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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.

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.

Neural responses in the cerebral cortex change dramatically between the ‘synchronized’ state during sleep and ‘desynchronized’ state during wakefulness. Our understanding of cortical state emerges largely from experiments performed in sensory areas of head-fixed or tethered rodents due to technical limitations of recording from larger freely-moving animals for several hours. Here, we report a system integrating wireless electrophysiology, wireless eye tracking, and real-time video analysis to examine the dynamics of population activity in a high-level, executive area – dorsolateral prefrontal cortex (dlPFC) of unrestrained monkey. This technology allows us to identify cortical substates during quiet and active wakefulness, and transitions in population activity during rest. We further show that narrow-spiking neurons exhibit stronger synchronized fluctuations in population activity than broad-spiking neurons regardless of state. Our results show that cortical state is controlled by behavioral demands and arousal by asymmetrically modulating the slow response fluctuations of local excitatory and inhibitory cell populations., Authors develop an integrated wireless system to examine brain states in freely-moving monkeys. They show that neural population activity in prefrontal cortex covaries with natural behavioral dynamics. Active behavior is associated with elevated arousal and increases in spiking activity while reducing low-frequency synchrony within cortical populations.

Summary 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.

In the hippocampus, a network of place cells generates a cognitive map of space, in which each cell is responsive to a particular area of the environment -- its place field. The peak response of each cell and the size of each place field have considerable variability. Experimental evidence suggests that place cells encode a topological map of space that serves as a basis of spatial memory and spatial awareness. Using a computational model based on Persistent Homology Theory we demonstrate that if the parameters of the place cells spiking activity fall inside of the physiological range, the network correctly encodes the topological features of the environment. We next introduce parameters of synaptic connectivity into the model and demonstrate that failures in synapses that detect coincident neuronal activity lead to spatial learning deficiencies similar to the ones that are observed in rodent models of neurodegenerative diseases. Moreover, we show that these learning deficiencies may be mitigated by increasing the number of active cells and/or by increasing their firing rate, suggesting the existence of a compensatory mechanism inherent to the cognitive map.