Bipolar cells contribute to nonlinear spatial summation in the brisk-transient (Y) ganglion cell in mammalian retina

Nonlinear receptive fields evoke redundant retinal coding of natural scenes

The role of the vertebrate retina in early vision is generally described by the efficient coding hypothesis1,2, which predicts that the retina reduces the redundancy inherent in natural scenes3 by discarding spatiotemporal correlations while preserving stimulus information4. It is unclear, however, whether the predicted decorrelation and redundancy reduction in the activity of ganglion cells, the retina's output neurons, hold under gaze shifts, which dominate the dynamics of the natural visual input5. We show here that species-specific gaze patterns in natural stimuli can drive correlated spiking responses both in and across distinct types of ganglion cells in marmoset as well as mouse retina. These concerted responses disrupt redundancy reduction to signal fixation periods with locally high spatial contrast. Model-based analyses of ganglion cell responses to natural stimuli show that the observed response correlations follow from nonlinear pooling of ganglion cell inputs. Our results indicate cell-type-specific deviations from efficient coding in retinal processing of natural gaze shifts.

Mammalian Retinal Bipolar Cells: Morphological Identification and Systematic Classification in Rabbit Retina with a Comparative Perspective

Retinal bipolar cells (BCs) convey visual signals from photoreceptors to more than 50 types of rabbit retinal ganglion cells (Famiglietti, 2020). More than 40 years ago, 10-11 types of bipolar cell were recognized in rabbit and cat retinas (Famiglietti, 1981). Twenty years later 10 were identified in mouse, rat, and monkey, while recent molecular genetic studies indicate that there are 15 types of bipolar cell in mouse retina (Shekhar et al., 2016). The present detailed study of more than 800 bipolar cells in ten Golgi-impregnated rabbit retinas indicates that there are 14-16 types of cone bipolar cell and one type of rod bipolar cell in rabbit retina. These have been carefully analyzed in terms of dendritic and axonal morphology, and axon terminal stratification with respect to fiducial starburst amacrine cells. In fortuitous proximity, several types of bipolar cell can be related to identified ganglion cells by stratification and by contacts suggestive of synaptic connection. These results are compared with other studies of rabbit bipolar cells. Homologies with bipolar cells of mouse and monkey are considered in functional terms.

Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina

Spatially nonlinear stimulus integration by retinal ganglion cells lies at the heart of various computations performed by the retina. It arises from the nonlinear transmission of signals that ganglion cells receive from bipolar cells, which thereby constitute functional subunits within a ganglion cell's receptive field. Inferring these subunits from recorded ganglion cell activity promises a new avenue for studying the functional architecture of the retina. This calls for efficient methods, which leave sufficient experimental time to leverage the acquired knowledge for further investigating identified subunits. Here, we combine concepts from super-resolution microscopy and computed tomography and introduce super-resolved tomographic reconstruction (STR) as a technique to efficiently stimulate and locate receptive field subunits. Simulations demonstrate that this approach can reliably identify subunits across a wide range of model variations, and application in recordings of primate parasol ganglion cells validates the experimental feasibility. STR can potentially reveal comprehensive subunit layouts within only a few tens of minutes of recording time, making it ideal for online analysis and closed-loop investigations of receptive field substructure in retina recordings.

Primate retina trades single-photon detection for high-fidelity contrast encoding

How the spike output of the retina enables human visual perception is not fully understood. Here, we address this at the sensitivity limit of vision by correlating human visual perception with the spike outputs of primate ON and OFF parasol (magnocellular) retinal ganglion cells in tightly matching stimulus conditions. We show that human vision at its ultimate sensitivity limit depends on the spike output of the ON but not the OFF retinal pathway. Consequently, nonlinear signal processing in the retinal ON pathway precludes perceptual detection of single photons in darkness but enables quantal-resolution discrimination of differences in light intensity.

An actor-model framework for visual sensory encoding

A fundamental challenge in neuroengineering is determining a proper artificial input to a sensory system that yields the desired perception. In neuroprosthetics, this process is known as artificial sensory encoding, and it holds a crucial role in prosthetic devices restoring sensory perception in individuals with disabilities. For example, in visual prostheses, one key aspect of artificial image encoding is to downsample images captured by a camera to a size matching the number of inputs and resolution of the prosthesis. Here, we show that downsampling an image using the inherent computation of the retinal network yields better performance compared to learning-free downsampling methods. We have validated a learning-based approach (actor-model framework) that exploits the signal transformation from photoreceptors to retinal ganglion cells measured in explanted mouse retinas. The actor-model framework generates downsampled images eliciting a neuronal response in-silico and ex-vivo with higher neuronal reliability than the one produced by a learning-free approach. During the learning process, the actor network learns to optimize contrast and the kernel's weights. This methodological approach might guide future artificial image encoding strategies for visual prostheses. Ultimately, this framework could be applicable for encoding strategies in other sensory prostheses such as cochlear or limb.

Synaptic Origins of the Complex Receptive Field Structure in Primate Smooth Monostratified Retinal Ganglion Cells

Considerable progress has been made in studying the receptive fields of the most common primate retinal ganglion cell (RGC) types, such as parasol RGCs. Much less is known about the rarer primate RGC types and the circuitry that gives rise to noncanonical receptive field structures. The goal of this study was to analyze synaptic inputs to smooth monostratified RGCs to determine the origins of their complex spatial receptive fields, which contain isolated regions of high sensitivity called "hotspots." Interestingly, smooth monostratified RGCs co-stratify with the well-studied parasol RGCs and are thus constrained to receiving input from bipolar and amacrine cells with processes sharing the same layer, raising the question of how their functional differences originate. Through 3D reconstructions of circuitry and synapses onto ON smooth monostratified and ON parasol RGCs from central macaque retina, we identified four distinct sampling strategies employed by smooth and parasol RGCs to extract diverse response properties from co-stratifying bipolar and amacrine cells. The two RGC types differed in the proportion of amacrine cell input, relative contributions of co-stratifying bipolar cell types, amount of synaptic input per bipolar cell, and spatial distribution of bipolar cell synapses. Our results indicate that the smooth RGC's complex receptive field structure arises through spatial asymmetries in excitatory bipolar cell input which formed several discrete clusters comparable with physiologically measured hotspots. Taken together, our results demonstrate how the striking differences between ON parasol and ON smooth monostratified RGCs arise from distinct strategies for sampling a common set of synaptic inputs.

