Structural versus dynamical origins of mean-field behavior in a self-organized critical model of neuronal avalanches

TL;DR

This study investigates whether structural connectivity or dynamical noise primarily causes mean-field behavior in neuronal avalanches, concluding that dynamical noise is the key factor in realistic cortical models.

Contribution

The paper demonstrates that dynamical noise, rather than structural features, is the main driver of mean-field behavior in neuronal avalanche models.

Findings

Structural mechanisms are insufficient to produce mean-field behavior in realistic models.

Strong dynamical noise consistently induces mean-field behavior.

Structural connectivity alone does not account for observed critical dynamics.

Abstract

Critical dynamics of cortical neurons have been intensively studied over the past decade. Neuronal avalanches provide the main experimental as well as theoretical tools to consider criticality in such systems. Experimental studies show that critical neuronal avalanches show mean-field behavior. There are structural as well as recently proposed [Phys. Rev. E 89, 052139 (2014)] dynamical mechanisms which can lead to mean-field behavior. In this work we consider a simple model of neuronal dynamics based on threshold self-organized critical models with synaptic noise. We investigate the role of high average connectivity, random long range connections, as well as synaptic noise in achieving mean-field behavior. We employ finite-size scaling in order to extract critical exponents with good accuracy. We conclude that relevant structural mechanisms responsible for mean-field behavior cannot be justified in realistic models of the cortex. However, strong dynamical noise, which can have realistic justifications, always leads to mean-field behavior regardless of the underlying structure. Our work provides a different (dynamical) origin than the conventionaly accepted (structural) mechanisms for mean-field behavior in neuronal avalanches.