Spatial segregation of adaptation and predictive sensitization in retinal ganglion cells

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function parametrized to mimic f-I curves of biological neurons, either by learning an individual static function or via a learned and shared adaptation mechanism to modify activation functions in real-time during a task. We find that such adaptive networks show much-improved robustness to noise and changes in input statistics. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time. Finally, we outline similarities between these optimized solutions and known coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration.

Localized and Long-Lasting Adaptation in Dragonfly Target-Detecting Neurons

Some visual neurons in the dragonfly (Hemicordulia tau) optic lobe respond to small, moving targets, likely underlying their fast pursuit of prey and conspecifics. In response to repetitive targets presented at short intervals, the spiking activity of these "small target motion detector" (STMD) neurons diminishes over time. Previous experiments limited this adaptation by including intertrial rest periods of varying durations. However, the characteristics of this effect have never been quantified. Here, using extracellular recording techniques lasting for several hours, we quantified both the spatial and temporal properties of STMD adaptation. We found that the time course of adaptation was variable across STMD units. In any one STMD, a repeated series led to more rapid adaptation, a minor accumulative effect more akin to habituation. Following an adapting stimulus, responses recovered quickly, though the rate of recovery decreased nonlinearly over time. We found that the region of adaptation is highly localized, with targets displaced by ∼2.5° eliciting a naive response. Higher frequencies of target stimulation converged to lower levels of sustained response activity. We determined that adaptation itself is a target-tuned property, not elicited by moving bars or luminance flicker. As STMD adaptation is a localized phenomenon, dependent on recent history, it is likely to play an important role in closed-loop behavior where a target is foveated in a localized region for extended periods of the pursuit duration.

Adaptation in the visual system: Networked fatigue or suppressed prediction error signalling?

Our brains are constantly adapting to changes in our visual environments. Neural adaptation exerts a persistent influence on the activity of sensory neurons and our perceptual experience, however there is a lack of consensus regarding how adaptation is implemented in the visual system. One account describes fatigue-based mechanisms embedded within local networks of stimulus-selective neurons (networked fatigue models). Another depicts adaptation as a product of stimulus expectations (predictive coding models). In this review, I evaluate neuroimaging and psychophysical evidence that poses fundamental problems for predictive coding models of neural adaptation. Specifically, I discuss observations of distinct repetition and expectation effects, as well as incorrect predictions of repulsive adaptation aftereffects made by predictive coding accounts. Based on this evidence, I argue that networked fatigue models provide a more parsimonious account of adaptation effects in the visual system. Although stimulus expectations can be formed based on recent stimulation history, any consequences of these expectations are likely to co-occur (or interact) with effects of fatigue-based adaptation. I conclude by proposing novel, testable hypotheses relating to interactions between fatigue-based adaptation and other predictive processes, focusing on stimulus feature extrapolation phenomena.

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

A fundamental task for the brain is to generate predictions of future sensory inputs, and signal errors in these predictions. Many neurons have been shown to signal omitted stimuli during periodic stimulation, even in the retina. However, the mechanisms of this error signaling are unclear. Here we show that depressing inhibitory synapses shape the timing of the response to an omitted stimulus in the retina. While ganglion cells, the retinal output, responded to an omitted flash with a constant latency over many frequencies of the flash sequence, we found that this was not the case once inhibition was blocked. We built a simple circuit model and showed that depressing inhibitory synapses were a necessary component to reproduce our experimental findings. A new prediction of our model is that the accuracy of the constant latency requires a sufficient amount of flashes in the stimulus, which we could confirm experimentally. Depressing inhibitory synapses could thus be a key component to generate the predictive responses observed in the retina, and potentially in many brain areas.

