Maximum Entropy Principle Analysis in Network Systems with Short-time Recordings

TL;DR

This paper demonstrates that maximum entropy principle analysis can effectively reconstruct network state distributions from short-term recordings, especially under asynchronous activity, with applications in neuronal population coding.

Contribution

It shows that MEP analysis is applicable to short recordings in network systems, expanding its practical utility in neuroscience research.

Findings

MEP analysis can reconstruct network state distributions from short recordings.

Application verified with Hodgkin-Huxley neuronal networks and experimental data.

Supports investigation of neuronal coding properties with limited data.

Abstract

In many realistic systems, maximum entropy principle (MEP) analysis provides an effective characterization of the probability distribution of network states. However, to implement the MEP analysis, a sufficiently long-time data recording in general is often required, e.g., hours of spiking recordings of neurons in neuronal networks. The issue of whether the MEP analysis can be successfully applied to network systems with data from short recordings has yet to be fully addressed. In this work, we investigate relationships underlying the probability distributions, moments, and effective interactions in the MEP analysis and then show that, with short recordings of network dynamics, the MEP analysis can be applied to reconstructing probability distributions of network states under the condition of asynchronous activity of nodes in the network. Using spike trains obtained from both Hodgkin-Huxley neuronal networks and electrophysiological experiments, we verify our results and demonstrate that MEP analysis provides a tool to investigate the neuronal population coding properties, even for short recordings.