Self-organized criticality in a network of interacting neurons

TL;DR

This paper analyzes a neural network model that demonstrates self-organized criticality, showing phase transition behavior similar to directed percolation and the sand-pile model, using statistical field theory and renormalization group methods.

Contribution

It introduces a neural network model exhibiting self-organized criticality with a detailed theoretical analysis using path integrals and renormalization group techniques.

Findings

Network exhibits hysteresis and bistability.

Transitions occur via saddle-node bifurcations.

Behavior matches the sand-pile model of Bak, Tang & Wiesenfeld.

Abstract

This paper contains an analysis of a simple neural network that exhibits self-organized criticality. Such criticality follows from the combination of a simple neural network with an excitatory feedback loop that generates bistability, in combination with an anti-Hebbian synapse in its input pathway. Using the methods of statistical field theory, we show how one can formulate the stochastic dynamics of such a network as the action of a path integral, which we then investigate using renormalization group methods. The results indicate that the network exhibits hysteresis in switching back and forward between its two stable states, each of which loses its stability at a saddle-node bifurcation. The renormalization group analysis shows that the fluctuations in the neighborhood of such bifurcations have the signature of directed percolation. Thus the network states undergo the neural analog of a phase transition in the universality class of directed percolation. The network replicates precisely the behavior of the original sand-pile model of Bak, Tang & Wiesenfeld.