Diversity of Ganglion Cell Responses to Saccade-Like Image Shifts in the Primate Retina

Saccades are a fundamental part of natural vision. They interrupt fixations of the visual gaze and rapidly shift the image that falls onto the retina. These stimulus dynamics can cause activation or suppression of different retinal ganglion cells, but how they affect the encoding of visual information in different types of ganglion cells is largely unknown. Here, we recorded spiking responses to saccade-like shifts of luminance gratings from ganglion cells in isolated marmoset retinas and investigated how the activity depended on the combination of presaccadic and postsaccadic images. All identified cell types, On and Off parasol and midget cells, as well as a type of Large Off cells, displayed distinct response patterns, including particular sensitivity to either the presaccadic or the postsaccadic image or combinations thereof. In addition, Off parasol and Large Off cells, but not On cells, showed pronounced sensitivity to whether the image changed across the transition. Stimulus sensitivity of On cells could be explained based on their responses to step changes in light intensity, whereas Off cells, in particular, parasol and the Large Off cells, seem to be affected by additional interactions that are not triggered during simple light-intensity flashes. Together, our data show that ganglion cells in the primate retina are sensitive to different combinations of presaccadic and postsaccadic visual stimuli. This contributes to the functional diversity of the output signals of the retina and to asymmetries between On and Off pathways and provides evidence of signal processing beyond what is triggered by isolated steps in light intensity.SIGNIFICANCE STATEMENT Sudden eye movements (saccades) shift our direction of gaze, bringing new images in focus on our retinas. To study how retinal neurons deal with these rapid image transitions, we recorded spiking activity from ganglion cells, the output neurons of the retina, in isolated retinas of marmoset monkeys while shifting a projected image in a saccade-like fashion across the retina. We found that the cells do not just respond to the newly fixated image, but that different types of ganglion cells display different sensitivities to the presaccadic and postsaccadic stimulus patterns. Certain Off cells, for example, are sensitive to changes in the image across transitions, which contributes to differences between On and Off information channels and extends the range of encoded stimulus features.

Systematic reduction of the dimensionality of natural scenes allows accurate predictions of retinal ganglion cell spike outputs

The mammalian retina engages a broad array of linear and nonlinear circuit mechanisms to convert natural scenes into retinal ganglion cell (RGC) spike outputs. Although many individual integration mechanisms are well understood, we know less about how multiple mechanisms interact to encode the complex spatial features present in natural inputs. Here, we identified key spatial features in natural scenes that shape encoding by primate parasol RGCs. Our approach identified simplifications in the spatial structure of natural scenes that minimally altered RGC spike responses. We observed that reducing natural movies into 16 linearly integrated regions described ∼80% of the structure of parasol RGC spike responses; this performance depended on the number of regions but not their precise spatial locations. We used simplified stimuli to design high-dimensional metamers that recapitulated responses to naturalistic movies. Finally, we modeled the retinal computations that convert flashed natural images into one-dimensional spike counts.

Behavioral signatures of Y-like neuronal responses in human vision

Retinal ganglion cells initiating the magnocellular/Y-cell visual pathways respond nonlinearly to high spatial frequencies (SFs) and temporal frequencies (TFs). This nonlinearity is implicated in the processing of contrast modulation (CM) stimuli in cats and monkeys, but its contribution to human visual perception is not well understood. Here, we evaluate human psychophysical performance for CM stimuli, consisting of a high SF grating carrier whose contrast is modulated by a low SF sinewave envelope. Subjects reported the direction of motion of CM envelopes or luminance modulation (LM) gratings at different eccentricities. The performance on SF (for LMs) or carrier SF (for CMs) was measured for different TFs (LMs) or carrier TFs (CMs). The best performance for LMs was at lower TFs and SFs, decreasing systematically with eccentricity. However, performance with CMs was bandpass with carrier SF, largely independent of carrier TF, and at the highest carrier TF (20 Hz) decreased minimally with eccentricity. Since the nonlinear subunits of Y-cells respond better at higher TFs compared to the linear response components and respond best at higher SFs that are relatively independent of eccentricity, these results suggest that behavioral tasks employing CM stimuli might reveal nonlinear contributions of retinal Y-like cells to human perception.

Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina

Motion sensing is a critical aspect of vision. We studied the representation of motion in mouse retinal bipolar cells and found that some bipolar cells are radially direction selective, preferring the origin of small object motion trajectories. Using a glutamate sensor, we directly observed bipolar cells synaptic output and found that there are radial direction selective and non-selective bipolar cell types, the majority being selective, and that radial direction selectivity relies on properties of the center-surround receptive field. We used these bipolar cell receptive fields along with connectomics to design biophysical models of downstream cells. The models and additional experiments demonstrated that bipolar cells pass radial direction selective excitation to starburst amacrine cells, which contributes to their directional tuning. As bipolar cells provide excitation to most amacrine and ganglion cells, their radial direction selectivity may contribute to motion processing throughout the visual system.

Retinal Encoding of Natural Scenes

An ultimate goal in retina science is to understand how the neural circuit of the retina processes natural visual scenes. Yet most studies in laboratories have long been performed with simple, artificial visual stimuli such as full-field illumination, spots of light, or gratings. The underlying assumption is that the features of the retina thus identified carry over to the more complex scenario of natural scenes. As the application of corresponding natural settings is becoming more commonplace in experimental investigations, this assumption is being put to the test and opportunities arise to discover processing features that are triggered by specific aspects of natural scenes. Here, we review how natural stimuli have been used to probe, refine, and complement knowledge accumulated under simplified stimuli, and we discuss challenges and opportunities along the way toward a comprehensive understanding of the encoding of natural scenes. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Origins of direction selectivity in the primate retina

From mouse to primate, there is a striking discontinuity in our current understanding of the neural coding of motion direction. In non-primate mammals, directionally selective cell types and circuits are a signature feature of the retina, situated at the earliest stage of the visual process. In primates, by contrast, direction selectivity is a hallmark of motion processing areas in visual cortex, but has not been found in the retina, despite significant effort. Here we combined functional recordings of light-evoked responses and connectomic reconstruction to identify diverse direction-selective cell types in the macaque monkey retina with distinctive physiological properties and synaptic motifs. This circuitry includes an ON-OFF ganglion cell type, a spiking, ON-OFF polyaxonal amacrine cell and the starburst amacrine cell, all of which show direction selectivity. Moreover, we discovered that macaque starburst cells possess a strong, non-GABAergic, antagonistic surround mediated by input from excitatory bipolar cells that is critical for the generation of radial motion sensitivity in these cells. Our findings open a door to investigation of a precortical circuitry that computes motion direction in the primate visual system.

Adaptation in cone photoreceptors contributes to an unexpected insensitivity of primate On parasol retinal ganglion cells to spatial structure in natural images

Neural circuits are constructed from nonlinear building blocks, and not surprisingly overall circuit behavior is often strongly nonlinear. But neural circuits can also behave near linearly, and some circuits shift from linear to nonlinear behavior depending on stimulus conditions. Such control of nonlinear circuit behavior is fundamental to neural computation. Here, we study a surprising stimulus dependence of the responses of macaque On (but not Off) parasol retinal ganglion cells: these cells respond nonlinearly to spatial structure in some stimuli but near linearly to spatial structure in others, including natural inputs. We show that these differences in the linearity of the integration of spatial inputs can be explained by a shift in the balance of excitatory and inhibitory synaptic inputs that originates at least partially from adaptation in the cone photoreceptors. More generally, this highlights how subtle asymmetries in signaling - here in the cone signals - can qualitatively alter circuit computation.

Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration

A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. For salamander ganglion cells, we find that response predictions for cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields.

For understanding how sensory systems operate in the natural environment, an important goal is to develop models that capture neuronal responses to natural stimuli. For retinal ganglion cells, which connect the eye to the brain, current standard models often fail to capture responses to natural visual scenes. This shortcoming is at least partly rooted in the fact that ganglion cells may combine visual signals over space in a nonlinear fashion. We here show that a simple model, which not only considers the average light intensity inside a cell’s receptive field but also the variance of light intensity over space, can partly account for these nonlinearities and thereby improve current standard models. This provides an easy-to-obtain benchmark for modeling ganglion cell responses to natural images.

Inhibition, but not excitation, recovers from partial cone loss with greater spatiotemporal integration, synapse density, and frequency

Neural circuits function in the face of changing inputs, either caused by normal variation in stimuli or by cell death. To maintain their ability to perform essential computations with partial inputs, neural circuits make modifications. Here, we study the retinal circuit’s responses to changes in light stimuli or in photoreceptor inputs by inducing partial cone death in the mature mouse retina. Can the retina withstand or recover from input loss? We find that the excitatory pathways exhibit functional loss commensurate with cone death and with some aspects predicted by partial light stimulation. However, inhibitory pathways recover functionally from lost input by increasing spatiotemporal integration in a way that is not recapitulated by partially stimulating the control retina. Anatomically, inhibitory synapses are upregulated on secondary bipolar cells and output ganglion cells. These findings demonstrate the greater capacity for inhibition, compared with excitation, to modify spatiotemporal processing with fewer cone inputs.

Lee et al. find partial cone loss triggers inhibition, but not excitation, to increase spatiotemporal integration, recover contrast gain, and increase synaptic release onto retinal ganglion cells. Natural images filtered by cone-loss receptive fields perceptually match those of controls. Thus, inhibition compensates for fewer cones to potentially preserve perception.

Impact of Photoreceptor Loss on Retinal Circuitry

Our sense of sight relies on photoreceptors, which transduce photons into the nervous system's electrochemical interpretation of the visual world. These precious photoreceptors can be disrupted by disease, injury, and aging. Once photoreceptors start to die, but before blindness occurs, the remaining retinal circuitry can withstand, mask, or exacerbate the photoreceptor deficit and potentially be receptive to newfound therapies for vision restoration. To maximize the retina's receptivity to therapy, one must understand the conditions that influence the state of the remaining retina. In this review, we provide an overview of the retina's structure and function in health and disease. We analyze a collection of observations on photoreceptor disruption and generate a predictive model to identify parameters that influence the retina's response. Finally, we speculate on whether the retina, with its remarkable capacity to function over light levels spanning nine orders of magnitude, uses these same adaptational mechanisms to withstand and perhaps mask photoreceptor loss.

Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli

The retina dissects the visual scene into parallel information channels, which extract specific visual features through nonlinear processing. The first nonlinear stage is typically considered to occur at the output of bipolar cells, resulting from nonlinear transmitter release from synaptic terminals. In contrast, we show here that bipolar cells themselves can act as nonlinear processing elements at the level of their somatic membrane potential. Intracellular recordings from bipolar cells in the salamander retina revealed frequent nonlinear integration of visual signals within bipolar cell receptive field centers, affecting the encoding of artificial and natural stimuli. These nonlinearities provide sensitivity to spatial structure below the scale of bipolar cell receptive fields in both bipolar and downstream ganglion cells and appear to arise at the excitatory input into bipolar cells. Thus, our data suggest that nonlinear signal pooling starts earlier than previously thought: that is, at the input stage of bipolar cells.

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Some retinal bipolar cells represent visual contrast in a nonlinear fashion

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These bipolar cells also nonlinearly integrate visual signals over space

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The spatial nonlinearity affects the encoding of natural stimuli by bipolar cells

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The nonlinearity results from feedforward input, not from feedback inhibition

Linear and nonlinear chromatic integration in the mouse retina

The computations performed by a neural circuit depend on how it integrates its input signals into an output of its own. In the retina, ganglion cells integrate visual information over time, space, and chromatic channels. Unlike the former two, chromatic integration is largely unexplored. Analogous to classical studies of spatial integration, we here study chromatic integration in mouse retina by identifying chromatic stimuli for which activation from the green or UV color channel is maximally balanced by deactivation through the other color channel. This reveals nonlinear chromatic integration in subsets of On, Off, and On-Off ganglion cells. Unlike the latter two, nonlinear On cells display response suppression rather than activation under balanced chromatic stimulation. Furthermore, nonlinear chromatic integration occurs independently of nonlinear spatial integration, depends on contributions from the rod pathway and on surround inhibition, and may provide information about chromatic boundaries, such as the skyline in natural scenes.

Computational challenges and opportunities for a bi-directional artificial retina

A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve more acute visual perception will also require substantial computational advances. Using high-density large-scale recording and stimulation in the primate retina with an ex vivo multi-electrode array lab prototype, we frame several of the major computational problems, and describe current progress and future opportunities in solving them. First, we identify cell types and locations from spontaneous activity in the blind retina, and then efficiently estimate their visual response properties by using a low-dimensional manifold of inter-retina variability learned from a large experimental dataset. Second, we estimate retinal responses to a large collection of relevant electrical stimuli by passing current patterns through an electrode array, spike sorting the resulting recordings and using the results to develop a model of evoked responses. Third, we reproduce the desired responses for a given visual target by temporally dithering a diverse collection of electrical stimuli within the integration time of the visual system. Together, these novel approaches may substantially enhance artificial vision in a next-generation device.

On optimal coupling of the 'electronic photoreceptors' into the degenerate retina

Objective

To restore sight in atrophic age-related macular degeneration, the lost photoreceptors can be replaced with electronic implants, which replicate their two major functions: (1) converting light into an electric signal, and (2) transferring visual information to the secondary neurons in the retinal neural network—the bipolar cells (BC). We study the selectivity of BC activation by subretinal implants and dynamics of their response to pulsatile waveforms in order to optimize the electrical stimulation scheme such that retinal signal processing with 'electronic photoreceptors' remains as close to natural as possible.

Approach

A multicompartmental model of a BC was implemented to simulate responses of the voltage-gated calcium channels and subsequent synaptic vesicle release under continuous and pulsatile stimuli. We compared the predicted response under various frequencies, pulse durations, and alternating gratings to the corresponding experimental measurements. In addition, electric field was computed for various electrode configurations in a 3-d finite element model to assess the stimulation selectivity via spatial confinement of the field.

Main results

The modeled BC-mediated retinal responses were, in general, in good agreement with previously published experimental results. Kinetics of the calcium pumps and of the neurotransmitter release in ribbon synapses, which underpin the BC's temporal filtering and rectifying functions, allow mimicking the natural BC response with high frequency pulsatile stimulation, thereby preserving features of the retinal signal processing, such as flicker fusion, adaptation to static stimuli and non-linear summation of subunits in receptive field. Selectivity of the BC stimulation while avoiding direct activation of the downstream neurons (amacrine and ganglion cells—RGCs) is improved with local return electrodes.

Significance

If the retinal neural network is preserved to a large extent in age-related macular degeneration, selective stimulation of BCs with proper spatial and temporal modulation of the extracellular electric field may retain many features of the natural retinal signal processing and hence allow highly functional restoration of sight.

Inference of nonlinear receptive field subunits with spike-triggered clustering

Responses of sensory neurons are often modeled using a weighted combination of rectified linear subunits. Since these subunits often cannot be measured directly, a flexible method is needed to infer their properties from the responses of downstream neurons. We present a method for maximum likelihood estimation of subunits by soft-clustering spike-triggered stimuli, and demonstrate its effectiveness in visual neurons. For parasol retinal ganglion cells in macaque retina, estimated subunits partitioned the receptive field into compact regions, likely representing aggregated bipolar cell inputs. Joint clustering revealed shared subunits between neighboring cells, producing a parsimonious population model. Closed-loop validation, using stimuli lying in the null space of the linear receptive field, revealed stronger nonlinearities in OFF cells than ON cells. Responses to natural images, jittered to emulate fixational eye movements, were accurately predicted by the subunit model. Finally, the generality of the approach was demonstrated in macaque V1 neurons.

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

Descriptive statistical models of neural responses generally aim to characterize the mapping from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the conductance-based encoding model (CBEM), which describes a mapping from stimuli to excitatory and inhibitory synaptic conductances governing the dynamics of sub-threshold membrane potential. Remarkably, we show that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents. We validate these predictions with intracellular recordings from macaque retinal ganglion cells. Moreover, we offer a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced. This work forges a new link between statistical and biophysical models of neural encoding and sheds new light on the biophysical variables that underlie spiking in the early visual pathway.

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.

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.

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.

Non-linearities in the Rod and Cone Photoreceptor Inputs to the Afferent Pupil Light Response

Purpose: To assess the nature and extent of non-linear processes in pupil responses using rod- and cone-isolating visual beat stimuli.

