title: Analytical investigation of self-organized criticality in neural networks.
abstract: Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.
authors.affiliation: Bernstein Center for Computational Neuroscience, Haus 2, Philippstrasse 13, Berlin, Germany. felix.droste@bccn-berlin.de
authors.forename: Felix
authors.initials: F
authors.lastname: Droste
authors.forename: Anne-Ly
authors.initials: AL
authors.lastname: Do
authors.forename: Thilo
authors.initials: T
authors.lastname: Gross
mesh_terms: D000818:Animals; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D013569:Synapses / Q000502:physiology*