Feature representation under crowding in macaque V1 and V4 neuronal populations.

Visual perception depends strongly on spatial context. A profound example is visual crowding, whereby the presence of nearby stimuli impairs the discriminability of object features. Despite extensive work on perceptual crowding and the spatial integrative properties of visual cortical neurons, the link between these two aspects of visual processing remains unclear. To understand better the neural basis of crowding, we recorded activity simultaneously from neuronal populations in V1 and V4 of fixating macaque monkeys. We assessed the information available from the measured responses about the orientation of a visual target both for targets presented in isolation and amid distractors. Both single neuron and population responses had less information about target orientation when distractors were present. Information loss was moderate in V1 and more substantial in V4. Information loss could be traced to systematic divisive and additive changes in neuronal tuning. Additive and multiplicative changes in tuning were more severe in V4; in addition, tuning exhibited other, non-affine transformations that were greater in V4, further restricting the ability of a fixed sensory readout strategy to extract accurate feature information across displays. Our results provide a direct test of crowding effects at different stages of the visual hierarchy. They reveal how crowded visual environments alter the spiking activity of cortical populations by which sensory stimuli are encoded and connect these changes to established mechanisms of neuronal spatial integration.

Feedforward and feedback interactions between visual cortical areas use different population activity patterns.

Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", which allows feedback signals to not directly affect activity that is fed forward.

Disentangling the flow of signals between populations of neurons.

Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.

Neuronal variability reflects probabilistic inference tuned to natural image statistics.

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses-their variability-can be explained by a probabilistic representation tuned to naturalistic inputs.

Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans.

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.

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.

Statistical methods for dissecting interactions between brain areas.

The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of inter-areal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions.

An Open Resource for Non-human Primate Optogenetics.

Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.

Principles of Corticocortical Communication: Proposed Schemes and Design Considerations.

Nearly all brain functions involve routing neural activity among a distributed network of areas. Understanding this routing requires more than a description of interareal anatomical connectivity: it requires understanding what controls the flow of signals through interareal circuitry and how this communication might be modulated to allow flexible behavior. Here we review proposals of how communication, particularly between visual cortical areas, is instantiated and modulated, highlighting recent work that offers new perspectives. We suggest transitioning from a focus on assessing changes in the strength of interareal interactions, as often seen in studies of interareal communication, to a broader consideration of how different signaling schemes might contribute to computation. To this end, we discuss a set of features that might be desirable for a communication scheme.

Spatial contextual effects in primary visual cortex limit feature representation under crowding.

Crowding is a profound loss of discriminability of visual features, when a target stimulus is surrounded by distractors. Numerous studies of human perception have characterized how crowding depends on the properties of a visual display. Yet, there is limited understanding of how and where stimulus information is lost in the visual system under crowding. Here, we show that macaque monkeys exhibit perceptual crowding for target orientation that is similar to humans. We then record from neuronal populations in monkey primary visual cortex (V1). These populations show an appreciable loss of information about target orientation in the presence of distractors, due both to divisive and additive modulation of responses to targets by distractors. Our results show that spatial contextual effects in V1 limit the discriminability of visual features and can contribute substantively to crowding.

Texture Interpolation for Probing Visual Perception.

Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (i.e. the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures. Compared to other CNN-based approaches, our interpolation method appears to match more closely the geometry of texture perception, and our mathematical framework is better suited to study its statistical nature. We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.

Binocular viewing geometry shapes the neural representation of the dynamic three-dimensional environment.

Sensory signals give rise to patterns of neural activity, which the brain uses to infer properties of the environment. For the visual system, considerable work has focused on the representation of frontoparallel stimulus features and binocular disparities. However, inferring the properties of the physical environment from retinal stimulation is a distinct and more challenging computational problem-this is what the brain must actually accomplish to support perception and action. Here we develop a computational model that incorporates projective geometry, mapping the three-dimensional (3D) environment onto the two retinae. We demonstrate that this mapping fundamentally shapes the tuning of cortical neurons and corresponding aspects of perception. For 3D motion, the model explains the strikingly non-canonical tuning present in existing electrophysiological data and distinctive patterns of perceptual errors evident in human behavior. Decoding the world from cortical activity is strongly affected by the geometry that links the environment to the sensory epithelium.

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.

Cortical Areas Interact through a Communication Subspace.

Most brain functions involve interactions among multiple, distinct areas or nuclei. For instance, visual processing in primates requires the appropriate relaying of signals across many distinct cortical areas. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Here we investigate how trial-to-trial fluctuations of population responses in primary visual cortex (V1) are related to simultaneously recorded population responses in area V2. Using dimensionality reduction methods, we find that V1-V2 interactions occur through a communication subspace: V2 fluctuations are related to a small subset of V1 population activity patterns, distinct from the largest fluctuations shared among neurons within V1. In contrast, interactions between subpopulations within V1 are less selective. We propose that the communication subspace may be a general, population-level mechanism by which activity can be selectively routed across brain areas.

