Ferraz Mariana Sacrini Ayres, Melo-Silva Hiago Lucas Cardeal, Kihara Alexandre Hiroaki
Núcleo de Cognição e Sistemas Complexos, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brasil.
PLoS One. 2017 Sep 18;12(9):e0184367. doi: 10.1371/journal.pone.0184367. eCollection 2017.
Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.
考虑到最佳动态范围和信息处理，临界动力学被假定为大脑中神经网络的理想状态。在此，我们关注时空活动模式中编码的信息熵在临界网络中如何变化。我们采用基于分支过程的模型来研究熵如何嵌入时空模式。我们确定，临界网络的信息容量可能会根据微观参数的操纵而变化。具体而言，平均连接数控制着网络中时空模式的数量。这些发现与特定脑回路中观察到的真实神经网络的发现一致，在这些脑回路中，临界行为对于最佳动态范围响应是必要的，但不需要时空模式编码的高熵所提供的不确定性。由此，我们能够揭示，在超出临界状态的神经网络中，信息处理可以得到优化。
