Entropy, mutual information, and systematic measures of structured spiking neural networks
Abstract
The aim of this paper is to investigate various information-theoretic measures, including entropy, mutual information, and some systematic measures that based on mutual information, for a class of structured spiking neuronal network. In order to analyze and compute these information-theoretic measures for large networks, we coarse-grained the data by ignoring the order of spikes that fall into the same small time bin. The resultant coarse-grained entropy mainly capture the information contained in the rhythm produced by a local population of the network. We first proved that these information theoretical measures are well-defined and computable by proving the stochastic stability and the law of large numbers. Then we use three neuronal network examples, from simple to complex, to investigate these information-theoretic measures. Several analytical and computational results about properties of these information-theoretic measures are given.
Keywords
Cite
@article{arxiv.1912.01507,
title = {Entropy, mutual information, and systematic measures of structured spiking neural networks},
author = {Wenjie Li and Yao Li},
journal= {arXiv preprint arXiv:1912.01507},
year = {2019}
}