2007/02/05 by Peter Krawitz, Krawitz, Peter, Ilya Shmulevich +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Neuroscience · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Gene Regulatory Network Analysis #Neural dynamics and brain function #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.cond-mat/0702114
openalex publication_date 2007/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The 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.