Visuo-frontal interactions during social learning in freely moving macaques.

Social interactions represent a ubiquitous aspect of our everyday life that we acquire by interpreting and responding to visual cues from conspecifics1. However, despite the general acceptance of this view, how visual information is used to guide the decision to cooperate is unknown. Here, we wirelessly recorded the spiking activity of populations of neurons in the visual and prefrontal cortex in conjunction with wireless recordings of oculomotor events while freely moving macaques engaged in social cooperation. As animals learned to cooperate, visual and executive areas refined the representation of social variables, such as the conspecific or reward, by distributing socially relevant information among neurons in each area. Decoding population activity showed that viewing social cues influences the decision to cooperate. Learning social events increased coordinated spiking between visual and prefrontal cortical neurons, which was associated with improved accuracy of neural populations to encode social cues and the decision to cooperate. These results indicate that the visual-frontal cortical network prioritizes relevant sensory information to facilitate learning social interactions while freely moving macaques interact in a naturalistic environment.

Brain state limits propagation of neural signals in laminar cortical circuits.

Our perception of the environment relies on the efficient propagation of neural signals across cortical networks. During the time course of a day, neural responses fluctuate dramatically as the state of the brain changes to possibly influence how electrical signals propagate across neural circuits. Despite the importance of this issue, how patterns of spiking activity propagate within neuronal circuits in different brain states remains unknown. Here, we used multielectrode laminar arrays to reveal that brain state strongly modulates the propagation of neural activity across the layers of early visual cortex (V1). We optogenetically induced synchronized state transitions within a group of neurons and examined how far electrical signals travel during wakefulness and rest. Although optogenetic stimulation elicits stronger neural responses during wakefulness relative to rest, signals propagate only weakly across the cortical column during wakefulness, and the extent of spread is inversely related to arousal level. In contrast, the light-induced population activity vigorously propagates throughout the entire cortical column during rest, even when neurons are in a desynchronized wake-like state prior to light stimulation. Mechanistically, the influence of global brain state on the propagation of spiking activity across laminar circuits can be explained by state-dependent changes in the coupling between neurons. Our results impose constraints on the conclusions of causal manipulation studies attempting to influence neural function and behavior, as well as on previous computational models of perception assuming robust signal propagation across cortical layers and areas.

Representation of Rapid Image Sequences in V4 Networks.

Natural viewing often consists of sequences of brief fixations to image patches of different structure. Whether and how briefly presented sequential stimuli are encoded in a temporal-position manner is poorly understood. Here, we performed multiple-electrode recordings in the visual cortex (area V4) of nonhuman primates (Macaca mulatta) viewing a sequence of 7 briefly flashed natural images, and measured correlations between the cue-triggered population response in the presence and absence of the stimulus. Surprisingly, we found significant correlations for images occurring at the beginning and the end of a sequence, but not for those in the middle. The correlation strength increased with stimulus exposure and favored the image position in the sequence rather than image identity. These results challenge the commonly held view that images are represented in visual cortex exclusively based on their informational content, and indicate that, in the absence of sensory information, neuronal populations exhibit reactivation of stimulus-evoked responses in a way that reflects temporal position within a stimulus sequence.

Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy.

Information processing in the cerebral cortex depends not only on the nature of incoming stimuli, but also on the state of neuronal networks at the time of stimulation. That is, the same stimulus will be processed differently depending on the neuronal context in which it is received. A major factor that could influence neuronal context is the background, or ongoing neuronal activity before stimulation. In visual cortex, ongoing activity is known to play a critical role in the development of local circuits, yet whether it influences the coding of visual features in adult cortex is unclear. Here, we investigate whether and how the information encoded by individual neurons and populations in primary visual cortex (V1) depends on the ongoing activity before stimulus presentation. We report that when individual neurons are in a "low" prestimulus state, they have a higher capacity to discriminate stimulus features, such as orientation, despite their reduction in evoked responses. By measuring the distribution of prestimulus activity across a population of neurons, we found that network discrimination accuracy is improved in the low prestimulus state. Thus, the distribution of ongoing activity states across the network creates an "internal context" that dynamically filters incoming stimuli to modulate the accuracy of sensory coding. The modulation of stimulus coding by ongoing activity state is consistent with recurrent network models in which ongoing activity dynamically controls the balanced background excitation and inhibition to individual neurons.

Focal Changes to Human Electrocorticography With Drowsiness: A Novel Measure of Local Sleep.

