Time-Scale and Noise Optimality in Self-Organized Critical Adaptive Networks

Details Time-Scale and Noise Optimality in Self-Organized Critical Adaptive Networks Time-Scale and Noise Optimality in Self-Organized Critical Adaptive Networks

2011-10-25

arxiv.org/abs/1110.5433v3

Nonlinear Sciences

Adaptation and Self-Organizing Systems

4 pages, 6 figures; several changes in exposition and focus on applications in revised version

Scientific paper

Recent studies have shown that adaptive networks driven by simple local rules can organize into "critical" global steady states, providing another framework for self-organized criticality (SOC). We focus on the important convergence to criticality and show that noise and time-scale optimality are reached at finite values. This is in sharp contrast to the previously believed optimal zero noise and infinite time scale separation case. Furthermore, we discover a noise induced phase transition for the breakdown of SOC. We also investigate each of the three new effects separately by developing models. These models reveal three generically low-dimensional dynamical behaviors: time-scale resonance (TR), a new simplified version of stochastic resonance - which we call steady state stochastic resonance (SSR) - as well as noise-induced phase transitions.

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