GABA receptors mediate adaptation and sensitization processes in mouse retinal ganglion cells

Two coordinated dynamic properties (adaptation and sensitization) are observed in retinal ganglion cells (RGCs) under the contrast stimulation. During sustained high-contrast period, adaptation decreases RGCs' responses while sensitization increases RGCs' responses. In mouse retina, adaptation and sensitization respectively show OFF- and ON-pathway-dominance. However, the mechanisms which drive the differentiation between adaptation and sensitization remain unclear. In the present study, multi-electrode recordings were conducted on isolated mouse retina under full-field contrast stimulation. Dynamic property was quantified based on the trend of RGC's firing rate during high-contrast period, light sensitivity was estimated by linear-nonlinear analysis and coding ability was estimated through stimulus reconstruction algorism. γ-Aminobutyric acid (GABA) receptors were pharmacologically blocked to explore the relation between RGCs' dynamic property and the activity of GABA receptors. It was found that GABAA and GABAC receptors respectively mediated the adaptation and sensitization processes in RGCs' responses. RGCs' dynamic property changes occurred after the blockage of GABA receptors were related to the modulation of the cells' light sensitivity. Further, the blockage of GABAA (GABAC) receptor significantly decreased RGCs' overall coding ability and eliminated the functional benefits of adaptation (sensitization). Our work suggests that the dynamic property of individual RGC is related to the balance between its GABAA-receptor-mediated inputs and GABAC-receptor-mediated inputs. Blockage of GABA receptors breaks the balance of retinal circuitry for signal processing, and down-regulates the visual information coding ability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09950-2.

Two Sides of the Same Coin: Efficient and Predictive Neural Coding

Some visual properties are consistent across a wide range of environments, while other properties are more labile. The efficient coding hypothesis states that many of these regularities in the environment can be discarded from neural representations, thus allocating more of the brain's dynamic range to properties that are likely to vary. This paradigm is less clear about how the visual system prioritizes different pieces of information that vary across visual environments. One solution is to prioritize information that can be used to predict future events, particularly those that guide behavior. The relationship between the efficient coding and future prediction paradigms is an area of active investigation. In this review, we argue that these paradigms are complementary and often act on distinct components of the visual input. We also discuss how normative approaches to efficient coding and future prediction can be integrated.

Calibrating Vision: Concepts and Questions

The idea that visual coding and perception are shaped by experience and adjust to changes in the environment or the observer is universally recognized as a cornerstone of visual processing, yet the functions and processes mediating these calibrations remain in many ways poorly understood. In this article we review a number of facets and issues surrounding the general notion of calibration, with a focus on plasticity within the encoding and representational stages of visual processing. These include how many types of calibrations there are - and how we decide; how plasticity for encoding is intertwined with other principles of sensory coding; how it is instantiated at the level of the dynamic networks mediating vision; how it varies with development or between individuals; and the factors that may limit the form or degree of the adjustments. Our goal is to give a small glimpse of an enormous and fundamental dimension of vision, and to point to some of the unresolved questions in our understanding of how and why ongoing calibrations are a pervasive and essential element of vision.

Visual Stimulation Induces Distinct Forms of Sensitization of On-Off Direction-Selective Ganglion Cell Responses in the Dorsal and Ventral Retina

Experience-dependent modulation of neuronal responses is a key attribute in sensory processing. In the mammalian retina, the On-Off direction-selective ganglion cell (DSGC) is well known for its robust direction selectivity. However, how the On-Off DSGC light responsiveness dynamically adjusts to the changing visual environment is underexplored. Here, we report that On-Off DSGCs tuned to posterior motion direction [i.e. posterior DSGCs (pDSGCs)] in mice of both sexes can be transiently sensitized by prior stimuli. Notably, distinct sensitization patterns are found in dorsal and ventral pDSGCs. Although responses of both dorsal and ventral pDSGCs to dark stimuli (Off responses) are sensitized, only dorsal cells show the sensitization of responses to bright stimuli (On responses). Visual stimulation to the dorsal retina potentiates a sustained excitatory input from Off bipolar cells, leading to tonic depolarization of pDSGCs. Such tonic depolarization propagates from the Off to the On dendritic arbor of the pDSGC to sensitize its On response. We also identified a previously overlooked feature of DSGC dendritic architecture that can support dendritic integration between On and Off dendritic layers bypassing the soma. By contrast, ventral pDSGCs lack a sensitized tonic depolarization and thus do not exhibit sensitization of their On responses. Our results highlight a topographic difference in Off bipolar cell inputs underlying divergent sensitization patterns of dorsal and ventral pDSGCs. Moreover, substantial crossovers between dendritic layers of On-Off DSGCs suggest an interactive dendritic algorithm for processing On and Off signals before they reach the soma.SIGNIFICANCE STATEMENT Visual neuronal responses are dynamically influenced by the prior visual experience. This form of plasticity reflects the efficient coding of the naturalistic environment by the visual system. We found that a class of retinal output neurons, On-Off direction-selective ganglion cells, transiently increase their responsiveness after visual stimulation. Cells located in dorsal and ventral retinas exhibit distinct sensitization patterns because of different adaptive properties of Off bipolar cell signaling. A previously overlooked dendritic morphologic feature of the On-Off direction-selective ganglion cell is implicated in the cross talk between On and Off pathways during sensitization. Together, these findings uncover a topographic difference in the adaptive encoding of upper and lower visual fields and the underlying neural mechanism in the dorsal and ventral retinas.

A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.

Remembering the Past to See the Future

In addition to the role that our visual system plays in determining what we are seeing right now, visual computations contribute in important ways to predicting what we will see next. While the role of memory in creating future predictions is often overlooked, efficient predictive computation requires the use of information about the past to estimate future events. In this article, we introduce a framework for understanding the relationship between memory and visual prediction and review the two classes of mechanisms that the visual system relies on to create future predictions. We also discuss the principles that define the mapping from predictive computations to predictive mechanisms and how downstream brain areas interpret the predictive signals computed by the visual system. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Efficient and adaptive sensory codes

The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.

How inhibitory neurons increase information transmission under threshold modulation

Modulation of neuronal thresholds is ubiquitous in the brain. Phenomena such as figure-ground segmentation, motion detection, stimulus anticipation, and shifts in attention all involve changes in a neuron’s threshold based on signals from larger scales than its primary inputs. However, this modulation reduces the accuracy with which neurons can represent their primary inputs, creating a mystery as to why threshold modulation is so widespread in the brain. We find that modulation is less detrimental than other forms of neuronal variability and that its negative effects can be nearly completely eliminated if modulation is applied selectively to sparsely responding neurons in a circuit by inhibitory neurons. We verify these predictions in the retina where we find that inhibitory amacrine cells selectively deliver modulation signals to sparsely responding ganglion cell types. Our findings elucidate the central role that inhibitory neurons play in maximizing information transmission under modulation.

Modulation of neuronal thresholds is ubiquitous in the brain but reduces the accuracy of neural signaling. Hsu et al. show that the negative impact of threshold modulation can be almost completely eliminated when modulation is not delivered uniformly to all neurons but only to a subset and via inhibitory neurons.

Adjudicating Between Local and Global Architectures of Predictive Processing in the Subcortical Auditory Pathway

Predictive processing, a leading theoretical framework for sensory processing, suggests that the brain constantly generates predictions on the sensory world and that perception emerges from the comparison between these predictions and the actual sensory input. This requires two distinct neural elements: generative units, which encode the model of the sensory world; and prediction error units, which compare these predictions against the sensory input. Although predictive processing is generally portrayed as a theory of cerebral cortex function, animal and human studies over the last decade have robustly shown the ubiquitous presence of prediction error responses in several nuclei of the auditory, somatosensory, and visual subcortical pathways. In the auditory modality, prediction error is typically elicited using so-called oddball paradigms, where sequences of repeated pure tones with the same pitch are at unpredictable intervals substituted by a tone of deviant frequency. Repeated sounds become predictable promptly and elicit decreasing prediction error; deviant tones break these predictions and elicit large prediction errors. The simplicity of the rules inducing predictability make oddball paradigms agnostic about the origin of the predictions. Here, we introduce two possible models of the organizational topology of the predictive processing auditory network: (1) the global view, that assumes that predictions on the sensory input are generated at high-order levels of the cerebral cortex and transmitted in a cascade of generative models to the subcortical sensory pathways; and (2) the local view, that assumes that independent local models, computed using local information, are used to perform predictions at each processing stage. In the global view information encoding is optimized globally but biases sensory representations along the entire brain according to the subjective views of the observer. The local view results in a diminished coding efficiency, but guarantees in return a robust encoding of the features of sensory input at each processing stage. Although most experimental results to-date are ambiguous in this respect, recent evidence favors the global model.

