Maximum entropy models reveal the excitatory and inhibitory
correlation structures in cortical neuronal activity.
Trang-Anh Nghiem, Bartosz Telenczuk, Olivier Marre, Alain
Destexhe and Ulisse Ferrari
Physical Review E 98: 012402, 2018.
Maximum entropy models can be inferred from large datasets to
uncover how collective dynamics emerge from local interactions.
Here, such models are employed to investigate neurons recorded by
multi-electrode 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 us to shed light on differences between excitatory
and inhibitory activity across different brain states such
aswakefulness and deep sleep, in agreementwith previous findings.
Additionally, maximum entropy models can also unveil novel features
of neuronal interactions, which are found to be dominated by
pairwise interactions duringwakefulness, but are population-wide
during deep sleep. Overall, we demonstrate that maximum entropy
models can be useful to analyze datasets with classified neuron
types and to reveal the respective roles of excitatory and
inhibitory neurons in organizing coherent dynamics in the cerebral
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