Widespread and Multifaceted Binocular Integration in the Mouse Primary Visual Cortex.

The brain combines two-dimensional images received from the two eyes to form a percept of three-dimensional surroundings. This process of binocular integration in the primary visual cortex (V1) serves as a useful model for studying how neural circuits generate emergent properties from multiple input signals. Here, we perform a thorough characterization of binocular integration using electrophysiological recordings in the V1 of awake adult male and female mice by systematically varying the orientation and phase disparity of monocular and binocular stimuli. We reveal widespread binocular integration in mouse V1 and demonstrate that the three commonly studied binocular properties-ocular dominance, interocular matching, and disparity selectivity-are independent of each other. For individual neurons, the responses to monocular stimulation can predict the average amplitude of binocular response but not its selectivity. Finally, the extensive and independent binocular integration of monocular inputs is seen across cortical layers in both regular-spiking and fast-spiking neurons, regardless of stimulus design. Our data indicate that the current model of simple feedforward convergence is inadequate to account for binocular integration in mouse V1, thus suggesting an indispensable role played by intracortical circuits in binocular computation.SIGNIFICANCE STATEMENT Binocular integration is an important step of visual processing that takes place in the visual cortex. Studying the process by which V1 neurons become selective for certain binocular disparities is informative about how neural circuits integrate multiple information streams at a more general level. Here, we systematically characterize binocular integration in mice. Our data demonstrate more widespread and complex binocular integration in mouse V1 than previously reported. Binocular responses cannot be explained by a simple convergence of monocular responses, contrary to the prevailing model of binocular integration. These findings thus indicate that intracortical circuits must be involved in the exquisite computation of binocular disparity, which would endow brain circuits with the plasticity needed for binocular development and processing.

Mapping epileptic directional brain networks using intracranial EEG data.

The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another as directional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. As such, effective epilepsy diagnosis and treatment require accurate identification of directional connections among regions, i.e., mapping of epileptic patients' brain networks. This article aims to understand the epileptic brain network using intracranial electroencephalographic data-recordings of epileptic patients' brain activities in many regions. The most popular models for directional connectivity use ordinary differential equations (ODE). However, ODE models are sensitive to data noise and computationally costly. To address these issues, we propose a high-dimensional state-space multivariate autoregression (SSMAR) model for the brain's directional connectivity. Different from standard multivariate autoregression and SSMAR models, the proposed SSMAR features a cluster structure, where the brain network consists of several clusters of densely connected brain regions. We develop an expectation-maximization algorithm to estimate the proposed model and use it to map the interregional networks of epileptic patients in different seizure stages. Our method reveals the evolution of brain networks during seizure development.

Decision Signals in the Local Field Potentials of Early and Mid-Level Macaque Visual Cortex.

The neural basis of perceptual decision making has typically been studied using measurements of single neuron activity, though decisions are likely based on the activity of large neuronal ensembles. Local field potentials (LFPs) may, in some cases, serve as a useful proxy for population activity and thus be useful for understanding the neural basis of perceptual decision making. However, little is known about whether LFPs in sensory areas include decision-related signals. We therefore analyzed LFPs recorded using two 48-electrode arrays implanted in primary visual cortex (V1) and area V4 of macaque monkeys trained to perform a fine orientation discrimination task. We found significant choice information in low (0-30 Hz) and higher (70-500 Hz) frequency components of the LFP, but little information in gamma frequencies (30-70 Hz). Choice information was more robust in V4 than V1 and stronger in LFPs than in simultaneously measured spiking activity. LFP-based choice information included a global component, common across electrodes within an area. Our findings reveal the presence of robust choice-related signals in the LFPs recorded in V1 and V4 and suggest that LFPs may be a useful complement to spike-based analyses of decision making.

Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History.

