中文摘要：

在神经网络之中估计相互作用是在神经科学的一个有趣的问题。Somemethods 被建议了在神经网络之中估计联合力量；然而，在神经网络之中的联合方向(信息流动) 的很少评价被尝试了。知道的 Itis 那个贝叶斯的评估者基于先验的知识和事件出现的可能性。在这篇论文，一个新方法被建议估计与被 Bayesianestimation 估计的有条件的相互的信息在神经网络之中联合方向。首先，这个方法被使用分析一个非线性的块参数模型产生的模仿的 EEG 系列。与有 Shannonentropy 的有条件的相互的信息比较，这个方法在估计联合方向是更成功的，这被发现，并且对 EEG 系列的长度感觉迟钝。因此，这个方法是合适的在实践分析一个短时间系列。第二，我们表明这个方法怎么能被用于 humanintracranial 的分析癫痫的脑电图(EEG ) 记录，并且在神经网络之中显示联合方向。因此，这个方法帮助阐明癫痫的焦点本地化。

英文摘要：

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.