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Coevolution of information processing and topology in hierarchical adaptive random Boolean networks
2016
European Physical Journal B : Condensed Matter Physics
Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node
doi:10.1140/epjb/e2015-60530-6
fatcat:ma6ts7tbpvdzrcj7eovyquwte4