Methods: A four-primary photostimulating method based on the principle of silent substitution was implemented to generate rod or cone isolating and combined sinusoidal stimuli at a single component frequency (1, 4, 5, 8, or 9 Hz) or a 1 Hz beat frequency (frequency pairs: 4 + 5, 8 + 9 Hz). The component frequencies were chosen to minimize the melanopsin photoresponse of intrinsically photosensitive retinal ganglion cells (ipRGCs) such that the pupil response was primarily driven by outer retinal photoreceptor inputs. Full-field (Ganzfeld) pupil responses and electroretinograms (ERGs) were recorded to the same stimuli at two mesopic light levels (−0.9 and 0 log cd/m²). Fourier analysis was used to derive the amplitudes and phases of the pupil and ERG responses.

Results: For the beat frequency condition, when modulation was restricted to the same photoreceptor type at the higher mesopic level (0 log cd/m²), there was a pronounced pupil response to the 1 Hz beat frequency with the 4 + 5 Hz frequency pair and rare beat responses for the 8 + 9 Hz frequency pair. At the lower mesopic level there were few and inconsistent beat responses. When one component modulated the rod excitation and the other component modulated the cone excitation, responses to the beat frequency were rare and lower than the 1 Hz component frequency condition responses. These results were confirmed by ERG recordings.

Conclusions: There is non-linearity in both the pupil response and electroretinogram to rod and cone inputs at mesopic light levels. The presence of a beat response for modulation components restricted to a single photoreceptor type, but not for components with cross-photoreceptor types, indicates that the location of a non-linear process in the pupil pathway occurs at a retinal site earlier than where the rod and cone signals are combined, that is, at the photoreceptor level.

Synaptic Convergence Patterns onto Retinal Ganglion Cells Are Preserved despite Topographic Variation in Pre- and Postsynaptic Territories

Sensory processing can be tuned by a neuron's integration area, the types of inputs, and the proportion and number of connections with those inputs. Integration areas often vary topographically to sample space differentially across regions. Here, we highlight two visual circuits in which topographic changes in the postsynaptic retinal ganglion cell (RGC) dendritic territories and their presynaptic bipolar cell (BC) axonal territories are either matched or unmatched. Despite this difference, in both circuits, the proportion of inputs from each BC type, i.e., synaptic convergence between specific BCs and RGCs, remained constant across varying dendritic territory sizes. Furthermore, synapse density between BCs and RGCs was invariant across topography. Our results demonstrate a wiring design, likely engaging homotypic axonal tiling of BCs, that ensures consistency in synaptic convergence between specific BC types onto their target RGCs while enabling independent regulation of pre- and postsynaptic territory sizes and synapse number between cell pairs.

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.

Spatial summation in the human fovea: Do normal optical aberrations and fixational eye movements have an effect?

Psychophysical inferences about the neural mechanisms supporting spatial vision can be undermined by uncertainties introduced by optical aberrations and fixational eye movements, particularly in fovea where the neuronal grain of the visual system is fine. We examined the effect of these preneural factors on photopic spatial summation in the human fovea using a custom adaptive optics scanning light ophthalmoscope that provided control over optical aberrations and retinal stimulus motion. Consistent with previous results, Ricco's area of complete summation encompassed multiple photoreceptors when measured with ordinary amounts of ocular aberrations and retinal stimulus motion. When both factors were minimized experimentally, summation areas were essentially unchanged, suggesting that foveal spatial summation is limited by postreceptoral neural pooling. We compared our behavioral data to predictions generated with a physiologically-inspired front-end model of the visual system, and were able to capture the shape of the summation curves obtained with and without pre-retinal factors using a single postreceptoral summing filter of fixed spatial extent. Given our data and modeling, neurons in the magnocellular visual pathway, such as parasol ganglion cells, provide a candidate neural correlate of Ricco's area in the central fovea.

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.

Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons

Implantable retinal stimulators activate surviving neurons to restore a sense of vision in people who have lost their photoreceptors through degenerative diseases. Complex spatial and temporal interactions occur in the retina during multi-electrode stimulation. Due to these complexities, most existing implants activate only a few electrodes at a time, limiting the repertoire of available stimulation patterns. Measuring the spatiotemporal interactions between electrodes and retinal cells, and incorporating them into a model may lead to improved stimulation algorithms that exploit the interactions. Here, we present a computational model that accurately predicts both the spatial and temporal nonlinear interactions of multi-electrode stimulation of rat retinal ganglion cells (RGCs). The model was verified using in vitro recordings of ON, OFF, and ON-OFF RGCs in response to subretinal multi-electrode stimulation with biphasic pulses at three stimulation frequencies (10, 20, 30 Hz). The model gives an estimate of each cell’s spatiotemporal electrical receptive fields (ERFs); i.e., the pattern of stimulation leading to excitation or suppression in the neuron. All cells had excitatory ERFs and many also had suppressive sub-regions of their ERFs. We show that the nonlinearities in observed responses arise largely from activation of presynaptic interneurons. When synaptic transmission was blocked, the number of sub-regions of the ERF was reduced, usually to a single excitatory ERF. This suggests that direct cell activation can be modeled accurately by a one-dimensional model with linear interactions between electrodes, whereas indirect stimulation due to summated presynaptic responses is nonlinear.

Implantable neural stimulation devices are being widely used and clinically tested for the restoration of lost function (e.g. cochlear implants) and the treatment of neurological disorders. Smart devices that can combine sensing and stimulation will dramatically improve future patient outcomes. To this end, mathematical models that can accurately predict neural responses to electrical stimulation will be critical for the development of smart stimulation devices. Here, we demonstrate a model that predicts neural responses to simultaneous stimulation across multiple electrodes in the retina. We show that the activation of presynaptic neurons leads to nonlinearities in the responses of postsynaptic retinal ganglion cells. The model is accurate and is applicable to a wide range of neural stimulation devices.

Diverse inhibitory and excitatory mechanisms shape temporal tuning in transient OFF α ganglion cells in the rabbit retina

KEY POINTS

Neurons combine excitatory and inhibitory signals to perform computations. In the retina, interactions between excitation and inhibition enable neurons to detect specific visual features. We describe how several excitatory and inhibitory mechanisms work together to allow transient OFF α ganglion cells in the rabbit retina to respond selectively to high temporal frequencies and thus detect faster image motion. The weightings of these different mechanisms change with the contrast and spatiotemporal properties of the visual input, and thereby support temporal tuning in α cells over a range of visual conditions. The results help us understand how ganglion cells selectively integrate excitatory and inhibitory signals to extract specific information from the visual input.

ABSTRACT

The 20 to 30 types of ganglion cell in the mammalian retina represent parallel signalling pathways that convey different information to the brain. α ganglion cells are selective for high temporal frequencies in visual inputs, which makes them particularly sensitive to rapid motion. Although α ganglion cells have been studied in several species, the synaptic basis for their selective temporal tuning remains unclear. Here, we analyse excitatory synaptic inputs to transient OFF α ganglion cells (t-OFF α GCs) in the rabbit retina. We show that convergence of excitatory and inhibitory synaptic inputs within the bipolar cell terminals presynaptic to the t-OFF α GCs shifts the temporal tuning to higher temporal frequencies. GABAergic inhibition suppresses the excitatory input at low frequencies, but potentiates it at high frequencies. Crossover glycinergic inhibition and sodium channel activity in the presynaptic bipolar cells also potentiate high frequency excitatory inputs. We found differences in the spatial and temporal properties, and contrast sensitivities of these mechanisms. These differences in stimulus selectivity allow these mechanisms to generate bandpass temporal tuning of t-OFF α GCs over a range of visual conditions.

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.

Multiplexed computations in retinal ganglion cells of a single type

In the early visual system, cells of the same type perform the same computation in different places of the visual field. How these cells code together a complex visual scene is unclear. A common assumption is that cells of a single-type extract a single-stimulus feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the rat retina, we show that a homogeneous population of fast OFF ganglion cells simultaneously encodes two radically different features of a visual scene. Cells close to a moving object code quasilinearly for its position, while distant cells remain largely invariant to the object's position and, instead, respond nonlinearly to changes in the object's speed. We develop a quantitative model that accounts for this effect and identify a disinhibitory circuit that mediates it. Ganglion cells of a single type thus do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems.

Mind the Gap Junctions: The Importance of Electrical Synapses to Visual Processing

In this issue of Neuron, Kuo et al. (2016) show that coordinated interaction between electrical and chemical synapses in a defined retinal circuit enhances sensitivity to moving objects. Their work demonstrates how electrical and chemical synapses combine to improve information processing in a specific area of the CNS.

Local synaptic integration enables ON-OFF asymmetric and layer-specific visual information processing in vGluT3 amacrine cell dendrites

A basic scheme of neuronal organization in the mammalian retina is the segregation of ON and OFF pathways in the inner plexiform layer (IPL), where glutamate is released from ON and OFF bipolar cell terminals in separate inner (ON) and outer (OFF) sublayers in response to light intensity increments and decrements, respectively. However, recent studies have found that vGluT3-expressing glutamatergic amacrine cells (GACs) generate ON-OFF somatic responses and release glutamate onto both ON and OFF ganglion cell types, raising the possibility of crossover excitation in violation of the canonical ON-OFF segregation scheme. To test this possibility, we recorded light-evoked Ca²⁺ responses from dendrites of individual GACs infected with GCaMP6s in mouse. Under two-photon imaging, a single GAC generated rectified local dendritic responses, showing ON-dominant responses in ON sublayers and OFF-dominant responses in OFF sublayers. This unexpected ON-OFF segregation within a small-field amacrine cell arose from local synaptic processing, mediated predominantly by synaptic inhibition. Multiple forms of synaptic inhibition compartmentalized the GAC dendritic tree and endowed all dendritic varicosities with a small-center, strong-surround receptive field, which varied in receptive field size and degree of ON-OFF asymmetry with IPL depth. The results reveal a form of short-range dendritic autonomy that enables a small-field, dual-transmitter amacrine cell to process diverse dendritic functions in a stratification level- and postsynaptic target-specific manner, while preserving the fundamental ON-OFF segregation scheme for parallel visual processing and high spatial resolution for small object motion and uniformity detection.

Selective synaptic connections in the retinal pathway for night vision

The mammalian retina encodes visual information in dim light using rod photoreceptors and a specialized circuit: rods→rod bipolar cells→AII amacrine cell. The AII amacrine cell uses sign-conserving electrical synapses to modulate ON cone bipolar cell terminals and sign-inverting chemical (glycinergic) synapses to modulate OFF cone cell bipolar terminals; these ON and OFF cone bipolar terminals then drive the output neurons, retinal ganglion cells (RGCs), following light increments and decrements, respectively. The AII amacrine cell also makes direct glycinergic synapses with certain RGCs, but it is not well established how many types receive this direct AII input. Here, we investigated functional AII amacrine→RGC synaptic connections in the retina of the guinea pig (Cavia porcellus) by recording inhibitory currents from RGCs in the presence of ionotropic glutamate receptor (iGluR) antagonists. This condition isolates a specific pathway through the AII amacrine cell that does not require iGluRs: cone→ON cone bipolar cell→AII amacrine cell→RGC. These recordings show that AII amacrine cells make direct synapses with OFF Alpha, OFF Delta and a smaller OFF transient RGC type that co-stratifies with OFF Alpha cells. However, AII amacrine cells avoid making synapses with numerous RGC types that co-stratify with the connected RGCs. Selective AII connections ensure that a privileged minority of RGC types receives direct input from the night-vision pathway, independent from OFF bipolar cell activity. Furthermore, these results illustrate the specificity of retinal connections, which cannot be predicted solely by co-stratification of dendrites and axons within the inner plexiform layer.

