Abstract

Info

Title:: Identification of biological neuron network connection structures by dynamic Bayesian network method based on conditional mutual information

Author(s):: REN Jingwen1; 2; DONG Chaoyi1; 2; ZHU Meijia1; 2; BAI Penghui1; 2; ZHAO Xiaoyi1; 2; MA Shuang1; 2; JIA Tingting1; 2; 1. School of Electric Power, Inner Mongolia University of Technology, Hohhot 010080,China 2. Inner Mongolia Key Laboratory of Electromechanical Control, Hohhot 010051, China

Keywords:: Keywords: biological neural network Bayesian network conditional mutual information integrate-and-fire model

Abstract:: Abstract: The accurate identification of biological neural network (BNN) connection structures helps to further understand their network behaviors and functions, and contributes to constructing biologically realistic artificial intelligent networks with more optimized structures. Dynamic Bayesian network method based on conditional mutual information is proposed for accurately identifying the connection structures of BNN. Spike neural network is firstly constructed by integrate-and-fire principle, and the multi-channel dynamic response data are obtained after network simulation. Based on the obtained data set, the conditional mutual information between neuron nodes are calculated, and the connection between nodes is assessed by comparing the calculated results with the given threshold δ. Finally, dynamic Bayesian network connection structures are identified. The experimental results reveal that dynamic Bayesian network method based on conditional mutual information has a high identification accuracy for BNN.