Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties
Name
PhysRevE.99.032408.pdf
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4.15 MB
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Author(s)
Zanoci, Cristian
Dehghani, Nima
Tegmark, Max Erik
Date Issued
March 2019
Journal
Physical Review E
Publisher
American Physical Society
Citation
Zanoci, Cristian, et al. “Ensemble Inhibition and Excitation in the Human Cortex: An Ising-Model Analysis with Uncertainties.” Physical Review E, vol. 99, no. 3, Mar. 2019. © 2019 American Physical Society
Version
Final published version
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 activity 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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DOI of Published Version
https://doi.org/10.1103/PhysRevE.99.032408
