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 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.

27 pages, 8 figures, 2 tables, supporting_information included, published in PLOS Computational Biology

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• Synchronization in complex networks
• Scale-free brain functional networks
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• Dynamic Range in the C.elegans Brain Network
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• Mesoscopic architecture enhances communication across the Macaque connectome revealing structure-function correspondence in the brain
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• Spike timing-dependent plasticity induces non-trivial topology in the brain
• Influence of autapses on synchronisation in neural networks with chemical synapses