in WF cells and local inhibition helps restrict responses to stimuli that are both small and moving.

SIGNIFICANCE STATEMENT

How do neurons respond selectively to some sensory stimuli but not others? In the visual system, a particularly relevant stimulus feature is object motion, which often reveals other animals. Here, we show how specific cells in the superior colliculus, one synapse downstream of the retina, respond selectively to object motion. These wide-field (WF) cells respond strongly to small objects that move slowly anywhere through a large region of space, but not to stationary objects or full-field motion. Action potential initiation in dendrites enables small stimuli to trigger visual responses and inhibitory input from cells that prefer large, suddenly appearing, or quickly moving stimuli restricts responses of WF cells to objects that are small and moving.

Three Small-Receptive-Field Ganglion Cells in the Mouse Retina Are Distinctly Tuned to Size, Speed, and Object Motion

Retinal ganglion cells (RGCs) are frequently divided into functional types by their ability to extract and relay specific features from a visual scene, such as the capacity to discern local or global motion, direction of motion, stimulus orientation, contrast or uniformity, or the presence of large or small objects. Here we introduce three previously uncharacterized, nondirection-selective ON-OFF RGC types that represent a distinct set of feature detectors in the mouse retina. The three high-definition (HD) RGCs possess small receptive-field centers and strong surround suppression. They respond selectively to objects of specific sizes, speeds, and types of motion. We present comprehensive morphological characterization of the HD RGCs and physiological recordings of their light responses, receptive-field size and structure, and synaptic mechanisms of surround suppression. We also explore the similarities and differences between the HD RGCs and a well characterized RGC with a comparably small receptive field, the local edge detector, in response to moving objects and textures. We model populations of each RGC type to study how they differ in their performance tracking a moving object. These results, besides introducing three new RGC types that together constitute a substantial fraction of mouse RGCs, provide insights into the role of different circuits in shaping RGC receptive fields and establish a foundation for continued study of the mechanisms of surround suppression and the neural basis of motion detection.

SIGNIFICANCE STATEMENT

The output cells of the retina, retinal ganglion cells (RGCs), are a diverse group of ∼40 distinct neuron types that are often assigned "feature detection" profiles based on the specific aspects of the visual scene to which they respond. Here we describe, for the first time, morphological and physiological characterization of three new RGC types in the mouse retina, substantially augmenting our understanding of feature selectivity. Experiments and modeling show that while these three "high-definition" RGCs share certain receptive-field properties, they also have distinct tuning to the size, speed, and type of motion on the retina, enabling them to occupy different niches in stimulus space.

Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization

Neurons in sensory systems often pool inputs over arrays of presynaptic cells, giving rise to functional subunits inside a neuron’s receptive field. The organization of these subunits provides a signature of the neuron’s presynaptic functional connectivity and determines how the neuron integrates sensory stimuli. Here we introduce the method of spike-triggered non-negative matrix factorization for detecting the layout of subunits within a neuron’s receptive field. The method only requires the neuron’s spiking responses under finely structured sensory stimulation and is therefore applicable to large populations of simultaneously recorded neurons. Applied to recordings from ganglion cells in the salamander retina, the method retrieves the receptive fields of presynaptic bipolar cells, as verified by simultaneous bipolar and ganglion cell recordings. The identified subunit layouts allow improved predictions of ganglion cell responses to natural stimuli and reveal shared bipolar cell input into distinct types of ganglion cells.

How a neuron integrates sensory information requires knowledge about its functional presynaptic connections. Here the authors report a new method using non-negative matrix factorization to identify the layout of presynaptic bipolar cell inputs onto retinal ganglion cells and predict their responses to natural stimuli.

Complex Visual Motion Representation in Mouse Area V1

Rodent visual cortex has a hierarchical architecture similar to that of higher mammals (Coogan and Burkhalter, 1993; Marshel et al., 2011; Wang et al., 2012). Although notable differences exist between the species in terms or receptive field sizes and orientation map organization (Dräger, 1975; Gattass et al., 1987; Van den Bergh et al., 2010), mouse V1 is thought to respond to local orientation and visual motion elements rather than to global patterns of motion, similar to V1 in higher mammals (Niell and Stryker, 2008; Bonin et al., 2011). However, recent results are inconclusive: some argue mouse V1 is analogous to monkey V1 (Juavinett and Callaway, 2015); others argue that it displays complex motion responses (Muir et al., 2015). We used type I plaids formed by the additive superposition of moving gratings (Adelson and Movshon, 1982; Movshon et al., 1985; Albright and Stoner, 1995) to investigate this question. We show that mouse V1 contains a considerably smaller fraction of component-motion-selective neurons (∼17% vs ∼84%), and a larger fraction of pattern-motion-selective neurons (∼10% vs <1.3%) compared with primate/cat V1. The direction of optokinetic nystagmus correlates with visual perception in higher mammals (Fox et al., 1975; Logothetis and Schall, 1990; Wei and Sun, 1998; Watanabe, 1999; Naber et al., 2011). Measurement of optokinetic responses to plaid stimuli revealed that mice demonstrate bistable perception, sometimes tracking individual stimulus components and others the global pattern of motion. Moreover, bistable optokinetic responses cannot be entirely attributed to subcortical circuitry as V1 lesions alter the fraction of responses occurring along pattern versus component motion. These observations suggest that area V1 input contributes to complex motion perception in the mouse.

