2019/11/27 by Li, Wenjie, Li, Yao
#92B20 #92C42 #94A17 #FOS: Biological sciences #FOS: Mathematics #Neurons and Cognition (q-bio.NC) #Probability (math.PR)
paper · doi:10.48550/arxiv.1912.01507
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.