Spectral modes of network dynamics reveal increased informational complexity near criticality
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- dc.contributor.author Arsiwalla, Xerxes D.
- dc.contributor.author Martinez Mediano, Pedro Antonio
- dc.contributor.author Verschure, Paul F. M. J.
- dc.date.accessioned 2023-06-26T05:59:59Z
- dc.date.available 2023-06-26T05:59:59Z
- dc.date.issued 2017
- dc.description.abstract What does the informational complexity of dynamical networked systems tell us about intrinsic mechanisms and functions of these complex systems? Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions. While several numerical schemes for estimating network integrated information exist, it is instructive to pursue an analytic approach that computes integrated information as a function of network weights. Our formulation of integrated information uses a Kullback-Leibler divergence between the multi-variate distribution on the set of network states versus the corresponding factorized distribution over its parts. Implementing stochastic Gaussian dynamics, we perform computations for several prototypical network topologies. Our findings show increased informational complexity near criticality, which remains consistent across network topologies. Spectral decomposition of the system’s dynamics reveals how informational complexity is governed by eigenmodes of both, the network’s covariance and adjacency matrices. We find that as the dynamics of the system approach criticality, high integrated information is exclusively driven by the eigenmode corresponding to the leading eigenvalue of the covariance matrix, while sub-leading modes get suppressed. The implication of this result is that it might be favorable for complex dynamical networked systems such as the human brain or communication systems to operate near criticality so that efficient information integration might be achieved.
- dc.description.sponsorship 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).
- dc.format.mimetype application/pdf
- dc.identifier.citation Arsiwalla XD, Mediano P.A.M, Verschure PFMJ. Spectral modes of network dynamics reveal increased informational complexity near criticality. Procedia Computer Science. 2017;108:119-28. DOI: 10.1016/j.procs.2017.05.241
- dc.identifier.doi http://dx.doi.org/10.1016/j.procs.2017.05.241
- dc.identifier.issn 1877-0509
- dc.identifier.uri http://hdl.handle.net/10230/57328
- dc.language.iso eng
- dc.publisher Elsevier
- dc.relation.ispartof Procedia Computer Science. 2017;108:119-28
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/341196
- dc.rights © 2017 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
- dc.subject.keyword Network dynamics
- dc.subject.keyword Complexity measures
- dc.subject.keyword Information theory
- dc.title Spectral modes of network dynamics reveal increased informational complexity near criticality
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion