Retina is structured to process an excess of darkness in natural scenes

The functional asymmetry of ON and OFF channels in the perception of contrast

To fully understand the relationship between perception and single neural responses, one should take into consideration the early stages of sensory processing. Few studies, however, have directly examined the neural underpinning of visual perception in the lateral geniculate nucleus (LGN), only one synapse away from the retina. In this study we recorded from LGN parvocellular (P) ON-center and OFF-center neurons while monkeys either passively viewed or actively detected a full range of contrasts. We found that OFF neurons were more sensitive in detecting negative contrasts than ON neurons were in detecting positive contrasts. Also, OFF neurons had higher spontaneous activities, higher peak response amplitudes, and were more sustained than ON neurons in their contrast responses. Puzzlingly, OFF neurons failed to show any significant correlations with the monkeys' perceptual choices, despite their greater contrast sensitivities. If, however, choice probabilities were calculated from interspike intervals instead of spike counts (thus taking into account the higher firing rates of OFF neurons), OFF neurons but not ON neurons were significantly correlated with behavioral choices. Taken together, these results demonstrate in awake, behaving animals that: 1) the ON and OFF pathways do not simply mirror each other in their functionality but instead carry qualitatively different types of information, and 2) the responses of ON and OFF neurons can be correlated with perceptual choices even in the absence of physical stimuli and interneuronal correlations.

Indirect activation elicits strong correlations between light and electrical responses in ON but not OFF retinal ganglion cells

KEY POINTS

To improve the quality of vision elicited by retinal prosthetics, elicited neural activity should resemble physiological signalling patterns; here, we hypothesized that electric stimulation that activates the synaptic circuitry of the retina would lead to closer matches than that which activates ganglion cells directly. We evaluated this hypothesis by comparing light and electrical responses in different types of ganglion cells. In contrast to the similarity in their light responses, electrical responses in ON and OFF cells of the same type were quite distinct. Further, electrical and light responses in the same cell were much better correlated in ON vs. OFF ganglion cells. Stimuli that activated photoreceptors yielded better correlations than those which activated bipolar cells. Our results suggest that the closer match to physiology in the ON signal transmitted to the brain may help to explain preferential reports of 'bright' phosphenes during earlier clinical trials.

ABSTRACT

To improve the efficacy of microelectronic retinal prosthetics it will be necessary to better understand the response of retinal neurons to electric stimulation. While stimulation that directly activates ganglion cells generally has the lowest threshold, the similarity in responsiveness across cells makes it extremely difficult for such an approach to re-create cell-type specific patterns of neural activity that arise normally in the healthy retina. In contrast, stimulation that activates neurons presynaptic to ganglion cells utilizes at least some of the existing retinal circuitry and therefore is thought to produce neural activity that better matches physiological signalling. Surprisingly, the actual benefit(s) of this approach remain unsubstantiated. Here, we recorded from ganglion cells in the rabbit retinal explant in response to electrical stimuli that activated the network. Targeted cells were first classified into known types via light responses so that the consistency of electrical responses within individual types could be evaluated. Both transient and sustained ON ganglion cells exhibited highly consistent electrical response patterns which were distinct from one another. Further, properties of the response (interspike interval, latency, peak firing rate, and spike count) in a given cell were well correlated to the corresponding properties of the light response for that same cell. Electric responses in OFF ganglion cells formed two groups, distinct from ON groups, and the correlation levels between electric and light responses were much weaker. The closer match in ON pathway responses may help to explain some preferential reporting of bright stimuli during psychophysical testing.

Predicting cortical dark/bright asymmetries from natural image statistics and early visual transforms

The nervous system has evolved in an environment with structure and predictability. One of the ubiquitous principles of sensory systems is the creation of circuits that capitalize on this predictability. Previous work has identified predictable non-uniformities in the distributions of basic visual features in natural images that are relevant to the encoding tasks of the visual system. Here, we report that the well-established statistical distributions of visual features -- such as visual contrast, spatial scale, and depth -- differ between bright and dark image components. Following this analysis, we go on to trace how these differences in natural images translate into different patterns of cortical input that arise from the separate bright (ON) and dark (OFF) pathways originating in the retina. We use models of these early visual pathways to transform natural images into statistical patterns of cortical input. The models include the receptive fields and non-linear response properties of the magnocellular (M) and parvocellular (P) pathways, with their ON and OFF pathway divisions. The results indicate that there are regularities in visual cortical input beyond those that have previously been appreciated from the direct analysis of natural images. In particular, several dark/bright asymmetries provide a potential account for recently discovered asymmetries in how the brain processes visual features, such as violations of classic energy-type models. On the basis of our analysis, we expect that the dark/bright dichotomy in natural images plays a key role in the generation of both cortical and perceptual asymmetries.

Sensory systems must contend with a tremendous amount of diversity in the natural world. Gaining a detailed description of the natural world’s statistical regularities is a critical part of understanding how the nervous system is adapted to its environment. Here, we report that the well-established statistical distributions of basic visual features—such as visual contrast and spatial scale—diverge when separated into bright and dark components. Operations such as dark/bright segregation are key features of early visual pathways. By modeling these pathways, we demonstrate that the dark and bright visual patterns driving cortical networks are asymmetric across a number of visual features, producing previously unappreciated second-order regularities. The results provide a parsimonious account for recently discovered asymmetries in cortical activity.

On-Off asymmetry in the perception of blur

Natural images appear blurred when imperfect lens focus reduces contrast energy at higher spatial frequencies. Here, we present evidence that perceived blur also depends on asymmetries between On (positive contrast polarities) and Off (negative contrast polarities) image signals. Psychophysical matching experiments involving natural and artificial stimuli suggest that attenuating Off signals at high spatial frequencies results in increased perceptual blur relative to similar attenuations of On signals. Results support the notion that Off image signals play an important role in blur perception.

Primate area V1: largest response gain for receptive fields in the straight-ahead direction

Although neuronal responses in behaving monkeys are typically studied while the monkey fixates straight ahead, it is known that eye position modulates responses of visual neurons. The modulation has been found to enhance neuronal responses when the receptive field is placed in the straight-ahead position for neurons receiving input from the peripheral but not the central retina. We studied the effect of eye position on the responses of V1 complex cells receiving input from the central retina (1.1-5.7° eccentricity) while minimizing the effect of fixational eye movements. Contrast response functions were obtained separately with drifting light and dark bars. Data were fit with the Naka-Rushton equation: r(c)=Rmax×c/(c+c50)+s, where r(c) is mean spike rate at contrast c, Rmax is the maximum response, c50 is the contrast that elicits half of Rmax, and s is the spontaneous activity. Contrast sensitivity as measured by c50 was not affected by eye position. For dark bars, there was a statistically significant decline in the normalized Rmax with increasing deviation from straight ahead. Data for bright bars showed a similar trend with a less rapid decline. Our results indicate that neurons representing the central retina show a bias for the straight-ahead position resulting from modulation of the response gain without an accompanying modulation of contrast sensitivity. The modulation is especially obvious for dark stimuli, which might be useful for directing attention to hazardous situations such as dark holes or shadows concealing important objects (Supplement 1: Video Abstract, Supplemental digital content 1, http://links.lww.com/WNR/A295).

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.

Toward functional classification of neuronal types

How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological, or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here, theoretical arguments are outlined for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species.

Variance predicts salience in central sensory processing

Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime--when sampling limitations constrain performance--efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.

Benefits of pathway splitting in sensory coding

In many sensory systems, the neural signal splits into multiple parallel pathways. For example, in the mammalian retina, ~20 types of retinal ganglion cells transmit information about the visual scene to the brain. The purpose of this profuse and early pathway splitting remains unknown. We examine a common instance of splitting into ON and OFF neurons excited by increments and decrements of light intensity in the visual scene, respectively. We test the hypothesis that pathway splitting enables more efficient encoding of sensory stimuli. Specifically, we compare a model system with an ON and an OFF neuron to one with two ON neurons. Surprisingly, the optimal ON-OFF system transmits the same information as the optimal ON-ON system, if one constrains the maximal firing rate of the neurons. However, the ON-OFF system uses fewer spikes on average to transmit this information. This superiority of the ON-OFF system is also observed when the two systems are optimized while constraining their mean firing rate. The efficiency gain for the ON-OFF split is comparable with that derived from decorrelation, a well known processing strategy of early sensory systems. The gain can be orders of magnitude larger when the ecologically important stimuli are rare but large events of either polarity. The ON-OFF system also provides a better code for extracting information by a linear downstream decoder. The results suggest that the evolution of ON-OFF diversification in sensory systems may be driven by the benefits of lowering average metabolic cost, especially in a world in which the relevant stimuli are sparse.