Diversity in spatial scope of contrast adaptation among mouse retinal ganglion cells

Retinal ganglion cells adapt to changes in visual contrast by adjusting their response kinetics and sensitivity. While much work has focused on the time scales of these adaptation processes, less is known about the spatial scale of contrast adaptation. For example, do small, localized contrast changes affect a cell's signal processing across its entire receptive field? Previous investigations have provided conflicting evidence, suggesting that contrast adaptation occurs either locally within subregions of a ganglion cell's receptive field or globally over the receptive field in its entirety. Here, we investigated the spatial extent of contrast adaptation in ganglion cells of the isolated mouse retina through multielectrode-array recordings. We applied visual stimuli so that ganglion cell receptive fields contained regions where the average contrast level changed periodically as well as regions with constant average contrast level. This allowed us to analyze temporal stimulus integration and sensitivity separately for stimulus regions with and without contrast changes. We found that the spatial scope of contrast adaptation depends strongly on cell identity, with some ganglion cells displaying clear local adaptation, whereas others, in particular large transient ganglion cells, adapted globally to contrast changes. Thus, the spatial scope of contrast adaptation in mouse retinal ganglion cells appears to be cell-type specific. This could reflect differences in mechanisms of contrast adaptation and may contribute to the functional diversity of different ganglion cell types.NEW & NOTEWORTHY Understanding whether adaptation of a neuron in a sensory system can occur locally inside the receptive field or whether it always globally affects the entire receptive field is important for understanding how the neuron processes complex sensory stimuli. For mouse retinal ganglion cells, we here show that both local and global contrast adaptation exist and that this diversity in spatial scope can contribute to the functional diversity of retinal ganglion cell types.

Four alpha ganglion cell types in mouse retina: Function, structure, and molecular signatures

The retina communicates with the brain using ≥30 parallel channels, each carried by axons of distinct types of retinal ganglion cells. In every mammalian retina one finds so-called "alpha" ganglion cells (αRGCs), identified by their large cell bodies, stout axons, wide and mono-stratified dendritic fields, and high levels of neurofilament protein. In the mouse, three αRGC types have been described based on responses to light steps: On-sustained, Off-sustained, and Off-transient. Here we employed a transgenic mouse line that labels αRGCs in the live retina, allowing systematic targeted recordings. We characterize the three known types and identify a fourth, with On-transient responses. All four αRGC types share basic aspects of visual signaling, including a large receptive field center, a weak antagonistic surround, and absence of any direction selectivity. They also share a distinctive waveform of the action potential, faster than that of other RGC types. Morphologically, they differ in the level of dendritic stratification within the IPL, which accounts for their response properties. Molecularly, each type has a distinct signature. A comparison across mammals suggests a common theme, in which four large-bodied ganglion cell types split the visual signal into four channels arranged symmetrically with respect to polarity and kinetics.

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.

Neural Processing of Second-Order Motion in the Suprasylvian Cortex of the Cat

Neuronal responses to second-order motion, that is, to spatiotemporal variations of texture or contrast, have been reported in several cortical areas of mammals, including the middle-temporal (MT) area in primates. In this study, we investigated whether second-order responses are present in the cat posteromedial lateral suprasylvian (PMLS) cortex, a possible homolog of the primate area MT. The stimuli used were luminance-based sine-wave gratings (first-order) and contrast-modulated carrier stimuli (second-order), which consisted of a high-spatial-frequency static grating (carrier) whose contrast was modulated by a low-spatial-frequency drifting grating (envelope). Results indicate that most PMLS neurons responded to second-order motion and for the vast majority of cells, first- and second-order preferred directions were conserved. However, responses to second-order stimuli were significantly reduced when compared to those evoked by first-order gratings. Circular variance was increased for second-order stimuli, indicating that PMLS direction selectivity was weaker for this type of stimulus. Finally, carrier orientation selectivity was either absent or very broad and had no influence on the envelope's orientation selectivity. In conclusion, our data show that PMLS neurons exhibit similar first- and second-order response profiles and that, akin primate area MT cells, they perform a form-cue invariant analysis of motion signals.

A Computational Framework for Realistic Retina Modeling

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

Identification of a Retinal Circuit for Recurrent Suppression Using Indirect Electrical Imaging

Understanding the function of modulatory interneuron networks is a major challenge, because such networks typically operate over long spatial scales and involve many neurons of different types. Here, we use an indirect electrical imaging method to reveal the function of a spatially extended, recurrent retinal circuit composed of two cell types. This recurrent circuit produces peripheral response suppression of early visual signals in the primate magnocellular visual pathway. We identify a type of polyaxonal amacrine cell physiologically via its distinctive electrical signature, revealed by electrical coupling with ON parasol retinal ganglion cells recorded using a large-scale multi-electrode array. Coupling causes the amacrine cells to fire spikes that propagate radially over long distances, producing GABA-ergic inhibition of other ON parasol cells recorded near the amacrine cell axonal projections. We propose and test a model for the function of this amacrine cell type, in which the extra-classical receptive field of ON parasol cells is formed by reciprocal inhibition from other ON parasol cells in the periphery, via the electrically coupled amacrine cell network.

Nonlinear computations shaping temporal processing of precortical vision

Computations performed by the visual pathway are constructed by neural circuits distributed over multiple stages of processing, and thus it is challenging to determine how different stages contribute on the basis of recordings from single areas. In the current article, we address this problem in the lateral geniculate nucleus (LGN), using experiments combined with nonlinear modeling capable of isolating various circuit contributions. We recorded cat LGN neurons presented with temporally modulated spots of various sizes, which drove temporally precise LGN responses. We utilized simultaneously recorded S-potentials, corresponding to the primary retinal ganglion cell (RGC) input to each LGN cell, to distinguish the computations underlying temporal precision in the retina from those in the LGN. Nonlinear models with excitatory and delayed suppressive terms were sufficient to explain temporal precision in the LGN, and we found that models of the S-potentials were nearly identical, although with a lower threshold. To determine whether additional influences shaped the response at the level of the LGN, we extended this model to use the S-potential input in combination with stimulus-driven terms to predict the LGN response. We found that the S-potential input "explained away" the major excitatory and delayed suppressive terms responsible for temporal patterning of LGN spike trains but revealed additional contributions, largely PULL suppression, to the LGN response. Using this novel combination of recordings and modeling, we were thus able to dissect multiple circuit contributions to LGN temporal responses across retina and LGN, and set the foundation for targeted study of each stage.

