Sparseness is a quantitative measure of coding efficiency that characterizes the extent to which information is represented by the selective activation of a small subset of neurons. However, although highly influential, this general principle of sensory coding has only been tested under restrictive experimental conditions whereby stimuli were presented on a computer screen while animals were physically restrained. Here, we wirelessly recorded the spiking activity of populations of neurons in visual (area V4) and dorsolateral prefrontal cortex (dlPFC) in conjunction with oculomotor events while freely moving male macaques inspected their environment. A convolutional autoencoder was trained to reduce the dimensionality of fixated natural stimuli to help examine the structure of population activity in each cortical area and its contribution to stimulus encoding in different states of wakefulness. We found a sparseness-based connectivity rule between neurons in different cortical areas whereby V4 and dlPFC neurons with high degrees of sparseness are more strongly coupled. The highly sparse neurons in both areas had a higher contribution to stimulus encoding during active relative to passive wakefulness. Our results indicate that sparsification constitutes a general principle underlying population coding across sensory and executive cortical circuits during unrestrained environmental exploration.

The brain's ability to extract temporal information from dynamic stimuli in the environment is essential for everyday behavior. To extract temporal statistical regularities, neural circuits must possess the ability to measure, produce, and anticipate sensory events. Here we report that when neural populations in macaque primary visual cortex are triggered to exhibit a periodic response to a repetitive sequence of optogenetic laser flashes, they learn to accurately reproduce the temporal sequence even when light stimulation is turned off. Despite the fact that individual cells had a poor capacity to extract temporal information, the population of neurons reproduced the periodic sequence in a temporally precise manner. The same neural population could learn different frequencies of external stimulation, and the ability to extract temporal information was found in all cortical layers. These results demonstrate a remarkable ability of sensory cortical populations to extract and reproduce complex temporal structure from unsupervised external stimulation even when stimuli are perceptually irrelevant.

Causal control of short- and long-range projections between networks is necessary to study complex cognitive processes and cortical computations. Neural circuits can be studied via optogenetic approaches, which provide excellent genetic and temporal control and electrophysiological recordings. However, in nonhuman primates (NHPs), these approaches are commonly performed at a single location, missing out on the potential to test connections between separate networks. We have recently developed an approach for optogenetic manipulation in NHPs which targets intra- and interareal cortical projections. Here we describe the combination of optogenetic stimulation with standard chamber-based electrophysiological recordings in awake NHPs to monitor and manipulate both short- and long-range feedforward and feedback circuits. We describe the injection of viral constructs, the simultaneous electrophysiological recordings with the optical stimulation of neurons at various cortical distances and the evaluation of gene expression using a focal biopsy technique. We focus on details that are specific to NHP preparations, such as the precise targeting of injection sites, choosing appropriate viral constructs and considerations for behavioral measures. When combined with laminar electrode configurations (to functionally identify cortical layers) and complex cognitive behavioral tasks, our approach can be used to investigate an array of systems neuroscience questions, such as the role of feedback circuits in attention and the role of lateral connections in contrast normalization. The procedure requires 2–3 active days and 45 waiting days to transduce selected neural circuits and several weeks to complete experiments. The procedure is appropriate for users with expertise in in vivo, awake electrophysiology with NHPs. The combination of optogenetic manipulation with standard chamber-based electrophysiological recordings in awake nonhuman primates enables the manipulation of short- and long-range feedforward and feedback circuits

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.

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.

Sleep improves cognitive performance, yet little is known about the neural mechanisms of this improvement. We performed multielectrode recording in macaque visual and dorsolateral prefrontal cortex while animals performed a visual discrimination task before and after non-rapid eye movement (NREM) sleep. Although sleep induces synchronized fluctuations in population activity across cortical areas, the post-sleep population activity became more desynchronized relative to the pre-sleep state. The changes after sleep were correlated with an increase in information encoded in population activity in each area and improved behavioral performance. Electrically stimulating visual cortex at 4 hertz emulated the beneficial effects of sleep on network and perceptual performance. A large-scale neural network model indicated that asymmetric depression of local intracortical synapses is consistent with the observed changes in neural activity after sleep.

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.

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.

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.

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.

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.

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 cortical state to selectively enhance different modes of sensory processing during perception in a state-dependent manner.

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.

Color is a key feature of natural environments that higher mammals routinely use to detect food, avoid predators, and interpret social signals. The distribution of color signals in natural scenes is widely variable, ranging from uniform patches to highly nonuniform regions in which different colors lie in close proximity. Whether individual neurons are tuned to this high degree of variability of color signals is unknown. Here, we identified a distinct population of cells in macaque visual cortex (area V4) that have a heterogeneous receptive field (RF) structure in which individual subfields are tuned to different colors even though the full RF is only weakly tuned. This spatial heterogeneity in color tuning indicates a higher degree of complexity of color-encoding mechanisms in visual cortex than previously believed to efficiently extract chromatic information from the environment.

Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These offtarget side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey primary visual cortex (V1) induces robust suppression at the lighttargeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified four types of stimulusevoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. These results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Importantly, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side effects can confound interpretation of inactivation experiments.

One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown. Here, we performed multi-area electrical recordings to find that 2nd-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured ∼90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from the prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions.

The complete characterization of the activity of a neural population is a challenging task that is complicated by the number of potential interactions that grows exponentially as the size of the population increases. One influential view that has emerged several decades ago is that simple pairwise interactions between neurons can account for the observed firing patterns of large networks. However, despite its prevalence, this view originates from computational and electrophysiological studies in the retina and the primary visual cortex (V1) of anesthetized animals. Therefore, whether or not pairwise interactions predict the observed distribution of firing patterns across multiple brain areas in behaving animals remains unknown. Here we performed multi-electrode recordings from 3 cortical areas to report that second-order neuronal interactions can explain a high fraction of the entropy of the population response in early and mid-level visual cortex (areas V1 and V4) while providing a good approximation of the probability of spiking for groups of neurons. Surprisingly, despite the fact the firing pattern of neurons is controlled by brain state, the model based on pairwise interactions captures more than 90% of the structure in the detailed patterns of spiking observed during wakefulness and sleep. In contrast, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from executive brain areas, such as the dorsolateral prefrontal cortex (dlPFC). These results indicate that higher-order interactions explain the population dynamics in downstream, executive areas, whereas simple pairwise interactions account for the dynamic behavior of neuronal networks in sensory cortical areas.

Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These off-target side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey primary visual cortex (V1) induces robust suppression at the light-targeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified four types of stimulus-evoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. These results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Importantly, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side effects can confound interpretation of inactivation experiments.

Primary visual cortex (V1) is absolutely necessary for normal visual processing, but whether V1 encodes upcom-ing behavioral decisions based on visual information is an unresolved issue, with conflicting evidence. Further,no study so far has been able to predict choice from time-resolved spiking activity in V1. Here, we hypothesizedthat the choice cannot be decoded with classical decoding schemes due to the noise in incorrect trials, but itmight be possible to decode with generalized learning. We trained the decoder in the presence of the informationon both the stimulus class and the correct behavioral choice. The learned structure of population responseswas then utilized to decode trials that differ in the choice alone. We show that with such generalized learningscheme, the choice can be successfully predicted from spiking activity of neural ensembles in V1 in single trials,relying on the partial overlap in the representation between the stimuli and the choice. In addition, we showthat the representation of the choice is primarily carried by bursting neurons in the superficial layer of thecortex. We demonstrated how bursting of single neurons and noise correlations between neurons with similardecoding selectivity helps the accumulation of the choice signal.