Analytical investigation of self-organized criticality in neural networks

Details Analytical investigation of self-organized criticality in neural networks Analytical investigation of self-organized criticality in neural networks

2012-03-22

arxiv.org/abs/1203.4942v1

Nonlinear Sciences

Adaptation and Self-Organizing Systems

7 pages, 5 figures

Scientific paper

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.

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