Successive patterns of activation and deactivation in local areas of the brain indicate the mechanisms of information processing in the brain. It is possible that this process can be optimized by principles, such as the maximization of mutual information and the minimization of energy consumption. In the present paper, I showed evidence for this argument by demonstrating the correlation among mutual information, the energy of the activation, and the activation patterns. Modeling the information processing based on the functional connectome datasets of the human brain, I simulated information transfer in this network structure. Evaluating the statistical quantities of the different network states, I clarified the correlation between them. First, I showed that mutual information and network energy have a close relationship, and that the values are maximized and minimized around a same network state. This implies that there is an optimal network state in the brain that is organized according to the principles regarding mutual information and energy. On the other hand, the evaluation of the network structure revealed that the characteristic network structure known as the criticality also emerges around this state. These results imply that the characteristic features of the functional network are also affected strongly by these principles. To assess the functional aspects of this state, I investigated the output activation patterns in response to random input stimuli. Measuring the redundancy of the responses in terms of the number of overlapping activation patterns, the results indicate that there is a negative correlation between mutual information and the redundancy in the patterns, suggesting that there is a trade-off between communication efficiency and robustness due to redundancy, and the principles of mutual information and network energy are important to network formation and its function in the human brain.

中文翻译：

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互信息最大化和能量最小化原则影响大脑中大规模网络的激活模式

大脑局部区域的连续激活和失活模式表明了大脑中信息处理的机制。这个过程有可能通过互信息最大化和能耗最小化等原则来优化。在本文中，我通过证明互信息、激活能量和激活模式之间的相关性来证明这一论点。基于人脑的功能连接组数据集对信息处理进行建模，我模拟了这种网络结构中的信息传递。评估不同网络状态的统计量，我澄清了它们之间的相关性。首先，我展示了互信息和网络能量有密切的关系，并且这些值围绕相同的网络状态最大化和最小化。这意味着大脑中有一个最佳网络状态，它是根据有关互信息和能量的原则组织起来的。另一方面，对网络结构的评估表明，被称为临界性的特征网络结构也围绕这种状态出现。这些结果意味着功能网络的特征也受到这些原则的强烈影响。为了评估这种状态的功能方面，我研究了响应随机输入刺激的输出激活模式。根据重叠激活模式的数量测量响应的冗余，