Covariance Plasticity and Regulated Criticality

TL;DR

This paper explores how Hebbian covariance plasticity can regulate neural networks to operate near criticality, balancing activity levels and oscillations, which may be fundamental to brain function.

Contribution

It demonstrates that covariance plasticity can drive neural networks to a critical boundary, unifying different activity regimes under a common regulation mechanism.

Findings

Network converges to a critical state at the boundary of activity and oscillation regimes.

The regulation mechanism is effective under broad conditions.

The system exhibits three modes: high activity, low activity, and oscillation.

Abstract

We propose that a regulation mechanism based on Hebbian covariance plasticity may cause the brain to operate near criticality. We analyze the effect of such a regulation on the dynamics of a network with excitatory and inhibitory neurons and uniform connectivity within and across the two populations. We show that, under broad conditions, the system converges to a critical state lying at the common boundary of three regions in parameter space; these correspond to three modes of behavior: high activity, low activity, oscillation.