Dynamic integration of information about salience and value for smooth pursuit eye movements

Eye movement behavior can be determined by bottom-up factors like visual salience and by top-down factors like expected value. These different types of signals have to be combined for the control of eye movements. In this study we investigated how smooth pursuit eye movements integrate salience and value information. Observers were asked to track a random-dot kinematogram containing two coherent motion directions. To manipulate salience, the coherence or the density of one of the motion signals was varied. To manipulate value, observers won or lost money in a separate experiment if they were tracking one or the other motion direction. Our results show that pursuit direction was initially determined only by salience. 300-400 ms after target motion onset, pursuit steered towards the rewarded direction and the salience effects disappeared. The time course of this effect depended crucially on the difficulty to segment the two signal directions. These results indicate that salience determines early pursuit responses in the same way as saccades with short latencies. Value information is processed slower and dominates pursuit after several 100 ms.

Perceived depth in natural images reflects encoding of low-level luminance statistics

Sighted animals must survive in an environment that is diverse yet highly structured. Neural-coding models predict that the visual system should allocate its computational resources to exploit regularities in the environment, and that this allocation should facilitate perceptual judgments. Here we use three approaches (natural scenes statistical analysis, a reanalysis of single-unit data from alert behaving macaque, and a behavioral experiment in humans) to address the question of how the visual system maximizes behavioral success by taking advantage of low-level regularities in the environment. An analysis of natural scene statistics reveals that the probability distributions for light increments and decrements are biased in a way that could be exploited by the visual system to estimate depth from relative luminance. A reanalysis of neurophysiology data from Samonds et al. (2012) shows that the previously reported joint tuning of V1 cells for relative luminance and binocular disparity is well matched to a predicted distribution of binocular disparities produced by natural scenes. Finally, we show that a percept of added depth can be elicited in images by exaggerating the correlation between luminance and depth. Together, the results from these three approaches provide further evidence that the visual system allocates its processing resources in a way that is driven by the statistics of the natural environment.

Smaller pupil size and better proofreading performance with positive than with negative polarity displays

The 'positive polarity advantage' describes the fact that reading performance is better for dark text on light background (positive polarity) than for light text on dark background (negative polarity). We investigated the underlying mechanism by assessing pupil size and proofreading performance when reading positive and negative polarity texts. In particular, we tested the display luminance hypothesis which postulates that the typically greater brightness of positive compared to negative polarity displays leads to smaller pupil sizes and, hence, a sharper retinal image and better perception of detail. Indeed, pupil sizes were smaller and proofreading performance was better with positive than with negative polarity displays. The results are compatible with the hypothesis that the positive polarity advantage is an effect of display luminance. Limitations of the study are being discussed.

Refractory sampling links efficiency and costs of sensory encoding to stimulus statistics

Sensory neurons integrate information about the world, adapting their sampling to its changes. However, little is understood mechanistically how this primary encoding process, which ultimately limits perception, depends upon stimulus statistics. Here, we analyze this open question systematically by using intracellular recordings from fly (Drosophila melanogaster and Coenosia attenuata) photoreceptors and corresponding stochastic simulations from biophysically realistic photoreceptor models. Recordings show that photoreceptors can sample more information from naturalistic light intensity time series (NS) than from Gaussian white-noise (GWN), shuffled-NS or Gaussian-1/f stimuli; integrating larger responses with higher signal-to-noise ratio and encoding efficiency to large bursty contrast changes. Simulations reveal how a photoreceptor's information capture depends critically upon the stochastic refractoriness of its 30,000 sampling units (microvilli). In daylight, refractoriness sacrifices sensitivity to enhance intensity changes in neural image representations, with more and faster microvilli improving encoding. But for GWN and other stimuli, which lack longer dark contrasts of real-world intensity changes that reduce microvilli refractoriness, these performance gains are submaximal and energetically costly. These results provide mechanistic reasons why information sampling is more efficient for natural/naturalistic stimulation and novel insight into the operation, design, and evolution of signaling and code in sensory neurons.

