Ensemble Inhibition and Excitation in the Human Cortex: an Ising Model Analysis with Uncertainties
arXiv:1810.07253 · doi:10.1103/PhysRevE.99.032408
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.
arXiv comment17 pages, 8 figures
References in corpus (35)
- An efficient, multiple range random walk algorithm to calculate the density of states
- Weak pairwise correlations imply strongly correlated network states in a neural population
- Emergent complex neural dynamics
- Optimal Dynamical Range of Excitable Networks at Criticality
- Are biological systems poised at criticality?
- Network information and connected correlations
- Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex
- Searching for collective behavior in a network of real neurons
- Inverse statistical problems: from the inverse Ising problem to data science
- Power-law statistics and universal scaling in the absence of criticality
- The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity
- Pairwise maximum entropy models for studying large biological systems: when they can and when they can't work
- Inverse Ising inference using all the data
- Consciousness as a State of Matter
- Prediction of spatio-temporal patterns of neural activity from pairwise correlations
- Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data
- Statistical physics of pairwise probability models
- Zipf's law and criticality in multivariate data without fine-tuning
- The simplest maximum entropy model for collective behavior in a neural network
- Dynamical criticality in the collective activity of a population of retinal neurons
- On sampling and modeling complex systems
- Spin glass models for a network of real neurons
- On the criticality of inferred models
- Faster solutions of the inverse pairwise Ising problem
- The Effect of Nonstationarity on Models Inferred from Neural Data
- Signatures of criticality arise in simple neural population models with correlations
- Maximum entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity
- Gibbs distribution analysis of temporal correlations structure in retina ganglion cells
- Criticality of mostly informative samples: A Bayesian model selection approach
- Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations
- Statistical modelling of higher-order correlations in pools of neural activity
- A tractable method for describing complex couplings between neurons and population rate
- Pairwise maximum-entropy models and their Glauber dynamics: bimodality, bistability, non-ergodicity problems, and their elimination via inhibition
- The Structured `Low Temperature' Phase of the Retinal Population Code
- Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons