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Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks | | Piotr J. Gorski
; Agnieszka Czaplicka
; Janusz A. Holyst
; | | Date: |
9 Feb 2015 | | Abstract: | 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 information, mean edge information as well
as a mean node degree as functions of model parameters and demonstrate HARBN’s
ability to describe complex hierarchical systems. | | Source: | arXiv, 1502.3338 | | Services: | Forum | Review | PDF | Favorites | |
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