Grid cells and cortical representation

One of the grand challenges in neuroscience is to comprehend neural computation in the association cortices, the parts of the cortex that have shown the largest expansion and differentiation during mammalian evolution and that are thought to contribute profoundly to the emergence of advanced cognition in humans. In this Review, we use grid cells in the medial entorhinal cortex as a gateway to understand network computation at a stage of cortical processing in which firing patterns are shaped not primarily by incoming sensory signals but to a large extent by the intrinsic properties of the local circuit.

Neuronal and perceptual differences in the temporal processing of darks and lights

Visual information is mediated by two major thalamic pathways that signal light decrements (OFF) and increments (ON) in visual scenes, the OFF pathway being faster than the ON. Here, we demonstrate that this OFF temporal advantage is transferred to visual cortex and has a correlate in human perception. OFF-dominated cortical neurons in cats responded ∼3 ms faster to visual stimuli than ON-dominated cortical neurons, and dark-mediated suppression in ON-dominated neurons peaked ∼14 ms faster than light-mediated suppression in OFF-dominated neurons. Consistent with the neuronal differences, human observers were 6-14 ms faster at detecting darks than lights and better at discriminating dark than light flickers. Neuronal and perceptual differences both vanished if backgrounds were biased toward darks. Our results suggest that the cortical OFF pathway is faster than the ON pathway at increasing and suppressing visual responses, and these differences have parallels in the human visual perception of lights and darks.

Neuronal nonlinearity explains greater visual spatial resolution for darks than lights

Astronomers and physicists noticed centuries ago that visual spatial resolution is higher for dark than light stimuli, but the neuronal mechanisms for this perceptual asymmetry remain unknown. Here we demonstrate that the asymmetry is caused by a neuronal nonlinearity in the early visual pathway. We show that neurons driven by darks (OFF neurons) increase their responses roughly linearly with luminance decrements, independent of the background luminance. However, neurons driven by lights (ON neurons) saturate their responses with small increases in luminance and need bright backgrounds to approach the linearity of OFF neurons. We show that, as a consequence of this difference in linearity, receptive fields are larger in ON than OFF thalamic neurons, and cortical neurons are more strongly driven by darks than lights at low spatial frequencies. This ON/OFF asymmetry in linearity could be demonstrated in the visual cortex of cats, monkeys, and humans and in the cat visual thalamus. Furthermore, in the cat visual thalamus, we show that the neuronal nonlinearity is present at the ON receptive field center of ON-center neurons and ON receptive field surround of OFF-center neurons, suggesting an origin at the level of the photoreceptor. These results demonstrate a fundamental difference in visual processing between ON and OFF channels and reveal a competitive advantage for OFF neurons over ON neurons at low spatial frequencies, which could be important during cortical development when retinal images are blurred by immature optics in infant eyes.

Flies and humans share a motion estimation strategy that exploits natural scene statistics

Sighted animals extract motion information from visual scenes by processing spatiotemporal patterns of light falling on the retina. The dominant models for motion estimation exploit intensity correlations only between pairs of points in space and time. Moving natural scenes, however, contain more complex correlations. We found that fly and human visual systems encode the combined direction and contrast polarity of moving edges using triple correlations that enhance motion estimation in natural environments. Both species extracted triple correlations with neural substrates tuned for light or dark edges, and sensitivity to specific triple correlations was retained even as light and dark edge motion signals were combined. Thus, both species separately process light and dark image contrasts to capture motion signatures that can improve estimation accuracy. This convergence argues that statistical structures in natural scenes have greatly affected visual processing, driving a common computational strategy over 500 million years of evolution.

Adaptation to changes in higher-order stimulus statistics in the salamander retina

Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution.

