Pairwise Ising model analysis of human cortical neuron recordings

TL;DR

This study applies a pairwise Ising model to analyze human cortical neuron recordings, revealing limitations during deep sleep and suggesting the need for more complex models to capture transient high activity states.

Contribution

The paper demonstrates the application of a maximum entropy pairwise Ising model to large-scale human cortical neuron data and compares its effectiveness across different brain states.

Findings

Pairwise Ising model captures some statistical properties during wakefulness.

Model fails to reproduce high activity transients during deep sleep.

Additional constraints are needed for accurate modeling of sleep activity.

Abstract

During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investigate their impact on neuronal behavior, we apply a pairwise Ising model analysis by inferring the maximum entropy model that reproduces single and pairwise moments of the neuron's spiking activity. In this work we first review the inference algorithm introduced in Ferrari,Phys. Rev. E (2016). We then succeed in applying the algorithm to infer the model from a large ensemble of neurons recorded by multi-electrode array in human temporal cortex. We compare the Ising model performance in capturing the statistical properties of the network activity during wakefulness and deep sleep. For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data.