Approximate Inference for Time-varying Interactions and Macroscopic Dynamics of Neural Populations

TL;DR

This paper introduces scalable approximation methods for dynamic neural interaction modeling, enabling analysis of larger neural populations and their macroscopic properties during behavior and stimulus processing.

Contribution

It develops analytic approximation techniques combining pseudolikelihood and mean-field methods to estimate time-varying pairwise neural interactions in populations up to 60 neurons.

Findings

Accurately estimates network dynamics such as sparseness, entropy, and heat capacity.

Demonstrates utility on monkey V4 neuron data and simulated neural networks.

Enables analysis of neural population dynamics beyond small-scale models.

Abstract

The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons.