Effect of Activity and Inter-Cluster Correlations on Information-Theoretic Properties of Neural Networks

TL;DR

This paper investigates how activity levels and inter-cluster correlations influence the information-theoretic properties of neural networks, using solutions of the master equation for small networks.

Contribution

It demonstrates the dependence of entropy and integrated information on neural activity and correlations, providing insights into neural information processing.

Findings

Conditional entropy varies with network activity.

Integrated information is affected by inter-cluster correlations.

Small network solutions reveal key dependencies.

Abstract

On the basis of solutions of the master equation for networks with a small number of neurons it is shown that the conditional entropy and integrated information of neural networks depend on their average activity and inter-cluster correlations.