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.
Highlights
A plethora of phenomena in nature can be effectively described by networks
If the brain learns by maximizing the Mutual Information between stimuli and response, or by updating the internal model of probabilities by using Bayesian techniques, or by minimizing the free-energy, it provides little insight about the dynamical mechanisms appearing in brain networks when evolved based on such principles
In our work we propose a working hypothesis supported by numerical simulations that brain dynamical networks evolve based on the principle of the maximization of their internal information flow capacity, i.e. the upper bound for the information transferred per time unit between any two nodes
Summary
A plethora of phenomena in nature can be effectively described by networks. Neuroscientists have used tools for the analysis of complex networks that help realize even more deeply the functionality and structure of the brain. Small-world topology has been identified at the cellular-network scale in functional cortical neural circuits in mammals [8] and in the nervous system of the nematode Caenorhabditis elegans (C.elegans) [9]. This topology seems to be relevant for the brain function because it is affected by diseases [10], normal ageing, and by pharmacological blockade of dopamine neurotransmission [11]
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