Our understanding of the neural basis of perceptual decision making has been built in part on relating co-fluctuations of single neuron responses to perceptual decisions on a trial-by-trial basis. The strength of this relationship is often compared across neurons or brain areas, recorded in different sessions, animals, or variants of a task. We sought to extend our understanding of perceptual decision making in three ways. First, we measured neuronal activity simultaneously in early [primary visual cortex (V1)] and midlevel (V4) visual cortex while macaque monkeys performed a fine orientation discrimination perceptual task. This allowed a direct comparison of choice signals in these two areas, including their dynamics. Second, we asked how our ability to predict animals' decisions would be improved by considering small simultaneously-recorded neuronal populations rather than individual units. Finally, we asked whether predictions would be improved by taking into account the animals' choice and reward histories, which can strongly influence decision making. We found that responses of individual V4 neurons were weakly predictive of decisions, but only in a brief epoch between stimulus offset and the indication of choice. In V1, few neurons showed significant decision-related activity. Analysis of neuronal population responses revealed robust choice-related information in V4 and substantially weaker signals in V1. Including choice- and reward-history information improved performance further, particularly when the recorded populations contained little decision-related information. Our work shows the power of using neuronal populations and decision history when relating neuronal responses to the perceptual decisions they are thought to underlie.SIGNIFICANCE STATEMENT Decades of research has provided a rich description of how visual information is represented in the visual cortex. Yet how cortical responses relate to visual perception remains poorly understood. Here we relate fluctuations in small neuronal population responses, recorded simultaneously in primary visual cortex (V1) and area V4 of monkeys, to perceptual reports in an orientation discrimination task. Choice-related signals were robust in V4, particularly late in the behavioral trial, but not in V1. Models that include both neuronal responses and choice-history information were able to predict a substantial portion of decisions. Our work shows the power of integrating information across neurons and including decision history in relating neuronal responses to perceptual decisions.

Delayed suppression shapes disparity selective responses in monkey V1.

The stereo correspondence problem poses a challenge to visual neurons because localized receptive fields potentially cause false responses. Neurons in the primary visual cortex (V1) partially resolve this problem by combining excitatory and suppressive responses to encode binocular disparity. We explored the time course of this combination in awake, monkey V1 neurons using subspace mapping of receptive fields. The stimulus was a binocular noise pattern constructed from discrete spatial frequency components. We forward correlated the firing of the V1 neuron with the occurrence of binocular presentations of each spatial frequency component. The forward correlation yielded a complete set of response time courses to every combination of spatial frequency and interocular phase difference. Some combinations produced suppressive responses. Typically, if an interocular phase difference for a given spatial frequency produced strong excitation, we saw suppression in response to the opposite interocular phase difference at lower spatial frequencies. The suppression was delayed relative to the excitation, with a median difference in latency of 7 ms. We found that the suppressive mechanism explains a well-known mismatch of monocular and binocular signals. The suppressive components increased power at low spatial frequencies in disparity tuning, whereas they reduced the monocular response to low spatial frequencies. This long-recognized mismatch of binocular and monocular signals reflects a suppressive mechanism that helps reduce the response to false matches.

Neural activity in cortical area V4 underlies fine disparity discrimination.

Primates are capable of discriminating depth with remarkable precision using binocular disparity. Neurons in area V4 are selective for relative disparity, which is the crucial visual cue for discrimination of fine disparity. Here, we investigated the contribution of V4 neurons to fine disparity discrimination. Monkeys discriminated whether the center disk of a dynamic random-dot stereogram was in front of or behind its surrounding annulus. We first behaviorally tested the reference frame of the disparity representation used for performing this task. After learning the task with a set of surround disparities, the monkey generalized its responses to untrained surround disparities, indicating that the perceptual decisions were generated from a disparity representation in a relative frame of reference. We then recorded single-unit responses from V4 while the monkeys performed the task. On average, neuronal thresholds were higher than the behavioral thresholds. The most sensitive neurons reached thresholds as low as the psychophysical thresholds. For subthreshold disparities, the monkeys made frequent errors. The variable decisions were predictable from the fluctuation in the neuronal responses. The predictions were based on a decision model in which each V4 neuron transmits the evidence for the disparity it prefers. We finally altered the disparity representation artificially by means of microstimulation to V4. The decisions were systematically biased when microstimulation boosted the V4 responses. The bias was toward the direction predicted from the decision model. We suggest that disparity signals carried by V4 neurons underlie precise discrimination of fine stereoscopic depth.

Neural circuits for binocular vision: Ocular dominance, interocular matching, and disparity selectivity.

The brain creates a single visual percept of the world with inputs from two eyes. This means that downstream structures must integrate information from the two eyes coherently. Not only does the brain meet this challenge effortlessly, it also uses small differences between the two eyes' inputs, i.e., binocular disparity, to construct depth information in a perceptual process called stereopsis. Recent studies have advanced our understanding of the neural circuits underlying stereoscopic vision and its development. Here, we review these advances in the context of three binocular properties that have been most commonly studied for visual cortical neurons: ocular dominance of response magnitude, interocular matching of orientation preference, and response selectivity for binocular disparity. By focusing mostly on mouse studies, as well as recent studies using ferrets and tree shrews, we highlight unresolved controversies and significant knowledge gaps regarding the neural circuits underlying binocular vision. We note that in most ocular dominance studies, only monocular stimulations are used, which could lead to a mischaracterization of binocularity. On the other hand, much remains unknown regarding the circuit basis of interocular matching and disparity selectivity and its development. We conclude by outlining opportunities for future studies on the neural circuits and functional development of binocular integration in the early visual system.

Suppressive mechanisms in monkey V1 help to solve the stereo correspondence problem.

Neurons encode the depth in stereoscopic images by combining the signals from the receptive fields in the two eyes. Local variations in single images can activate neurons that do not signal the correct disparity (false matches), giving rise to the stereo correspondence problem. We used binocular white-noise stimuli to decompose the responses of monkey primary visual cortex V1 neurons into the elements of a linear-nonlinear model (via spike-triggered covariance analysis). In our population of disparity-selective neurons, we find both excitatory and suppressive elements in many of the neurons. Their binocular receptive fields were aligned in a specific push-pull manner for disparity. We demonstrate that this arrangement reduces the responses to false matches but preserves the responses to true matches. The responses of the cells to the noise stimuli were well explained by a linear summation of the elements, followed by a nonlinearity. This model also explained the shape of independently measured disparity-tuning curves, although it overestimated the response magnitude. This study constitutes the first direct physiological evidence for the contribution of suppressive mechanisms to disparity selectivity. This new mechanism contributes to solving the stereo correspondence problem.

Coarse-to-fine processing drives the efficient coding of natural scenes in mouse visual cortex.

The visual system processes sensory inputs sequentially, perceiving coarse information before fine details. Here we study the neural basis of coarse-to-fine processing and its computational benefits in natural vision. We find that primary visual cortical neurons in awake mice respond to natural scenes in a coarse-to-fine manner, primarily driven by individual neurons rapidly shifting their spatial frequency preference from low to high over a brief response period. This shift transforms the population response in a way that counteracts the statistical regularities of natural scenes, thereby reducing redundancy and generating a more efficient neural representation. The increase in representational efficiency does not occur in either dark-reared or anesthetized mice, which show significantly attenuated coarse-to-fine spatial processing. Collectively, these results illustrate that coarse-to-fine processing is state dependent, develops postnatally via visual experience, and provides a computational advantage by generating more efficient representations of the complex spatial statistics of ethologically relevant natural scenes.

Strong tuning for stereoscopic depth indicates orientation-specific recurrent circuitry in tree shrew V1.

Neurons in the primary visual cortex (V1) are tuned to specific disparities between the two retinal images, which form the neural substrate for stereoscopic vision. We show that V1 neurons in tree shrews, but not in mice, display highly selective responses to narrow ranges of disparity in random-dot stereograms. Surprisingly, V1 neurons in both species show similarly strong tuning to gratings of varying interocular phase differences. This stimulus-dependent dissociation of disparity tuning can be explained by a network model that combines both feedforward and recurrent connections. The features of the model connections are supported by cortical organizations specific to each species. We validate this model by identifying putative inhibitory neurons and confirming their predicted disparity tuning in both species. Together, our studies establish a foundation for using tree shrews in studying binocular vision and raise an exciting possibility of how cortical columns could be uniquely important in computing stereoscopic depth.

Matching and correlation computations in stereoscopic depth perception.

A fundamental task of the visual system is to infer depth by using binocular disparity. To encode binocular disparity, the visual cortex performs two distinct computations: one detects matched patterns in paired images (matching computation); the other constructs the cross-correlation between the images (correlation computation). How the two computations are used in stereoscopic perception is unclear. We dissociated their contributions in near/far discrimination by varying the magnitude of the disparity across separate sessions. For small disparity (0.03°), subjects performed at chance level to a binocularly opposite-contrast (anti-correlated) random-dot stereogram (RDS) but improved their performance with the proportion of contrast-matched (correlated) dots. For large disparity (0.48°), the direction of perceived depth reversed with an anti-correlated RDS relative to that for a correlated one. Neither reversed nor normal depth was perceived when anti-correlation was applied to half of the dots. We explain the decision process as a weighted average of the two computations, with the relative weight of the correlation computation increasing with the disparity magnitude. We conclude that matching computation dominates fine depth perception, while both computations contribute to coarser depth perception. Thus, stereoscopic depth perception recruits different computations depending on the disparity magnitude.

Motion Streak Neurons in the Mouse Visual Cortex.

Motion streaks are smeared representation of fast-moving objects due to temporal integration. Here, we test for motion streak signals in mice with two-photon calcium imaging. For small dots moving at low speeds, neurons in primary visual cortex (V1) encode the component motion, with preferred direction along the axis perpendicular to their preferred orientation. At high speeds, V1 neurons prefer the direction along the axis parallel to their preferred orientation, as expected for encoding motion streaks. Whereas some V1 neurons (∼20%) display a switch of preferred motion axis with increasing speed, others (>40%) respond specifically to high speeds at the parallel axis. Motion streak neurons are also seen in higher visual lateromedial (LM), anterolateral (AL), and rostrolateral (RL) areas, but with higher transition speeds, and many still prefer the perpendicular axis even with fast motion. Our results thus indicate that diverse motion encoding exists in mouse visual cortex, with intriguing differences among visual areas.

Two Is Greater Than One: Binocular Visual Experience Drives Cortical Orientation Map Alignment.

One hallmark of the mature visual cortex is binocularly matched orientation maps. In this issue of Neuron, Chang et al. (2020) show that three different maps exist at vision onset and that binocular visual experience aligns them into a single unified representation.

Mechanisms underlying the transformation of disparity signals from V1 to V2 in the macaque.

Stereo vision relies on cortical signals that encode binocular disparity. In V1, the disparity energy model explains many features of binocular interaction, but it overestimates the responses to anticorrelated images. Combining the outputs of two, or more, energy model-like subunits [two-subunit (2SU) model] can resolve this discrepancy and provides an alternative explanation for disparity signals previously thought to indicate phase disparity between the receptive fields (RFs) of each eye. The 2SU model naturally explains how "near/far" (odd-symmetric) tuning becomes dominant in extrastriate cortex. To compare the energy and the 2SU models, we used a broadband compound grating and applied a common interocular phase difference to all spatial frequency components (a stimulus phase disparity), combined with a common spatial displacement (a stimulus position disparity). This produces binocular images that never occur in natural viewing, for which the 2SU model and the energy model make distinctively different predictions. Responses of neurons recorded from both V1 and V2 of awake rhesus macaques systematically deviated from the predictions of the energy model, in accordance with the 2SU model. These deviations correlated with the symmetry of the tuning curve, indicating that the 2SU mechanism is exploited to produce odd symmetry. Nonetheless, individual subunits also contain RF phase disparity that contributes to odd symmetry. The results suggest that neurons in V2 probably inherit phase disparity signals from V1 neurons, but systematically combine input from V1 neurons with different position disparities, in a way that elaborates odd-symmetric tuning and extends the range of disparities encoded by single neurons.

Disparity-energy signals in perceived stereoscopic depth.

Stereopsis, the ability to sense the world in three dimensions (3D) from pairs of retinal images, functions when both images have corresponding elements. When observers view stereograms lacking a global match, they do not perceive 3D structure, whereas several cortical areas encode stereoscopic depth in the disparity energy. Whether these neural representations are exploited or ignored in perceptual decisions remains elusive. By combining contrast-reversal and delay between stereo images, we found that disparity-energy signals mediate the reversal of stereoscopic depth judgments. A crisp, adjacent plane of reference was crucial for the signal to be used in the judgments. Disparity discrimination relies on the disparity-energy signal when the stimulus has no global binocular match and is accompanied by a fixed surface of reference.

Neural correlates of fine depth discrimination in monkey inferior temporal cortex.

