title: Self-tuned critical anti-Hebbian networks.
abstract: It is widely recognized that balancing excitation and inhibition is important in the nervous system. When such a balance is sought by global strategies, few modes remain poised close to instability, and all other modes are strongly stable. Here we present a simple abstract model in which this balance is sought locally by units following "anti-Hebbian" evolution: all degrees of freedom achieve a close balance of excitation and inhibition and become "critical" in the dynamical sense. At long time scales, a complex "breakout" dynamics ensues in which different modes of the system oscillate between prominence and extinction; the model develops various long-tailed statistical behaviors and may become self-organized critical.
authors: Laboratory of Mathematical Physics, Rockefeller University, 1230 York Avenue, New York, New York 10065, USA.
authors: Marcelo O
authors: MO
authors: Magnasco
authors: Oreste
authors: O
authors: Piro
authors: Guillermo A
authors: GA
authors: Cecchi
mesh_terms: D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*