Drowsiness may be defined as the progressive loss of cortical processing efficiency that occurs with time passing while awake. This loss of cortical processing efficiency is reflected in focal changes to the electroencephalogram, including islands of increased delta power concurrent with drop-offs in neuronal activity (i.e., focal cortical inactivity). The authors hypothesized that these focal changes are evidenced at individual electrodes by combination of increased instantaneous amplitude in delta band and decreased instantaneous frequency in theta-alpha band, permitting their categorization as "active" and "inactive." An analysis of records from six patients with refractory epilepsy undergoing video-electrocorticographic monitoring was conducted. Feature extraction and state classification on multiple recordings revealed focal changes consistent with the hypothesis, as well as progressively increased numbers of inactive electrodes with time awake. The implications of these findings on the study of sleep, and particularly local sleep, are discussed.

Negative Correlations in Visual Cortical Networks.

The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function.

Reactivation of visual-evoked activity in human cortical networks.

In the absence of sensory input, neuronal networks are far from being silent. Whether spontaneous changes in ongoing activity reflect previous sensory experience or stochastic fluctuations in brain activity is not well understood. Here we demonstrate reactivation of stimulus-evoked activity that is distributed across large areas in the human brain. We performed simultaneous electrocorticography recordings from occipital, parietal, temporal, and frontal areas in awake humans in the presence and absence of sensory stimulation. We found that, in the absence of visual input, repeated exposure to brief natural movies induces robust stimulus-specific reactivation at individual recording sites. The reactivation sites were characterized by greater global connectivity compared with those sites that did not exhibit reactivation. Our results indicate a surprising degree of short-term plasticity across multiple networks in the human brain as a result of repeated exposure to unattended information.

Image sequence reactivation in awake V4 networks.

In the absence of sensory input, neuronal networks are far from being silent. Whether spontaneous changes in ongoing activity reflect previous sensory experience or stochastic fluctuations in brain activity is not well understood. Here we describe reactivation of stimulus-evoked activity in awake visual cortical networks. We found that continuous exposure to randomly flashed image sequences induces reactivation in macaque V4 cortical networks in the absence of visual stimulation. This reactivation of previously evoked activity is stimulus-specific, occurs only in the same temporal order as the original response, and strengthens with increased stimulus exposures. Importantly, cells exhibiting significant reactivation carry more information about the stimulus than cells that do not reactivate. These results demonstrate a surprising degree of experience-dependent plasticity in visual cortical networks as a result of repeated exposure to unattended information. We suggest that awake reactivation in visual cortex may underlie perceptual learning by passive stimulus exposure.

Adaptive coding of visual information in neural populations.

Our perception of the environment relies on the capacity of neural networks to adapt rapidly to changes in incoming stimuli. It is increasingly being realized that the neural code is adaptive, that is, sensory neurons change their responses and selectivity in a dynamic manner to match the changes in input stimuli. Understanding how rapid exposure, or adaptation, to a stimulus of fixed structure changes information processing by cortical networks is essential for understanding the relationship between sensory coding and behaviour. Physiological investigations of adaptation have contributed greatly to our understanding of how individual sensory neurons change their responses to influence stimulus coding, yet whether and how adaptation affects information coding in neural populations is unknown. Here we examine how brief adaptation (on the timescale of visual fixation) influences the structure of interneuronal correlations and the accuracy of population coding in the macaque (Macaca mulatta) primary visual cortex (V1). We find that brief adaptation to a stimulus of fixed structure reorganizes the distribution of correlations across the entire network by selectively reducing their mean and variability. The post-adaptation changes in neuronal correlations are associated with specific, stimulus-dependent changes in the efficiency of the population code, and are consistent with changes in perceptual performance after adaptation. Our results have implications beyond the predictions of current theories of sensory coding, suggesting that brief adaptation improves the accuracy of population coding to optimize neuronal performance during natural viewing.

Adaptation-induced synchronization in laminar cortical circuits.

A fundamental feature of information processing in neocortex is the ability of individual neurons to adapt to changes in incoming stimuli. It is increasingly being understood that cortical adaptation is a phenomenon that requires network interactions. The fact that the structure of local networks depends critically on cortical layer raises the possibility that adaptation could induce specific effects in different layers. Here we show that brief exposure (300 ms) to a stimulus of fixed orientation modulates the strength of synchronization between individual neurons and local population activity in the gamma-band frequency (30-80 Hz) in macaque primary visual cortex (V1) and influences the ability of individual neurons to encode stimulus orientation. Using laminar probes, we found that although stimulus presentation elicits a large increase in the gamma synchronization of rhythmic neuronal activity in the input (granular) layers of V1, adaptation caused a pronounced increase in synchronization in the cortical output (supragranular) layers. The increase in gamma synchronization after adaptation was significantly correlated with an improvement in neuronal orientation discrimination performance only in the supragranular layers. Thus, synchronization between the spiking activity of individual neurons and their local population may enhance sensory coding to optimize network processing across laminar circuits.

