Stochastic cellular automata model of neural networks

TL;DR

This paper introduces a stochastic cellular automata model for neural networks that captures complex behaviors like phase transitions, oscillations, and stochastic resonance, highlighting how noise influences neural activity.

Contribution

It presents a novel stochastic dynamical model of neural networks with complex architectures, revealing phase transitions and oscillatory behaviors driven by noise.

Findings

Global neural oscillations emerge at a threshold noise level.

Stochastic resonance precedes phase transitions.

Oscillations occur even in small neural groups of 50 neurons.

Abstract

We propose a stochastic dynamical model of noisy neural networks with complex architectures and discuss activation of neural networks by a stimulus, pacemakers and spontaneous activity. This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of behavior. We show that if spontaneous activity (noise) reaches a threshold level then global neural oscillations emerge. Stochastic resonance is a precursor of this dynamical phase transition. These oscillations are an intrinsic property of even small groups of 50 neurons.