| Information flow among neural networks with Bayesian estimation |
| |
| 作者姓名： | LI Yan LI XiaoLi OUYANG GaoXiang GUAN XinPing |
| |
| 作者单位： | Centre for Networking Control and Bioinformatics, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China |
| |
| 基金项目： | Supported by the National Natural Science Foundation of China （Grant No. 60575012）.We thank the Epilepsy Centre at the Freiburg University in Germany for providing the EEG data. |
| |
| 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.
|
| 关 键 词： | 同步性 耦合 贝叶斯定理 癫痫 |
| 收稿时间： | 12 November 2006 |
| 修稿时间： | 2006-11-122007-03-02 |
Information flow among neural networks with Bayesian estimation |
| |
| LI Yan LI XiaoLi OUYANG GaoXiang GUAN XinPing.Information flow among neural networks with Bayesian estimation[J].Chinese Science Bulletin,2007,52(14):2006-2011. |
| |
| Authors: | Li Yan Li XiaoLi Ouyang GaoXiang Guan XinPing |
| |
| Institution: | (1) Centre for Networking Control and Bioinformatics, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China |
| |
| Abstract: | 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. |
| |
| Keywords: | phase synchronization coupling direction conditional mutual information Bayesian estimation epileptic EEG |
| 本文献已被 维普 万方数据 SpringerLink 等数据库收录！ |
| 点击此处可从《中国科学通报(英文版)》浏览原始摘要信息 |
| 点击此处可从《中国科学通报(英文版)》下载免费的PDF全文 |