Cortical brightness adaptation when darkness and brightness produce different dynamical states in the visual cortex

Darkness and brightness are very different perceptually. To understand the neural basis for the visual difference, we studied the dynamical states of populations of neurons in macaque primary visual cortex when a spatially uniform area (8° × 8°) of the visual field alternated between black and white. Darkness evoked sustained nerve-impulse spiking in primary visual cortex neurons, but bright stimuli evoked only a transient response. A peak in the local field potential (LFP) γ band (30-80 Hz) occurred during darkness; white-induced LFP fluctuations were of lower amplitude, peaking at 25 Hz. However, the sustained response to white in the evoked LFP was larger than for black. Together with the results on spiking, the LFP results imply that, throughout the stimulus period, bright fields evoked strong net sustained inhibition. Such cortical brightness adaptation can explain many perceptual phenomena: interocular speeding up of dark adaptation, tonic interocular suppression, and interocular masking.

Information theory of adaptation in neurons, behavior, and mood

The ability to make accurate predictions of future stimuli and consequences of one's actions are crucial for the survival and appropriate decision-making. These predictions are constantly being made at different levels of the nervous system. This is evidenced by adaptation to stimulus parameters in sensory coding, and in learning of an up-to-date model of the environment at the behavioral level. This review will discuss recent findings that actions of neurons and animals are selected based on detailed stimulus history in such a way as to maximize information for achieving the task at hand. Information maximization dictates not only how sensory coding should adapt to various statistical aspects of stimuli, but also that reward function should adapt to match the predictive information from past to future.

Transformation of stimulus correlations by the retina

Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.

An influential theory of early sensory processing argues that sensory circuits should conserve scarce resources in their outputs by reducing correlations present in their inputs. Measuring simultaneous responses from large numbers of retinal ganglion cells responding to widely different classes of visual stimuli, we find that output correlations increase when we present stimuli with spatial, but not temporal, correlations. On the other hand, we find evidence that retina adjusts to spatio-temporal structure so that retinal output correlations change less than input correlations would predict. Changes in the receptive field properties of individual cells, along with gain changes, largely explain this relative constancy of correlations over the population.

Olfactory stimulation selectively modulates the OFF pathway in the retina of zebrafish

Cross-modal regulation of visual performance by olfactory stimuli begins in the retina, where dopaminergic interneurons receive projections from the olfactory bulb. However, we do not understand how olfactory stimuli alter the processing of visual signals within the retina. We investigated this question by in vivo imaging activity in transgenic zebrafish expressing SyGCaMP2 in bipolar cell terminals and GCaMP3.5 in ganglion cells. The food-related amino acid methionine reduced the gain and increased sensitivity of responses to luminance and contrast transmitted through OFF bipolar cells but not ON. The effects of olfactory stimulus were blocked by inhibiting dopamine uptake and release. Activation of dopamine receptors increased the gain of synaptic transmission in vivo and potentiated synaptic calcium currents in isolated bipolar cells. These results indicate that olfactory stimuli alter the sensitivity of the retina through the dopaminergic regulation of presynaptic calcium channels that control the gain of synaptic transmission through OFF bipolar cells.

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Olfactory stimuli regulate transmission of signals through retinal bipolar cells

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Modulation of synaptic gain and sensitivity occur in OFF bipolar cells but not ON

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An inhibitor of dopamine uptake blocks odor-induced changes in synaptic gain

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Dopamine potentiates presynaptic calcium channels in isolated bipolar cells

Novel flicker-sensitive visual circuit neurons inhibited by stationary patterns

Many animals use visual motion cues for navigating within their surroundings. Both flies and vertebrates compute local motion by temporal correlation of neighboring photoreceptors, via so-called elementary motion detectors (EMDs). In the fly lobula plate and the vertebrate visual cortex the output from many EMDs is pooled in neurons sensitive to wide-field optic flow. Although the EMD has been the preferred model for more than 50 years, recent work has highlighted its limitations in describing some visual behaviors, such as responses to higher-order motion stimuli. Non-EMD motion processing may therefore serve an important function in vision. Here, we describe a novel neuron class in the fly lobula plate that clearly does not derive its input from classic EMDs. The centrifugal stationary inhibited flicker excited (cSIFE) neuron is strongly excited by flicker, up to very high temporal frequencies. The non-EMD driven flicker sensitivity leads to strong, nondirectional responses to high-speed, wide-field motion. Furthermore, cSIFE is strongly inhibited by stationary patterns, within a narrow wavelength band. cSIFE's outputs overlap with the inputs of well described optic flow-sensitive lobula plate tangential cells (LPTCs). Driving cSIFE affects the active dendrites of LPTCs, and cSIFE may therefore play a large role in motion vision.