High Contrast Allows the Retina to Compute More Than Just Contrast

The goal of sensory processing is to represent the environment of an animal. All sensory systems share a similar constraint: they need to encode a wide range of stimulus magnitudes within their narrow neuronal response range. The most efficient way, exploited by even the simplest nervous systems, is to encode relative changes in stimulus magnitude rather than the absolute magnitudes. For instance, the retina encodes contrast, which are the variations of light intensity occurring in time and in space. From this perspective, it is easy to understand why the bright plumage of a moving bird gains a lot of attention, while an octopus remains motionless and mimics its surroundings for concealment. Stronger contrasts simply cause stronger visual signals. However, the gains in retinal performance associated with higher contrast are far more than what can be attributed to just a trivial linear increase in signal strength. Here we discuss how this improvement in performance is reflected throughout different parts of the neural circuitry, within its neural code and how high contrast activates many non-linear mechanisms to unlock several sophisticated retinal computations that are virtually impossible in low contrast conditions.

On the potential role of lateral connectivity in retinal anticipation

We analyse the potential effects of lateral connectivity (amacrine cells and gap junctions) on motion anticipation in the retina. Our main result is that lateral connectivity can-under conditions analysed in the paper-trigger a wave of activity enhancing the anticipation mechanism provided by local gain control (Berry et al. in Nature 398(6725):334-338, 1999; Chen et al. in J. Neurosci. 33(1):120-132, 2013). We illustrate these predictions by two examples studied in the experimental literature: differential motion sensitive cells (Baccus and Meister in Neuron 36(5):909-919, 2002) and direction sensitive cells where direction sensitivity is inherited from asymmetry in gap junctions connectivity (Trenholm et al. in Nat. Neurosci. 16:154-156, 2013). We finally present reconstructions of retinal responses to 2D visual inputs to assess the ability of our model to anticipate motion in the case of three different 2D stimuli.

Functional-pathway-dominant contrast adaptation and sensitization in mouse retinal ganglion cells

Retinal ganglion cells (RGCs) reduce their light sensitivity during persistent high-contrast stimulation to prevent saturation to strong inputs and improve coding efficiency. This process is known as contrast adaptation. However, contrast adaptation also reduces RGCs' light response to weak inputs. On the other hand, some RGCs undergo contrast sensitization, and these RGCs respond to weak inputs following high contrast stimulation. In the present study, multi-electrode recordings were conducted on isolated mouse retinas under full-field visual stimulation with different contrast levels. Adaptation and sensitization were mainly observed in OFF and ON pathways, respectively. The results of linear-nonlinear analysis and stimulus reconstruction revealed that both the light sensitivity and encoded information were changed in opposite directions in adaptation and sensitization processes. Our work suggests that contrast adaptation and sensitization are two opposite dynamic processes. In mouse retina, OFF RGCs utilize adaptation to increase the discrimination of strong OFF inputs. On the other hand, ON RGCs use sensitization to increase the sensitivity to weak ON inputs. This functional differentiation might be meaningful for the mouse's survival as it lives in environments in which strong OFF stimuli often indicate potential predators while weak ON stimuli are usually related to movement and might be important for predation.

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.

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.

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.

Adaptation of Inhibition Mediates Retinal Sensitization

In response to a changing sensory environment, sensory systems adjust their neural code for a number of purposes, including an enhanced sensitivity for novel stimuli, prediction of sensory features, and the maintenance of sensitivity. Retinal sensitization is a form of short-term plasticity that elevates local sensitivity following strong, local, visual stimulation and has been shown to create a prediction of the presence of a nearby localized object. The neural mechanism that generates this elevation in sensitivity remains unknown. Using simultaneous intracellular and multielectrode recording in the salamander retina, we show that a decrease in tonic amacrine transmission is necessary for and is correlated spatially and temporally with ganglion cell sensitization. Furthermore, introducing a decrease in amacrine transmission is sufficient to sensitize nearby ganglion cells. A computational model accounting for adaptive dynamics and nonlinear pathways confirms a decrease in steady inhibitory transmission can cause sensitization. Adaptation of inhibition enhances the sensitivity to the sensory feature conveyed by an inhibitory pathway, creating a prediction of future input.

Coding Principles in Adaptation

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.

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.

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.

Learning to make external sensory stimulus predictions using internal correlations in populations of neurons

To compensate for sensory processing delays, the visual system must make predictions to ensure timely and appropriate behaviors. Recent work has found predictive information about the stimulus in neural populations early in vision processing, starting in the retina. However, to utilize this information, cells downstream must be able to read out the predictive information from the spiking activity of retinal ganglion cells. Here we investigate whether a downstream cell could learn efficient encoding of predictive information in its inputs from the correlations in the inputs themselves, in the absence of other instructive signals. We simulate learning driven by spiking activity recorded in salamander retina. We model a downstream cell as a binary neuron receiving a small group of weighted inputs and quantify the predictive information between activity in the binary neuron and future input. Input weights change according to spike timing-dependent learning rules during a training period. We characterize the readouts learned under spike timing-dependent synaptic update rules, finding that although the fixed points of learning dynamics are not associated with absolute optimal readouts they convey nearly all of the information conveyed by the optimal readout. Moreover, we find that learned perceptrons transmit position and velocity information of a moving-bar stimulus nearly as efficiently as optimal perceptrons. We conclude that predictive information is, in principle, readable from the perspective of downstream neurons in the absence of other inputs. This suggests an important role for feedforward prediction in sensory encoding.

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.

A predictive focus of gain modulation encodes target trajectories in insect vision

When a human catches a ball, they estimate future target location based on the current trajectory. How animals, small and large, encode such predictive processes at the single neuron level is unknown. Here we describe small target-selective neurons in predatory dragonflies that exhibit localized enhanced sensitivity for targets displaced to new locations just ahead of the prior path, with suppression elsewhere in the surround. This focused region of gain modulation is driven by predictive mechanisms, with the direction tuning shifting selectively to match the target’s prior path. It involves a large local increase in contrast gain which spreads forward after a delay (e.g. an occlusion) and can even transfer between brain hemispheres, predicting trajectories moved towards the visual midline from the other eye. The tractable nature of dragonflies for physiological experiments makes this a useful model for studying the neuronal mechanisms underlying the brain’s remarkable ability to anticipate moving stimuli.

DOI:http://dx.doi.org/10.7554/eLife.26478.001

Catching a ball requires a person to track the speed and direction of a small moving target often against a cluttered and varying background. Predatory insects, like dragonflies, face a similar challenge when they pursue their prey through the air. The task is made a little easier, however, by the fact that most moving targets tend to follow predictable trajectories. Indeed, animals are also better at tracking targets that follow smooth continuous trajectories, suggesting that brains have evolved to exploit the normal behavior of visual stimuli to reduce their workload

To find out how this process works, Wiederman, Fabian et al. studied the brains of dragonflies as they watched a black square intended to mimic prey. Brain cells called Small Target Motion Detectors (or STMD neurons for short) became more active in response to the target. But rather than simply following the target, the STMD neurons instead predicted its future location. In fact, individual neurons were more sensitive to movements occurring just ahead of the target’s current position, and less sensitive to movements occurring elsewhere.

If the target abruptly disappeared, the point in space where the neurons were most sensitive to movement continued to gradually move forward over time. Given that real-life targets typically disappear when they move behind other objects, this suggests that the brain is predicting where the target is most likely to reappear. The STMD neurons became more sensitive to movement by increasing their ability to detect differences in brightness between the target and its background. In some cases, the neurons increased their sensitivity more than five-fold.

Insects and mammals last shared a common ancestor more than 500 million years ago, and, in many respects, mammalian brains are substantially more complex than insect brains. Nevertheless, the results of Wiederman, Fabian et al. show that the insect brain can perform visual tasks that were previously associated only with mammals. Neuroscientists and engineers have used the insect brain for decades to study the circuits that support biological processes. In the coming years, insects such as the dragonfly may enable us to explore how visual regions of the brain predict future events. This knowledge could ultimately be applied to artificial vision systems, such as those in self-driving cars.

DOI:http://dx.doi.org/10.7554/eLife.26478.002

The feature-specific propagation of orientation and direction adaptation from areas 17 to 21a in cats

Adaptation plays a key role in visual information processing, and investigations on the adaptation across different visual regions will be helpful to understand how information is processed dynamically along the visual streams. Recent studies have found the enhanced adaptation effects in the early visual system (from LGN to V1) and the dorsal stream (from V1 to MT). However, it remains unclear how adaptation effect propagates along the form/orientation stream in the visual system. In this study, we compared the orientation and direction adaptation evoked by drifting gratings and stationary flashing gratings, as well as moving random dots, in areas 17 and 21a simultaneously of cats. Recorded by single-unit and intrinsic signal optical imaging, induced by both top-up and biased adaptation protocols, the orientation adaptation effect was greater in response decline and preferred orientation shifts in area 21a compared to area 17. However, for the direction adaptation, no difference was observed between these two areas. These results suggest the feature-specific propagation of the adaptation effect along the visual stream.

A normalized contrast-encoding model exhibits bright/dark asymmetries similar to early visual neurons

Biological sensory systems share a number of organizing principles. One such principle is the formation of parallel streams. In the visual system, information about bright and dark features is largely conveyed via two separate streams: the ON and OFF pathways. While brightness and darkness can be considered symmetric and opposite forms of visual contrast, the response properties of cells in the ON and OFF pathways are decidedly asymmetric. Here, we ask whether a simple contrast‐encoding model predicts asymmetries for brights and darks that are similar to the asymmetries found in the ON and OFF pathways. Importantly, this model does not include any explicit differences in how the visual system represents brights and darks, but it does include a common normalization mechanism. The phenomena captured by the model include (1) nonlinear contrast response functions, (2) greater nonlinearities in the responses to darks, and (3) larger responses to dark contrasts. We report a direct, quantitative comparison between these model predictions and previously published electrophysiological measurements from the retina and thalamus (guinea pig and cat, respectively). This work suggests that the simple computation of visual contrast may account for a range of early visual processing nonlinearities. Assessing explicit models of sensory representations is essential for understanding which features of neuronal activity these models can and cannot predict, and for investigating how early computations may reverberate through the sensory pathways.

Endocannabinoid signaling enhances visual responses through modulation of intracellular chloride levels in retinal ganglion cells

Type 1 cannabinoid receptors (CB1Rs) are widely expressed in the vertebrate retina, but the role of endocannabinoids in vision is not fully understood. Here, we identified a novel mechanism underlying a CB1R-mediated increase in retinal ganglion cell (RGC) intrinsic excitability acting through AMPK-dependent inhibition of NKCC1 activity. Clomeleon imaging and patch clamp recordings revealed that inhibition of NKCC1 downstream of CB1R activation reduces intracellular Cl⁻ levels in RGCs, hyperpolarizing the resting membrane potential. We confirmed that such hyperpolarization enhances RGC action potential firing in response to subsequent depolarization, consistent with the increased intrinsic excitability of RGCs observed with CB1R activation. Using a dot avoidance assay in freely swimming Xenopus tadpoles, we demonstrate that CB1R activation markedly improves visual contrast sensitivity under low-light conditions. These results highlight a role for endocannabinoids in vision and present a novel mechanism for cannabinoid modulation of neuronal activity through Cl⁻ regulation.

DOI:http://dx.doi.org/10.7554/eLife.15932.001

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.

Postsynaptic Plasticity Triggered by Ca²⁺-Permeable AMPA Receptor Activation in Retinal Amacrine Cells

Amacrine cells are thought to be a major locus for mechanisms of light adaptation and contrast enhancement in the retina. However, the potential for plasticity in their AMPA receptor currents remains largely unknown. Using paired patch-clamp recordings between bipolar cell terminals and amacrine cells, we have simultaneously measured presynaptic membrane capacitance changes and EPSCs. Repetitive bipolar cell depolarizations, designed to maintain the same amount of exocytosis, nevertheless significantly potentiated evoked EPSCs in a subpopulation of amacrine cells. Likewise, repetitive iontophoresis (or puffs) of glutamate (or AMPA) onto the dendrites of amacrine cells also significantly potentiated evoked currents and [Ca(2+)]i rises. However, strong postsynaptic Ca(2+) buffering with BAPTA abolished the potentiation and selective antagonists of Ca(2+)-permeable AMPA receptors also blocked the potentiation of AMPA-mediated currents. Together these results suggest that Ca(2+) influx via Ca(2+)-permeable AMPA receptors can elicit a rapid form of postsynaptic plasticity in a subgroup of amacrine cell dendrites.

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.

Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina

When visual contrast changes, retinal ganglion cells adapt by adjusting their sensitivity as well as their temporal filtering characteristics. The latter has classically been described by contrast-induced gain changes that depend on temporal frequency. Here, we explored a new perspective on contrast-induced changes in temporal filtering by using spike-triggered covariance analysis to extract multiple parallel temporal filters for individual ganglion cells. Based on multielectrode-array recordings from ganglion cells in the isolated salamander retina, we found that contrast adaptation of temporal filtering can largely be captured by contrast-invariant sets of filters with contrast-dependent weights. Moreover, differences among the ganglion cells in the filter sets and their contrast-dependent contributions allowed us to phenomenologically distinguish three types of filter changes. The first type is characterized by newly emerging features at higher contrast, which can be reproduced by computational models that contain response-triggered gain-control mechanisms. The second type follows from stronger adaptation in the Off pathway as compared to the On pathway in On-Off-type ganglion cells. Finally, we found that, in a subset of neurons, contrast-induced filter changes are governed by particularly strong spike-timing dynamics, in particular by pronounced stimulus-dependent latency shifts that can be observed in these cells. Together, our results show that the contrast dependence of temporal filtering in retinal ganglion cells has a multifaceted phenomenology and that a multi-filter analysis can provide a useful basis for capturing the underlying signal-processing dynamics.

Our sensory systems have to process stimuli under a wide range of environmental conditions. To cope with this challenge, the involved neurons adapt by adjusting their signal processing to the recently encountered intensity range. In the visual system, one finds, for example, that higher visual contrast leads to changes in how visual signals are temporally filtered, making signal processing faster and more band-pass-like at higher contrast. By analyzing signals from neurons in the retina of salamanders, we here found that these adaptation effects can be described by a fixed set of filters, independent of contrast, whose relative contributions change with contrast. Also, we found that different phenomena contribute to this adaptation. In particular, some cells change their relative sensitivity to light increments and light decrements, whereas other cells are influenced by a strong contrast-dependence of the exact timing of their responses. Our results show that contrast adaptation in the retina is not an entirely homogeneous phenomenon, and that models with multiple filters can help in characterizing sensory adaptation.