Synaptic Rectification Controls Nonlinear Spatial Integration of Natural Visual Inputs

A central goal in the study of any sensory system is to predict neural responses to complex inputs, especially those encountered during natural stimulation. Nowhere is the transformation from stimulus to response better understood than the vertebrate retina. Nevertheless, descriptions of retinal computation are largely based on stimulation using artificial visual stimuli, and it is unclear how these descriptions map onto the encoding of natural stimuli. We demonstrate that nonlinear spatial integration, a common feature of retinal ganglion cell (RGC) processing, shapes neural responses to natural visual stimuli in primate Off parasol RGCs, whereas On parasol RGCs exhibit surprisingly linear spatial integration. Despite this asymmetry, both cell types show strong nonlinear integration when presented with artificial stimuli. We show that nonlinear integration of natural stimuli is a consequence of rectified excitatory synaptic input and that accounting for nonlinear spatial integration substantially improves models that predict RGC responses to natural images.

Subcellular Imaging of Voltage and Calcium Signals Reveals Neural Processing In Vivo

A mechanistic understanding of neural computation requires determining how information is processed as it passes through neurons and across synapses. However, it has been challenging to measure membrane potential changes in axons and dendrites in vivo. We use in vivo, two-photon imaging of novel genetically encoded voltage indicators, as well as calcium imaging, to measure sensory stimulus-evoked signals in the Drosophila visual system with subcellular resolution. Across synapses, we find major transformations in the kinetics, amplitude, and sign of voltage responses to light. We also describe distinct relationships between voltage and calcium signals in different neuronal compartments, a substrate for local computation. Finally, we demonstrate that ON and OFF selectivity, a key feature of visual processing across species, emerges through the transformation of membrane potential into intracellular calcium concentration. By imaging voltage and calcium signals to map information flow with subcellular resolution, we illuminate where and how critical computations arise.

Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina

Electrical and chemical synapses coexist in circuits throughout the CNS. Yet, it is not well understood how electrical and chemical synaptic transmission interact to determine the functional output of networks endowed with both types of synapse. We found that release of glutamate from bipolar cells onto retinal ganglion cells (RGCs) was strongly shaped by gap-junction-mediated electrical coupling within the bipolar cell network of the mouse retina. Specifically, electrical synapses spread signals laterally between bipolar cells, and this lateral spread contributed to a nonlinear enhancement of bipolar cell output to visual stimuli presented closely in space and time. Our findings thus (1) highlight how electrical and chemical transmission can work in concert to influence network output and (2) reveal a previously unappreciated circuit mechanism that increases RGC sensitivity to spatiotemporally correlated input, such as that produced by motion.

Differential signalling and glutamate receptor compositions in the OFF bipolar cell types in the mouse retina

KEY POINTS

Using whole-cell clamp methods, we characterized the temporal coding in each type of OFF bipolar cell. We found that type 2 and 3a cells are transient, type 1 and 4 cells are sustained, and type 3b cells are intermediate. The light-evoked excitatory postsynaptic potentials in some types were rectified, suggesting that they provide inputs to the non-linear ganglion cells. Visual signalling from the photoreceptors was mediated exclusively through the kainate receptors in the transient OFF bipolar cells, whereas both kainate and AMPA receptors contributed in the other cells. This study demonstrates, for the first time, that parallel visual encoding starts at the OFF bipolar cells in a type-specific manner.

ABSTRACT

The retina is the entrance to the visual system, which receives various kinds of image signals and forms multiple encoding pathways. The second-order retinal neurons, the bipolar cells, are thought to initiate multiple neural streams by encoding various visual signals in different types of cells. However, the functions of each bipolar cell type have not been fully understood. We investigated whether OFF bipolar cells encode visual signals in a type-dependent manner. We recorded the changes in the bipolar cell voltage in response to two input functions: step and sinusoidal light stimuli. Type 1 and 4 OFF bipolar cells were sustained cells and responded to sinusoidal stimuli over a broad range of frequencies. Type 2 and 3a cells were transient and exhibited band-pass filtering. Type 3b cells were in the middle of these two groups. The distinct temporal responses might be attributed to different types of glutamate receptors. We examined the AMPA and kainate glutamate receptor composition in each bipolar cell type. The light responses in the transient OFF bipolar cells were exclusively mediated by kainate receptors. Although the kainate receptors mediated the light responses in the sustained cells, the AMPA receptors also mediated a portion of the responses in sustained cells. Furthermore, we found that some types of cells were rectified more than other types. Taken together, we found that the OFF bipolar cells encode diverse temporal image signals in a type-dependent manner, confirming that each type of OFF bipolar cell initiates diverse temporal visual processing in parallel.

Functional Circuitry of the Retina

The mammalian retina is an important model system for studying neural circuitry: Its role in sensation is clear, its cell types are relatively well defined, and its responses to natural stimuli-light patterns-can be studied in vitro. To solve the retina, we need to understand how the circuits presynaptic to its output neurons, ganglion cells, divide the visual scene into parallel representations to be assembled and interpreted by the brain. This requires identifying the component interneurons and understanding how their intrinsic properties and synapses generate circuit behaviors. Because the cellular composition and fundamental properties of the retina are shared across species, basic mechanisms studied in the genetically modifiable mouse retina apply to primate vision. We propose that the apparent complexity of retinal computation derives from a straightforward mechanism-a dynamic balance of synaptic excitation and inhibition regulated by use-dependent synaptic depression-applied differentially to the parallel pathways that feed ganglion cells.