Information transfer of an Ising model on a brain network
Abstract
We implement the Ising model on a structural connectivity matrix describing the brain at a coarse scale. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the number of incoming links.
Keywords
Cite
@article{arxiv.1302.3869,
title = {Information transfer of an Ising model on a brain network},
author = {Daniele Marinazzo and Mario Pellicoro and Guorong Wu and Leonardo Angelini and Jesus M Cortes and Sebastiano Stramaglia},
journal= {arXiv preprint arXiv:1302.3869},
year = {2013}
}