Dynamical and Statistical Criticality in a Model of Neural Tissue

TL;DR

This paper introduces a neural tissue model where local anti-Hebbian learning leads to a state of dynamical and statistical criticality, resulting in complex oscillatory behavior and self-organized criticality.

Contribution

It presents a novel neural tissue model where local anti-Hebbian rules induce dynamical and statistical criticality, unlike previous models with global balance strategies.

Findings

Modes oscillate around the instability line

System exhibits anomalous statistical behaviors

Achieves self-organized criticality

Abstract

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.