Antonopoulos Chris G, Srivastava Shambhavi, Pinto Sandro E de S, Baptista Murilo S
Department of Physics (ICSMB), University of Aberdeen, Aberdeen, United Kingdom.
Departamento de Física, Universidade Estadual de Ponta Grossa, Paraná, Brazil.
PLoS Comput Biol. 2015 Aug 28;11(8):e1004372. doi: 10.1371/journal.pcbi.1004372. eCollection 2015 Aug.
We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization.
我们提出了一个基于数值模拟支持的工作假设，即大脑网络基于其内部信息流容量最大化的原则而进化。我们发现，进化网络的同步行为和信息流容量很好地再现了秀丽隐杆线虫和人类大脑动态网络中观察到的相同行为，以及具有由这些大脑网络给出的图的Hindmarsh-Rose神经元网络的行为。通过表明与秀丽隐杆线虫和人类大脑网络具有最接近图距离的神经网络是具有使信息流容量最大化的耦合强度而进化的Hindmarsh-Rose神经网络，我们有力地证明了验证我们假设的理由。令人惊讶的是，我们发现在大脑进化过程中全局神经同步水平下降，这反映了一个潜在的全局非赫布式进化过程，该过程由进化过程中某些簇的非赫布式学习行为以及神经元同步增加的簇的赫布式学习规则驱动。