SIGNIFICANCE STATEMENT

Area V1 in the mouse is hierarchically similar but not necessarily identical to area V1 in cats and primates. Here we demonstrate that area V1 neurons process complex motion plaid stimuli differently in mice versus in cats or primates. Specifically, a smaller proportion of mouse V1 cells are sensitive to component motion, and a larger proportion to pattern motion than are found in area V1 of cats/primates. Furthermore, we demonstrate for the first time that mice exhibit bistable visual perception of plaid stimuli, and that this depends, at least in part, on area V1 input. Finally, we suggest that the relative proportion of component-motion-selective responses to pattern-motion-selective responses in mouse V1 may bias visual perception, as evidenced by changes in the direction of elicited optokinetic responses.

Retinal Circuit Emulator With Spatiotemporal Spike Outputs at Millisecond Resolution in Response to Visual Events

To gain insights on how visual information of the real world is filtered, compressed, and encoded by the vertebrate retinas, emulating in silico the spatiotemporal patterns of the graded and action potentials of neuronal responses to natural visual scenes on biological time scale is a feasible approach. As a basic platform for such an emulation, we here developed a compact hardware system comprising an analog silicon retina and a field-programmable gate array module. With utilizing the Izhikevich formalism, a retinal circuit model that emulates spiking of ganglion cells was implemented in this system. The emulated spike timing had the resolution of about 2 ms relative to the stimulus onset and was little affected by timings of the synchronous frame sampling in the silicon retina. Thus, the emulator can mimic the event-driven spike outputs of biological retinas. The system was useful for simultaneously visualizing neural images of both the graded potentials and the spikes in response to real live visual scenes. Since our emulator system is reconfigurable, it provides a flexible platform for investigating visual functions of retinal circuits under natural visual environment.

Identification of Functionally Interconnected Neurons Using Factor Analysis

The advances in electrophysiological methods have allowed registering the joint activity of single neurons. Thus, studies on functional dynamics of complex-valued neural networks and its information processing mechanism have been conducted. Particularly, the methods for identifying neuronal interconnections are in increasing demand in the area of neurosciences. Here, we proposed a factor analysis to identify functional interconnections among neurons via spike trains. This method was evaluated using simulations of neural discharges from different interconnections schemes. The results have revealed that the proposed method not only allows detecting neural interconnections but will also allow detecting the presence of presynaptic neurons without the need of the recording of them.

Stimulus background influences phase invariant coding by correlated neural activity

Previously we reported that correlations between the activities of peripheral afferents mediate a phase invariant representation of natural communication stimuli that is refined across successive processing stages thereby leading to perception and behavior in the weakly electric fish Apteronotus leptorhynchus (Metzen et al., 2016). Here, we explore how phase invariant coding and perception of natural communication stimuli are affected by changes in the sinusoidal background over which they occur. We found that increasing background frequency led to phase locking, which decreased both detectability and phase invariant coding. Correlated afferent activity was a much better predictor of behavior as assessed from both invariance and detectability than single neuron activity. Thus, our results provide not only further evidence that correlated activity likely determines perception of natural communication signals, but also a novel explanation as to why these preferentially occur on top of low frequency as well as low-intensity sinusoidal backgrounds.

Sensitivity to image recurrence across eye-movement-like image transitions through local serial inhibition in the retina

Standard models of stimulus encoding in the retina postulate that image presentations activate neurons according to the increase of preferred contrast inside the receptive field. During natural vision, however, images do not arrive in isolation, but follow each other rapidly, separated by sudden gaze shifts. We here report that, contrary to standard models, specific ganglion cells in mouse retina are suppressed after a rapid image transition by changes in visual patterns across the transition, but respond with a distinct spike burst when the same pattern reappears. This sensitivity to image recurrence depends on opposing effects of glycinergic and GABAergic inhibition and can be explained by a circuit of local serial inhibition. Rapid image transitions thus trigger a mode of operation that differs from the processing of simpler stimuli and allows the retina to tag particular image parts or to detect transition types that lead to recurring stimulus patterns.

DOI:http://dx.doi.org/10.7554/eLife.22431.001

Circuit Mechanisms of a Retinal Ganglion Cell with Stimulus-Dependent Response Latency and Activation Beyond Its Dendrites

Center-surround antagonism has been used as the canonical model to describe receptive fields of retinal ganglion cells (RGCs) for decades. We describe a newly identified RGC type in the mouse, called the ON delayed (OND) RGC, with receptive field properties that deviate from center-surround organization. Responding with an unusually long latency to light stimulation, OND RGCs respond earlier as the visual stimulus increases in size. Furthermore, OND RGCs are excited by light falling far beyond their dendrites. We unravel details of the circuit mechanisms behind these phenomena, revealing new roles for inhibition in controlling both temporal and spatial receptive field properties. The non-canonical receptive field properties of the OND RGC-integration of long temporal and large spatial scales-suggest that unlike typical RGCs, it may encode a slowly varying, global property of the visual scene.

Neural Circuit Inference from Function to Structure

Advances in technology are opening new windows on the structural connectivity and functional dynamics of brain circuits. Quantitative frameworks are needed that integrate these data from anatomy and physiology. Here, we present a modeling approach that creates such a link. The goal is to infer the structure of a neural circuit from sparse neural recordings, using partial knowledge of its anatomy as a regularizing constraint. We recorded visual responses from the output neurons of the retina, the ganglion cells. We then generated a systematic sequence of circuit models that represents retinal neurons and connections and fitted them to the experimental data. The optimal models faithfully recapitulated the ganglion cell outputs. More importantly, they made predictions about dynamics and connectivity among unobserved neurons internal to the circuit, and these were subsequently confirmed by experiment. This circuit inference framework promises to facilitate the integration and understanding of big data in neuroscience.