Do Brain Networks Evolve by Maximizing their Information Flow Capacity?

TL;DR

This paper hypothesizes that brain networks evolve by maximizing their internal information flow capacity, supported by simulations showing evolved networks resemble real brain networks in behavior and structure.

Contribution

It introduces a novel hypothesis that brain network evolution is driven by maximizing information flow capacity, validated through numerical simulations and comparisons with biological networks.

Findings

Evolved neural networks match behaviors of real brain networks.

Synchronization levels decrease during evolution, indicating a no Hebbian-like process.

Networks with maximum information flow capacity resemble biological brain networks.

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.