Information flow among neural networks with Bayesian estimation  

作　　者：LI Yan LI XiaoLi OUYANG GaoXiang GUAN XinPing 

机构地区：[1]Centre for Networking Control and Bioinformatics, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

出　　处：《Chinese Science Bulletin》2007年第14期2006-2011,共6页

基　　金：Supported by the National Natural Science Foundation of China (Grant No. 60575012)

摘　　要：Estimating the interaction among neural networks is an interesting issue in neuroscience. Some methods have been proposed to estimate the coupling strength among neural networks; however, few estimations of the coupling direction (information flow) among neural networks have been attempted. It is known that Bayesian estimator is based on a priori knowledge and a probability of event occurrence. In this paper, a new method is proposed to estimate coupling directions among neural networks with conditional mutual information that is estimated by Bayesian estimation. First, this method is applied to analyze the simulated EEG series generated by a nonlinear lumped-parameter model. In comparison with the conditional mutual information with Shannon entropy, it is found that this method is more successful in estimating the coupling direction, and is insensitive to the length of EEG series. Therefore, this method is suitable to analyze a short time series in practice. Second, we demonstrate how this method can be applied to the analysis of human intracranial epileptic electroencephalogram (EEG) recordings, and to indicate the coupling directions among neural networks. Therefore, this method helps to elucidate the epileptic focus localization.Estimating the interaction among neural networks is an interesting issue in neuroscience. Some methods have been proposed to estimate the coupling strength among neural networks; however, few estimations of the coupling direction （information flow） among neural networks have been attempted. It is known that Bayesian estimator is based on a priori knowledge and a probability of event occurrence. In this paper, a new method is proposed to estimate coupling directions among neural networks with conditional mutual information that is estimated by Bayesian estimation. First, this method is applied to analyze the simulated EEG series generated by a nonlinear lumped-parameter model. In comparison with the conditional mutual information with Shannon entropy, it is found that this method is more successful in estimating the coupling direction, and is insensitive to the length of EEG series. Therefore, this method is suitable to analyze a short time series in practice. Second, we demonstrate how this method can be applied to the analysis of human intracranial epileptic electroencephalogram （EEG） recordings, and to indicate the coupling directions among neural networks. Therefore, this method helps to elucidate the epileptic focus localization.

关 键 词：同步性 耦合 贝叶斯定理 癫痫 

分 类 号：R742.1[医药卫生—神经病学与精神病学]