Dynamical Coarse Graining of Large Scale-Free Boolean networks

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dc.creatorWang, Wen-Xu
dc.creatorYan, Gang
dc.creatorRen, Jie
dc.creatorWang, Bing-Hong
dc.date2006-03-07
dc.date.accessioned2026-07-07T07:02:12Z
dc.date.available2026-07-07T07:02:12Z
dc.descriptionWe present a renormalization-grouplike method performed in the state space for detecting the dynamical behaviors of large scale-free Boolean networks, especially for the chaotic regime as well as the edge of chaos. Numerical simulations with different coarse-graining level show that the state space networks of scale-free Boolean networks follow universal power-law distributions of in and out strength, in and out degree, as well as weight. These interesting results indicate scale-free Boolean networks still possess self-organized mechanism near the edge of chaos in the chaotic regime. The number of state nodes as a function of biased parameter for distinct coarse-graining level also demonstrates that the power-law behaviors are not the artifact of coarse-graining procedure. Our work may also shed some light on the investigation of brain dynamics.
dc.description5 pages, 6 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0603167
dc.identifierhttp://arxiv.org/abs/cond-mat/0603167
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/108520
dc.subjectDisordered Systems and Neural Networks
dc.titleDynamical Coarse Graining of Large Scale-Free Boolean networks
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