Maximum entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity

TL;DR

This study uses maximum entropy models to analyze cortical neuron activity, revealing distinct excitatory and inhibitory interaction patterns across different brain states, and highlighting the importance of neuron type classification.

Contribution

The paper introduces a maximum entropy modeling approach that incorporates neuron type distinctions, providing new insights into excitatory and inhibitory dynamics in cortical activity.

Findings

Pairwise interactions dominate during wakefulness.

Population-wide interactions are prominent during deep sleep.

Inhibitory neurons are strongly tuned to inhibitory populations.

Abstract

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.