title: Hierarchical networks, power laws, and neuronal avalanches.
abstract: We show that in networks with a hierarchical architecture, critical dynamical behaviors can emerge even when the underlying dynamical processes are not critical. This finding provides explicit insight into current studies of the brain's neuronal network showing power-law avalanches in neural recordings, and provides a theoretical justification of recent numerical findings. Our analysis shows how the hierarchical organization of a network can itself lead to power-law distributions of avalanche sizes and durations, scaling laws between anomalous exponents, and universal functions-even in the absence of self-organized criticality or critical points. This hierarchy-induced phenomenon is independent of, though can potentially operate in conjunction with, standard dynamical mechanisms for generating power laws.
authors.affiliation: Department of Computer Science, International Computer Science Institute, University of California Berkeley, California 94704, USA.
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Friedman
authors.forename: Adam S
authors.initials: AS
authors.lastname: Landsberg
mesh_terms: D001921:Brain / Q000166:cytology / Q000502:physiology*; D004738:Engineering / Q000379:methods; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology*; D012919:Social Behavior; D060756:Social Networking; D013598:Systems Theory; D013997:Time Factors
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