# Entropy, mutual information, and systematic measures of structured spiking neural networks

arXiv: https://arxiv.org/abs/1912.01507 · HTML: https://mlsanity.com/paper/1912.01507/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Supervised Parameter Estimation of Neuron Populations from Multiple Firing Events](https://mlsanity.com/paper/2210.01767/) (2022)

## Abstract (arXiv)

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.