Critical and maximally informative encoding between neural populations in the retina

Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid-gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid-gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment.

Moving sensory adaptation beyond suppressive effects in single neurons

How an object is perceived depends on the temporal context in which it is encountered. Sensory signals in the brain also depend on temporal context, a phenomenon often referred to as adaptation. Traditional descriptions of adaptation effects emphasize various forms of response fatigue in single neurons, which grow in strength with exposure to a stimulus. Recent work on vision, and other sensory modalities, has shown that this description has substantial shortcomings. Here we review our emerging understanding of how adaptation alters the balance between excitatory and suppressive signals, how effects depend on adaptation duration, and how adaptation influences representations that are distributed within and across multiple brain structures. This work points to a sophisticated set of mechanisms for adjusting to recent sensory experience, and suggests new avenues for understanding their function.

Adeno-associated virus-RNAi of GlyRα1 and characterization of its synapse-specific inhibition in OFF alpha transient retinal ganglion cells

In the central nervous system, inhibition shapes neuronal excitation. In spinal cord glycinergic inhibition predominates, whereas GABAergic inhibition predominates in the brain. The retina uses GABA and glycine in approximately equal proportions. Glycinergic crossover inhibition, initiated in the On retinal pathway, controls glutamate release from presynaptic OFF cone bipolar cells (CBCs) and directly shapes temporal response properties of OFF retinal ganglion cells (RGCs). In the retina, four glycine receptor (GlyR) α-subunit isoforms are expressed in different sublaminae and their synaptic currents differ in decay kinetics. GlyRα1, expressed in both On and Off sublaminae of the inner plexiform layer, could be the glycinergic isoform that mediates On-to-Off crossover inhibition. However, subunit-selective glycine contributions remain unknown because we lack selective antagonists or cell class-specific subunit knockouts. To examine the role of GlyRα1 in direct inhibition in mature RGCs, we used retrogradely transported adeno-associated virus (AAV) that performed RNAi and eliminated almost all glycinergic spontaneous and visually evoked responses in PV5 (OFFα(Transient)) RGCs. Comparisons of responses in PV5 RGCs infected with AAV-scrambled-short hairpin RNA (shRNA) or AAV-Glra1-shRNA confirm a role for GlyRα1 in crossover inhibition in cone-driven circuits. Our results also define a role for direct GlyRα1 inhibition in setting the resting membrane potential of PV5 RGCs. The absence of GlyRα1 input unmasked a serial and a direct feedforward GABA(A)ergic modulation in PV5 RGCs, reflecting a complex interaction between glycinergic and GABA(A)ergic inhibition.

Nonlinear spatial integration in the receptive field surround of retinal ganglion cells

Throughout different sensory systems, individual neurons integrate incoming signals over their receptive fields. The characteristics of this signal integration are crucial determinants for the neurons' functions. For ganglion cells in the vertebrate retina, receptive fields are characterized by the well-known center-surround structure and, although several studies have addressed spatial integration in the receptive field center, little is known about how visual signals are integrated in the surround. Therefore, we set out here to characterize signal integration and to identify relevant nonlinearities in the receptive field surround of ganglion cells in the isolated salamander retina by recording spiking activity with extracellular electrodes under visual stimulation of the center and surround. To quantify nonlinearities of spatial integration independently of subsequent nonlinearities of spike generation, we applied the technique of iso-response measurements as follows: using closed-loop experiments, we searched for different stimulus patterns in the surround that all reduced the center-evoked spiking activity by the same amount. The identified iso-response stimuli revealed strongly nonlinear spatial integration in the receptive field surrounds of all recorded cells. Furthermore, cell types that had been shown previously to have different nonlinearities in receptive field centers showed similar surround nonlinearities but differed systematically in the adaptive characteristics of the surround. Finally, we found that there is an optimal spatial scale of surround suppression; suppression was most effective when surround stimulation was organized into subregions of several hundred micrometers in diameter, indicating that the surround is composed of subunits that have strong center-surround organization themselves.