Ensemble Inhibition and Excitation in the Human Cortex: an Ising Model Analysis with Uncertainties

TL;DR

This study applies an enhanced Ising model with uncertainty estimation to human cortical neuron data, revealing its effectiveness in capturing collective neural activity and sleep-state variations, with implications for understanding brain criticality.

Contribution

We developed a reliable method for parameter uncertainty estimation in the Ising model and demonstrated its application to human cortical neurons, improving analysis of neural collective behavior.

Findings

Ising model outperforms independent models in capturing neural correlations

Ignoring inhibitory neurons overestimates E-neuron synchrony

Model explains 80-95% of correlations depending on sleep state

Abstract

The pairwise maximum entropy model, also known as the Ising model, has been widely used to analyze the collective activity of neurons. However, controversy persists in the literature about seemingly inconsistent findings, whose significance is unclear due to lack of reliable error estimates. We therefore develop a method for accurately estimating parameter uncertainty based on random walks in parameter space using adaptive Markov Chain Monte Carlo after the convergence of the main optimization algorithm. We apply our method to the spiking patterns of excitatory and inhibitory neurons recorded with multielectrode arrays in the human temporal cortex during the wake-sleep cycle. Our analysis shows that the Ising model captures neuronal collective behavior much better than the independent model during wakefulness, light sleep, and deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled; ignoring the inhibitory effects of I-neurons dramatically overestimates synchrony among E-neurons. Furthermore, information-theoretic measures reveal that the Ising model explains about 80%-95% of the correlations, depending on sleep state and neuron type. Thermodynamic measures show signatures of criticality, although we take this with a grain of salt as it may be merely a reflection of long-range neural correlations.