Robust Near-Critical Dynamics in Heavy-Tailed Neural Networks

TL;DR

This paper demonstrates that heavy-tailed synaptic connections in neural networks create a robust near-critical regime, with a phase transition characterized by unique scaling laws and automatic gain control, unlike traditional Gaussian models.

Contribution

It introduces a dynamical mean-field theory for Cauchy-distributed couplings, revealing a robust near-critical phase in heavy-tailed neural networks, extending to symmetric alpha-stable inputs.

Findings

Heavy-tailed synapses lead to a continuous phase transition.

Collective activity scales with the square root of criticality distance.

Activity-dependent noise suppresses gain while maintaining susceptibility.

Abstract

The criticality hypothesis posits that biological neural networks operate near a phase transition, yet within standard Gaussian mean-field theories this regime appears fragile and requires fine tuning. Here we show that heavy-tailed synaptic connectivity provides a robust alternative mechanism. By developing a dynamical mean-field theory for Cauchy-distributed couplings, we reduce the macroscopic dynamics to a one-dimensional gradient flow with a global Lyapunov potential. The resulting theory exhibits a continuous phase transition in which collective activity grows with the square root of the distance to criticality, and static susceptibility diverges only as the square root rather than linearly as in Gaussian mean-field theories. This structure gives rise to an emergent automatic gain control: activity-dependent noise fluctuations suppress the effective gain at high activity levels while preserving high susceptibility near the critical point. Extending this mechanism to general symmetric $\alpha$-stable inputs, we identify heavy-tailed synapses as a key microscopic origin of robust near-critical dynamics in disordered neural circuits.