State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons

TL;DR

This study employs a state-space Ising model to analyze monkey V1 neuron activity, revealing how pairwise interactions influence sparseness, fluctuation, and stimulus encoding during visual processing.

Contribution

It introduces a Bayesian inference-based state-space Ising model to dynamically estimate neural interactions and their role in stimulus encoding.

Findings

Pairwise interactions increase sparseness and fluctuation.

Stimulus orientation is partly encoded in neural interactions.

The model provides dynamic insights into neural network properties.

Abstract

In this study, we analyzed the activity of monkey V1 neurons responding to grating stimuli of different orientations using inference methods for a time-dependent Ising model. The method provides optimal estimation of time-dependent neural interactions with credible intervals according to the sequential Bayes estimation algorithm. Furthermore, it allows us to trace dynamics of macroscopic network properties such as entropy, sparseness, and fluctuation. Here we report that, in all examined stimulus conditions, pairwise interactions contribute to increasing sparseness and fluctuation. We then demonstrate that the orientation of the grating stimulus is in part encoded in the pairwise interactions of the neural populations. These results demonstrate the utility of the state-space Ising model in assessing contributions of neural interactions during stimulus processing.