Maximum Entropy models can be inferred from large data-sets to uncover how local interactions can generate collective dynamics . Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory  types, we construct a model including this biological feature. By comparing the performances of Maximum Entropy models at predicting neural activity in wakefulness and deep sleep, we identify the dominant interactions between neurons in each brain state. We find that during wakefulness, dominant functional interactions are pairwise while during sleep, interactions are population-wide  . In particular, inhibitory cells are shown to be strongly tuned to the inhibitory population. This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory cells, and can reveal the role of inhibitory cells URLanizing coherent dynamics in  cerebral cortex.
Maximum Entropy models can be inferred from large data-sets to uncover how collective dynamics emerge from local interactions . Here, such models are employed to investigate neurons recorded by multielectrode arrays in the human and monkey cortex . Taking advantage of the separation of excitatory and inhibitory neuron types, we construct a model including this distinction. This approach allows to shed light upon differences between excitatory and inhibitory activity across different brain states such as wakefulness and deep sleep, in agreement with previous findings. Additionally, Maximum Entropy models can also unveil novel features of neuronal interactions, which are found to be dominated by pairwise interactions during wakefulness, but are population-wide during deep sleep . In particular, inhibitory neurons are observed to be strongly tuned to the inhibitory population. Overall, we demonstrate Maximum Entropy models can be useful to analyze data-sets with classified neuron types, and to reveal the respective roles of excitatory and inhibitory neurons URLanizing coherent dynamics in the cerebral cortex.
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local interactions can generate collective dynamics
collective dynamics emerge from local interactions
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we employ such methods to investigate the characteristics of
such models are employed to investigate
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cortex of
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throughout states of wakefulness and sleep
cortex
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cells into
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neuron
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biological feature. By comparing the performances of Maximum Entropy models at predicting neural activity in
distinction. This approach allows to shed light upon differences between excitatory and inhibitory activity across different brain states such as
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we identify the dominant interactions between neurons in each brain state. We find that during wakefulness, dominant functional interactions
in agreement with previous findings. Additionally, Maximum Entropy models can also unveil novel features of neuronal interactions, which are found to be dominated by pairwise interactions during wakefulness, but
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pairwise while during sleep, interactions are
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during deep sleep
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cells are shown
neurons are observed
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This shows that
Overall, we demonstrate
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excitatory and inhibitory cells, and can reveal the role of inhibitory cells
classified neuron types, and to reveal the respective roles of excitatory and inhibitory neurons
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the