Population coding of strategic variables during foraging in freely moving macaques.

Until now, it has been difficult to examine the neural bases of foraging in naturalistic environments because previous approaches have relied on restrained animals performing trial-based foraging tasks. Here we allowed unrestrained monkeys to freely interact with concurrent reward options while we wirelessly recorded population activity in the dorsolateral prefrontal cortex. The animals decided when and where to forage based on whether their prediction of reward was fulfilled or violated. This prediction was not solely based on a history of reward delivery, but also on the understanding that waiting longer improves the chance of reward. The task variables were continuously represented in a subspace of the high-dimensional population activity, and this compressed representation predicted the animal's subsequent choices better than the true task variables and as well as the raw neural activity. Our results indicate that monkeys' foraging strategies are based on a cortical model of reward dynamics as animals freely explore their environment.

A maximum entropy test for evaluating higher-order correlations in spike counts.

Evaluating the importance of higher-order correlations of neural spike counts has been notoriously hard. A large number of samples are typically required in order to estimate higher-order correlations and resulting information theoretic quantities. In typical electrophysiology data sets with many experimental conditions, however, the number of samples in each condition is rather small. Here we describe a method that allows to quantify evidence for higher-order correlations in exactly these cases. We construct a family of reference distributions: maximum entropy distributions, which are constrained only by marginals and by linear correlations as quantified by the Pearson correlation coefficient. We devise a Monte Carlo goodness-of-fit test, which tests--for a given divergence measure of interest--whether the experimental data lead to the rejection of the null hypothesis that it was generated by one of the reference distributions. Applying our test to artificial data shows that the effects of higher-order correlations on these divergence measures can be detected even when the number of samples is small. Subsequently, we apply our method to spike count data which were recorded with multielectrode arrays from the primary visual cortex of anesthetized cat during an adaptation experiment. Using mutual information as a divergence measure we find that there are spike count bin sizes at which the maximum entropy hypothesis can be rejected for a substantial number of neuronal pairs. These results demonstrate that higher-order correlations can matter when estimating information theoretic quantities in V1. They also show that our test is able to detect their presence in typical in-vivo data sets, where the number of samples is too small to estimate higher-order correlations directly.

Suppressing feedback signals to visual cortex abolishes attentional modulation.

Attention improves perception by enhancing the neural encoding of sensory information. A long-standing hypothesis is that cortical feedback projections carry top-down signals to influence sensory coding. However, this hypothesis has never been tested to establish causal links. We used viral tools to label feedback connections from cortical area V4 targeting early visual cortex (area V1). While monkeys performed a visual-spatial attention task, inactivating feedback axonal terminals in V1 without altering local intracortical and feedforward inputs reduced the response gain of single cells and impaired the accuracy of neural populations for encoding external stimuli. These effects are primarily manifested in the superficial layers of V1 and propagate to downstream area V4. Attention enhances sensory coding across visual cortex by specifically altering the strength of corticocortical feedback in a layer-dependent manner.

Endogenous fluctuations in cortical state selectively enhance different modes of sensory processing in human temporal lobe.

The degree of synchronized fluctuations in neocortical network activity can vary widely during alertness. One influential idea that has emerged over the past few decades is that perceptual decisions are more accurate when the state of population activity is desynchronized. This suggests that optimal task performance may occur during a particular cortical state - the desynchronized state. Here we show that, contrary to this view, cortical state can both facilitate and suppress perceptual performance in a task-dependent manner. We performed electrical recordings from surface-implanted grid electrodes in the temporal lobe while human subjects completed two perceptual tasks. We found that when local population activity is in a synchronized state, network and perceptual performance are enhanced in a detection task and impaired in a discrimination task, but these modulatory effects are reversed when population activity is desynchronized. These findings indicate that the brain has adapted to take advantage of endogenous fluctuations in the state of neural populations in temporal cortex to selectively enhance different modes of sensory processing during perception in a state-dependent manner.

Adaptive coding across visual features during free-viewing and fixation conditions.

Theoretical studies have long proposed that adaptation allows the brain to effectively use the limited response range of sensory neurons to encode widely varying natural inputs. However, despite this influential view, experimental studies have exclusively focused on how the neural code adapts to a range of stimuli lying along a single feature axis, such as orientation or contrast. Here, we performed electrical recordings in macaque visual cortex (area V4) to reveal significant adaptive changes in the neural code of single cells and populations across multiple feature axes. Both during free viewing and passive fixation, populations of cells improved their ability to encode image features after rapid exposure to stimuli lying on orthogonal feature axes even in the absence of initial tuning to these stimuli. These results reveal a remarkable adaptive capacity of visual cortical populations to improve network computations relevant for natural viewing despite the modularity of the functional cortical architecture.

Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo.

To produce adaptive behavior, neural networks must balance between plasticity and stability. Computational work has demonstrated that network stability requires plasticity mechanisms to be counterbalanced by rapid compensatory processes. However, such processes have yet to be experimentally observed. Here we demonstrate that repeated optogenetic activation of excitatory neurons in monkey visual cortex (area V1) induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. This new form of rapid plasticity was observed only in the correlation structure, with firing rates remaining stable across trials. A computational network model operating in the balanced regime confirmed experimental findings and revealed that inhibitory plasticity is responsible for the decrease in correlated activity in response to repeated light stimulation. These results provide the first experimental evidence for rapid homeostatic plasticity that primarily operates during wakefulness, which stabilizes neuronal interactions during strong network co-activation.

Adaptive changes in neuronal synchronization in macaque V4.

A fundamental property of cortical neurons is the capacity to exhibit adaptive changes or plasticity. Whether adaptive changes in cortical responses are accompanied by changes in synchrony between individual neurons and local population activity in sensory cortex is unclear. This issue is important as synchronized neural activity is hypothesized to play an important role in propagating information in neuronal circuits. Here, we show that rapid adaptation (300 ms) to a stimulus of fixed orientation modulates the strength of oscillatory neuronal synchronization in macaque visual cortex (area V4) and influences the ability of neurons to distinguish small changes in stimulus orientation. Specifically, rapid adaptation increases the synchronization of individual neuronal responses with local population activity in the gamma frequency band (30-80 Hz). In contrast to previous reports that gamma synchronization is associated with an increase in firing rates in V4, we found that the postadaptation increase in gamma synchronization is associated with a decrease in neuronal responses. The increase in gamma-band synchronization after adaptation is functionally significant as it is correlated with an improvement in neuronal orientation discrimination performance. Thus, adaptive synchronization between the spiking activity of individual neurons and their local population can enhance temporally insensitive, rate-based-coding schemes for sensory discrimination.

Learning by Exposure in the Visual System.

It is increasingly being understood that perceptual learning involves different types of plasticity. Thus, whereas the practice-based improvement in the ability to perform specific tasks is believed to rely on top-down plasticity, the capacity of sensory systems to passively adapt to the stimuli they are exposed to is believed to rely on bottom-up plasticity. However, top-down and bottom-up plasticity have never been investigated concurrently, and hence their relationship is not well understood. To examine whether passive exposure influences perceptual performance, we asked subjects to test their orientation discrimination performance around and orthogonal to the exposed orientation axes, at an exposed and an unexposed location while oriented sine-wave gratings were presented in a fixed position. Here we report that repetitive passive exposure to oriented sequences that are not linked to a specific task induces a persistent, bottom-up form of learning that is stronger than top-down practice learning and generalizes across complex stimulus dimensions. Importantly, orientation-specific exposure learning led to a robust improvement in the discrimination of complex stimuli (shapes and natural scenes). Our results indicate that long-term sensory adaptation by passive exposure should be viewed as a form of perceptual learning that is complementary to practice learning in that it reduces constraints on speed and generalization.

Efficient coding in heterogeneous neuronal populations.

A ubiquitous feature of neuronal responses within a cortical area is their high degree of inhomogeneity. Even cells within the same functional column are known to have highly heterogeneous response properties when the same stimulus is presented. Whether the wide diversity of neuronal responses is an epiphenomenon or plays a role for cortical function is unknown. Here, we examined the relationship between the heterogeneity of neuronal responses and population coding. Contrary to our expectation, we found that the high variability of intrinsic response properties of individual cells changes the structure of neuronal correlations to improve the information encoded in the population activity. Thus, the heterogeneity of neuronal responses is in fact beneficial for sensory coding when stimuli are decoded from the population response.