Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

ABSTRACT: 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 synchroni

SUBMITTER: Antonopoulos CG 

PROVIDER: S-EPMC4552863 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature