Stochastic quorum percolation and noise focusing in neuronal networks

TL;DR

This paper introduces a minimal stochastic model called quorum percolation to explain noise-induced nucleation and propagation of activity in neuronal networks, with implications for understanding phase transitions and other spreading phenomena.

Contribution

The paper presents a novel stochastic quorum percolation model that explains noise focusing and phase transitions in neuronal activity, linking network topology to dynamics.

Findings

Reproduces first and second order phase transitions in neuronal activity.

Shows network topology significantly influences noise build-up and propagation.

Provides a framework applicable to disease and rumor spreading models.

Abstract

Recent experiments have shown that the spontaneous activity of young dissociated neuronal cultures can be described as a process of highly inhomogeneous nucleation and front propagation due to the localization of noise activity, i.e., noise focusing. However, the basic understanding of the mechanisms of noise build-up leading to the nucleation remain an open fundamental problem. Here we present a minimal dynamical model called stochastic quorum percolation that can account for the observed phenomena, while providing a robust theoretical framework. The model reproduces the first and second order phase--transitions of bursting dynamics and neuronal avalanches respectively, and captures the profound effect metric correlations in the network topology can have on the dynamics. The application of our results to other systems such as in the propagation of infectious diseases and of rumors is discussed.