Temporal Contingencies Determine Whether Adaptation Strengthens or Weakens Normalization.

A fundamental and nearly ubiquitous feature of sensory encoding is that neuronal responses are strongly influenced by recent experience, or adaptation. Theoretical and computational studies have proposed that many adaptation effects may result in part from changes in the strength of normalization signals. Normalization is a "canonical" computation in which a neuron's response is modulated (normalized) by the pooled activity of other neurons. Here, we test whether adaptation can alter the strength of cross-orientation suppression, or masking, a paradigmatic form of normalization evident in primary visual cortex (V1). We made extracellular recordings of V1 neurons in anesthetized male macaques and measured responses to plaid stimuli composed of two overlapping, orthogonal gratings before and after prolonged exposure to two distinct adapters. The first adapter was a plaid consisting of orthogonal gratings and led to stronger masking. The second adapter presented the same orthogonal gratings in an interleaved manner and led to weaker masking. The strength of adaptation's effects on masking depended on the orientation of the test stimuli relative to the orientation of the adapters, but was independent of neuronal orientation preference. Changes in masking could not be explained by altered neuronal responsivity. Our results suggest that normalization signals can be strengthened or weakened by adaptation depending on the temporal contingencies of the adapting stimuli. Our findings reveal an interplay between two widespread computations in cortical circuits, adaptation and normalization, that enables flexible adjustments to the structure of the environment, including the temporal relationships among sensory stimuli.SIGNIFICANCE STATEMENT Two fundamental features of sensory responses are that they are influenced by adaptation and that they are modulated by the activity of other nearby neurons via normalization. Our findings reveal a strong interaction between these two aspects of cortical computation. Specifically, we show that cross-orientation masking, a form of normalization, can be strengthened or weakened by adaptation depending on the temporal contingencies between sensory inputs. Our findings support theoretical proposals that some adaptation effects may involve altered normalization and offer a network-based explanation for how cortex adjusts to current sensory demands.

Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response.

Primate motor cortex projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid "tangling": moments where similar activity patterns led to dissimilar future patterns. Avoidance of tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Finally, we were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling.

The spatial structure of correlated neuronal variability.

Shared neural variability is ubiquitous in cortical populations. While this variability is presumed to arise from overlapping synaptic input, its precise relationship to local circuit architecture remains unclear. We combine computational models and in vivo recordings to study the relationship between the spatial structure of connectivity and correlated variability in neural circuits. Extending the theory of networks with balanced excitation and inhibition, we find that spatially localized lateral projections promote weakly correlated spiking, but broader lateral projections produce a distinctive spatial correlation structure: nearby neuron pairs are positively correlated, pairs at intermediate distances are negatively correlated and distant pairs are weakly correlated. This non-monotonic dependence of correlation on distance is revealed in a new analysis of recordings from superficial layers of macaque primary visual cortex. Our findings show that incorporating distance-dependent connectivity improves the extent to which balanced network theory can explain correlated neural variability.

Distinguishing Neural Adaptation and Predictive Coding Hypotheses in Auditory Change Detection.

The auditory mismatch negativity (MMN) component of event-related potentials (ERPs) has served as a neural index of auditory change detection. MMN is elicited by presentation of infrequent (deviant) sounds randomly interspersed among frequent (standard) sounds. Deviants elicit a larger negative deflection in the ERP waveform compared to the standard. There is considerable debate as to whether the neural mechanism of this change detection response is due to release from neural adaptation (neural adaptation hypothesis) or from a prediction error signal (predictive coding hypothesis). Previous studies have not been able to distinguish between these explanations because paradigms typically confound the two. The current study disambiguated effects of stimulus-specific adaptation from expectation violation using a unique stimulus design that compared expectation violation responses that did and did not involve stimulus change. The expectation violation response without the stimulus change differed in timing, scalp distribution, and attentional modulation from the more typical MMN response. There is insufficient evidence from the current study to suggest that the negative deflection elicited by the expectation violation alone includes the MMN. Thus, we offer a novel hypothesis that the expectation violation response reflects a fundamentally different neural substrate than that attributed to the canonical MMN.

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.

Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and percent shared variance-with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex.

Dimensionality reduction has been applied in various brain areas to study the activity of populations of neurons. To interpret the outputs of dimensionality reduction, it is important to first understand its outputs for brain areas for which the relationship between the stimulus and neural response is well characterized. Here, we applied principal component analysis (PCA) to trial-averaged neural responses in macaque primary visual cortex (V1) to study two fundamental, population-level questions. First, we characterized how neural complexity relates to stimulus complexity, where complexity is measured using relative comparisons of dimensionality. Second, we assessed the extent to which responses to different stimuli occupy similar dimensions of the population activity space using a novel statistical method. For comparison, we performed the same dimensionality reduction analyses on the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Our results show that the dimensionality of the population response changes systematically with alterations in the properties and complexity of the visual stimulus.