Binocular disparity is an important visual cue that gives rise to the perception of depth. Disparity signals are widely spread across the visual cortex, but their relative role is poorly understood. Here, we addressed the correlation between the responses of disparity-selective neurons in the occipitotemporal (ventral) visual pathway and the behavioral discrimination of stereoscopic depth. We recorded activity of disparity-selective neurons in the inferior temporal cortex (IT) while monkeys were engaged in a fine stereoscopic depth discrimination (stereoacuity) task. We found that trial-to-trial fluctuations in neuronal responses correlated with the monkey's perceptual choice. We suggest that disparity signals in the IT, located in the ventral visual pathway, are functionally linked to the discrimination of fine-grain depth.

Disparity processing in primary visual cortex.

The first step in binocular stereopsis is to match features on the left retina with the correct features on the right retina, discarding 'false' matches. The physiological processing of these signals starts in the primary visual cortex, where the binocular energy model has been a powerful framework for understanding the underlying computation. For this reason, it is often used when thinking about how binocular matching might be performed beyond striate cortex. But this step depends critically on the accuracy of the model, and real V1 neurons show several properties that suggest they may be less sensitive to false matches than the energy model predicts. Several recent studies provide empirical support for an extended version of the energy model, in which the same principles are used, but the responses of single neurons are described as the sum of several subunits, each of which follows the principles of the energy model. These studies have significantly improved our understanding of the role played by striate cortex in the stereo correspondence problem.This article is part of the themed issue 'Vision in our three-dimensional world'.

Multiplicative and Additive Modulation of Neuronal Tuning with Population Activity Affects Encoded Information.

Numerous studies have shown that neuronal responses are modulated by stimulus properties and also by the state of the local network. However, little is known about how activity fluctuations of neuronal populations modulate the sensory tuning of cells and affect their encoded information. We found that fluctuations in ongoing and stimulus-evoked population activity in primate visual cortex modulate the tuning of neurons in a multiplicative and additive manner. While distributed on a continuum, neurons with stronger multiplicative effects tended to have less additive modulation and vice versa. The information encoded by multiplicatively modulated neurons increased with greater population activity, while that of additively modulated neurons decreased. These effects offset each other so that population activity had little effect on total information. Our results thus suggest that intrinsic activity fluctuations may act as a "traffic light" that determines which subset of neurons is most informative.

Rejection of false matches for binocular correspondence in macaque visual cortical area V4.

A plane lying in depth is vividly perceived by viewing a random-dot stereogram (RDS) with a slight binocular disparity. Perception of a plane-in-depth is lost by reversing the contrast of dots seen by one of the eyes to generate an anticorrelated RDS. From a computational perspective, the visual system cannot find a globally consistent solution for matching the left and right eye images of an anticorrelated RDS. The neural representation of a global match should therefore be insensitive to binocular disparity in an anticorrelated RDS. Most neurons in the striate cortex (V1) respond to binocular disparity in anticorrelated RDSs, suggesting that further cortical processing in extrastriate areas is necessary to fully account for the matching computation. We examined neural responses to dynamic RDSs, both normal (correlated) and anticorrelated, in area V4 of the monkey visual cortex. More than half of the V4 cells were sensitive to the horizontal disparity embedded in a correlated RDS. Most of them greatly attenuated their selectivity for disparity when the RDS was anticorrelated. This attenuation was apparent from the response onset, and the degree of attenuation did not correlate with neuronal response latencies. Unlike the disparity tuning of V1 neurons to anticorrelated RDSs, that of V4 neurons was not an inversion of tuning to normal RDSs. Our results suggest that responses to false matches between contrast-reversed dots in the left and right eye images elicited in V1 are substantially reduced by the stage of V4.

Population codes in the visual cortex.

Every sensory event elicits activity in a broad population of cells that is distributed within and across cortical areas. How these neurons function together to represent the sensory environment is a major question in systems neuroscience. A number of proposals have been made, and recent advances in multi-neuronal recording have begun to allow researchers to test the predictions of these population-coding theories. In this review, I provide an introduction to some of the key concepts in population coding and describe several studies in the recent literature. The focus of this review is on sensory representation in the visual cortex and related perceptual decisions. The frameworks used to study population coding include population vectors, linear decoders, and Bayesian inference. Simple examples are provided to illustrate these concepts. Testing theories of population coding is an emerging subject in systems neuroscience, but advances in multi-neuronal recording and analysis suggest that an understanding is within reach.