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Self-Tuned Critical Anti-Hebbian Networks

In this paper we present an abstract model for the "operating point" of the brain. We hypothesize that the brain poises itself at an "extensive" critical point, i.e., one in whch there is a large number of degrees of freedom poised precisely at the onset of an oscillatory instability. This dynamical criticality leads to states that are globally coupled. Published online in Physical Review Letters:

Our preprint has just appeared in the preprint archives and has been submitted for publication.


For the nervous system to work at all, a delicate balance of excitation and inhibition must be achieved. However, when such a balance is sought by global strategies, only few modes remain balanced close to instability, and all other modes are strongly stable. Here we present a simple model of neural tissue in which this balance is sought locally by neurons following `anti-Hebbian' behavior: {\sl all} degrees of freedom achieve a close balance of excitation and inhibition and become "critical" in the dynamical sense. At long timescales, the modes of our model oscillate around the instability line, so an extremely complex "breakout" dynamics ensues in which different modes of the system oscillate between prominence and extinction. We show the system develops various anomalous statistical behaviours and hence becomes self-organized critical in the statistical sense.

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