  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 in organizing coherent dynamics in the cerebral cortex.
