Title:
|
The Global dynamical complexity of the human brain network
|
Author:
|
Arsiwalla, Xerxes D.; Verschure, Paul F. M. J.
|
Abstract:
|
How much information do large brain networks integrate as a whole over the sum of
their parts? Can the dynamical complexity of such networks be globally quantified in
an information-theoretic way and be meaningfully coupled to brain function? Recently,
measures of dynamical complexity such as integrated information have been proposed.
However, problems related to the normalization and Bell number of partitions
associated to these measures make these approaches computationally infeasible for
large-scale brain networks. Our goal in this work is to address this problem. Our
formulation of network integrated information is based on the Kullback-Leibler
divergence between the multivariate distribution on the set of network states versus
the corresponding factorized distribution over its parts. We find that implementing the
maximum information partition optimizes computations. These methods are
well-suited for large networks with linear stochastic dynamics. We compute the
integrated information for both, the system’s attractor states, as well as non-stationary
dynamical states of the network. We then apply this formalism to brain networks to
compute the integrated information for the human brain’s connectome. Compared to
a randomly re-wired network, we find that the specific topology of the brain generates
greater information complexity. |
Abstract:
|
This work has been supported by the European Research Council’s CDAC project: “The Role of Consciousness in Adaptive Behavior: A Combined Empirical, Computational and Robot based Approach” (ERC-2013- ADG 341196). |
Subject(s):
|
-Brain networks -Neural dynamics -Complexity measures |
Rights:
|
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
http://creativecommons.org/licenses/by/4.0/ |
Document type:
|
Article Article - Published version |
Published by:
|
SpringerOpen
|
Share:
|
|