Synaptic mechanisms of adaptation and sensitization in the retina

Sensory systems continually adjust the way stimuli are processed. What are the circuit mechanisms underlying this plasticity? We investigated how synapses in the retina of zebrafish adjust to changes in the temporal contrast of a visual stimulus by imaging activity in vivo. Following an increase in contrast, bipolar cell synapses with strong initial responses depressed, whereas synapses with weak initial responses facilitated. Depression and facilitation predominated in different strata of the inner retina, where bipolar cell output was anticorrelated with the activity of amacrine cell synapses providing inhibitory feedback. Pharmacological block of GABAergic feedback converted facilitating bipolar cell synapses into depressing ones. These results indicate that depression intrinsic to bipolar cell synapses causes adaptation of the ganglion cell response to contrast, whereas depression in amacrine cell synapses causes sensitization. Distinct microcircuits segregating to different layers of the retina can cause simultaneous increases or decreases in the gain of neural responses.

Efficient coding of spatial information in the primate retina

Sensory neurons have been hypothesized to efficiently encode signals from the natural environment subject to resource constraints. The predictions of this efficient coding hypothesis regarding the spatial filtering properties of the visual system have been found consistent with human perception, but they have not been compared directly with neural responses. Here, we analyze the information that retinal ganglion cells transmit to the brain about the spatial information in natural images subject to three resource constraints: the number of retinal ganglion cells, their total response variances, and their total synaptic strengths. We derive a model that optimizes the transmitted information and compare it directly with measurements of complete functional connectivity between cone photoreceptors and the four major types of ganglion cells in the primate retina, obtained at single-cell resolution. We find that the ganglion cell population exhibited 80% efficiency in transmitting spatial information relative to the model. Both the retina and the model exhibited high redundancy (~30%) among ganglion cells of the same cell type. A novel and unique prediction of efficient coding, the relationships between projection patterns of individual cones to all ganglion cells, was consistent with the observed projection patterns in the retina. These results indicate a high level of efficiency with near-optimal redundancy in visual signaling by the retina.

The neuronal organization of the retina

The mammalian retina consists of neurons of >60 distinct types, each playing a specific role in processing visual images. They are arranged in three main stages. The first decomposes the outputs of the rod and cone photoreceptors into ∼12 parallel information streams. The second connects these streams to specific types of retinal ganglion cells. The third combines bipolar and amacrine cell activity to create the diverse encodings of the visual world--roughly 20 of them--that the retina transmits to the brain. New transformations of the visual input continue to be found: at least half of the encodings sent to the brain (ganglion cell response selectivities) remain to be discovered. This diversity of the retina's outputs has yet to be incorporated into our understanding of higher visual function.

Relative luminance and binocular disparity preferences are correlated in macaque primary visual cortex, matching natural scene statistics

Humans excel at inferring information about 3D scenes from their 2D images projected on the retinas, using a wide range of depth cues. One example of such inference is the tendency for observers to perceive lighter image regions as closer. This psychophysical behavior could have an ecological basis because nearer regions tend to be lighter in natural 3D scenes. Here, we show that an analogous association exists between the relative luminance and binocular disparity preferences of neurons in macaque primary visual cortex. The joint coding of relative luminance and binocular disparity at the neuronal population level may be an integral part of the neural mechanisms for perceptual inference of depth from images.

Faster thalamocortical processing for dark than light visual targets

ON and OFF visual pathways originate in the retina at the synapse between photoreceptor and bipolar cells. OFF bipolar cells are shorter in length and use receptors with faster kinetics than ON bipolar cells and, therefore, process information faster. Here, we demonstrate that this temporal advantage is maintained through thalamocortical processing, with OFF visual responses reaching cortex ~3-6 ms before ON visual responses. Faster OFF visual responses could be demonstrated in recordings from large populations of cat thalamic neurons representing the center of vision (both X and Y) and from subpopulations making connection with the same cortical orientation column. While the OFF temporal advantage diminished as visual responses reached their peak, the integral of the impulse response was greater in OFF than ON neurons. Given the stimulus preferences from OFF and ON channels, our results indicate that darks are processed faster than lights in the thalamocortical pathway.

Neural population densities shape network correlations

The way sensory microcircuits manage cellular response correlations is a crucial question in understanding how such systems integrate external stimuli and encode information. Most sensory systems exhibit heterogeneities in terms of population sizes and features, which all impact their dynamics. This work addresses how correlations between the dynamics of neural ensembles depend on the relative size or density of excitatory and inhibitory populations. To do so, we study an apparently symmetric system of coupled stochastic differential equations that model the evolution of the populations' activities. Excitatory and inhibitory populations are connected by reciprocal recurrent connections, and both receive different stimuli exhibiting a certain level of correlation with each other. A stability analysis is performed, which reveals an intrinsic asymmetry in the distribution of the fixed points with respect to the threshold of the nonlinearities. Based on this, we show how the cross correlation between the population responses depends on the density of the inhibitory population, and that a specific ratio between both population sizes leads to a state of zero correlation. We show that this so-called asynchronous state subsists, despite the presence of stimulus correlation, and most importantly, that it occurs only in asymmetrical systems where one population outnumbers the other. Using linear approximations, we derive analytical expressions for the root of the cross-correlation function and study how the asynchronous state is impacted by the model's parameters. This work suggests a possible explanation for why inhibitory cells outnumber excitatory cells in the visual system.

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human interaction plays a key role in our method; but effort is minimized and streamlined via a graphical interface. We illustrate our method on data from guinea pig retinal ganglion cells and document its performance on simulated data consisting of spikes added to experimentally measured background noise. We present several tests demonstrating that the algorithm is highly accurate: it exhibits low error rates on fits to synthetic data, low refractory violation rates, good receptive field coverage, and consistency across users.

Natural images from the birthplace of the human eye

Here we introduce a database of calibrated natural images publicly available through an easy-to-use web interface. Using a Nikon D70 digital SLR camera, we acquired about six-megapixel images of Okavango Delta of Botswana, a tropical savanna habitat similar to where the human eye is thought to have evolved. Some sequences of images were captured unsystematically while following a baboon troop, while others were designed to vary a single parameter such as aperture, object distance, time of day or position on the horizon. Images are available in the raw RGB format and in grayscale. Images are also available in units relevant to the physiology of human cone photoreceptors, where pixel values represent the expected number of photoisomerizations per second for cones sensitive to long (L), medium (M) and short (S) wavelengths. This database is distributed under a Creative Commons Attribution-Noncommercial Unported license to facilitate research in computer vision, psychophysics of perception, and visual neuroscience.

Darks are processed faster than lights

Recent physiological studies claim that dark stimuli have access to greater neuronal resources than light stimuli in early visual pathway. We used two sets of novel stimuli to examine the functional consequences of this dark dominance in human observers. We show that increment and decrement thresholds are equal when controlled for adaptation and eye movements. However, measurements for salience differences at high contrasts show that darks are detected pronouncedly faster and more accurately than lights when presented against uniform binary noise. In addition, the salience advantage for darks is abolished when the background distribution is adjusted to control for the irradiation illusion. The threshold equality suggests that the highest sensitivities of neurons in the ON and OFF channels are similar, whereas the salience difference is consistent with a population advantage for the OFF system.

Information transmission in genetic regulatory networks: a review

Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'.

Illuminating synapses and circuitry in the retina

In the central nervous system, space is at a premium. This is especially true in the retina, where synapses, cells, and circuitry have evolved to maximize signal-processing capacity within a thin, optically transparent tissue. For example, at some retinal synapses, single presynaptic active zones contact multiple postsynaptic targets; some individual neurons perform completely different tasks depending on visual conditions, while others execute hundreds of circuit computations in parallel; and the retinal network adapts, at various levels, to the ever-changing visual world. Each of these features reflects efficient use of limited cellular resources to optimally encode visual information.