Basin Entropy in Boolean Network Ensembles

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dc.creatorKrawitz, Peter
dc.creatorShmulevich, Ilya
dc.date2007-02-05
dc.date.accessioned2026-07-07T07:44:39Z
dc.date.available2026-07-07T07:44:39Z
dc.descriptionThe information processing capacity of a complex dynamical system is reflected in the partitioning of its state space into disjoint basins of attraction, with state trajectories in each basin flowing towards their corresponding attractor. We introduce a novel network parameter, the basin entropy, as a measure of the complexity of information that such a system is capable of storing. By studying ensembles of random Boolean networks, we find that the basin entropy scales with system size only in critical regimes, suggesting that the informationally optimal partition of the state space is achieved when the system is operating at the critical boundary between the ordered and disordered phases.
dc.identifierhttps://arxiv.org/abs/cond-mat/0702114
dc.identifierhttp://arxiv.org/abs/cond-mat/0702114
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/123272
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.titleBasin Entropy in Boolean Network Ensembles
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