Self-organized criticality in neural networks from activity-based rewiring

TL;DR

This paper demonstrates that a simple local rewiring rule based on activity can lead neural networks to self-organize into a critical state, reproducing neural avalanche statistics without parameter tuning.

Contribution

It introduces a minimal neural network model that self-organizes to criticality through activity-based rewiring, aligning with observed neural avalanche exponents.

Findings

Network reaches criticality with power-law avalanche distributions.

Critical state is robust to noise and initial conditions.

Model aligns with dynamical scaling theory and neural data.

Abstract

Neural systems process information in a dynamical regime between silence and chaotic dynamics. This has lead to the criticality hypothesis which suggests that neural systems reach such a state by self-organizing towards the critical point of a dynamical phase transition. Here, we study a minimal neural network model that exhibits self-organized criticality in the presence of stochastic noise using a rewiring rule which only utilizes local information. For network evolution, incoming links are added to a node or deleted, depending on the node's average activity. Based on this rewiring-rule only, the network evolves towards a critcal state, showing typical power-law distributed avalanche statistics. The observed exponents are in accord with criticality as predicted by dynamical scaling theory, as well as with the observed exponents of neural avalanches. The critical state of the model is reached autonomously without need for parameter tuning, is independent of initial conditions, is robust under stochastic noise, and independent of details of the implementation as different variants of the model indicate. We argue that this supports the hypothesis that real neural systems may utilize similar mechanisms to self-organize towards criticality especially during early developmental stages.