Critical dynamics on a large human Open Connectome network

TL;DR

This study uses large-scale simulations on a human brain network to explore how heterogeneity and inhibitory connections influence critical dynamics and power-law behaviors, revealing non-universal scaling consistent with experimental data.

Contribution

It demonstrates that heterogeneity and inhibitory links induce extended critical regions and power-law scaling in brain network models without relying on self-organized criticality.

Findings

Power-law avalanche distributions match experimental exponents.

Critical regions extend over a range of parameters.

Heterogeneity and inhibition influence critical dynamics.

Abstract

Extended numerical simulations of threshold models have been performed on a human brain network with N=836733 connected nodes available from the Open Connectome project. While in case of simple threshold models a sharp discontinuous phase transition without any critical dynamics arises, variable thresholds models exhibit extended power-law scaling regions. This is attributed to fact that Griffiths effects, stemming from the topological/interaction heterogeneity of the network, can become relevant if the input sensitivity of nodes is equalized. I have studied the effects effects of link directness, as well as the consequence of inhibitory connections. Non-universal power-law avalanche size and time distributions have been found with exponents agreeing with the values obtained in electrode experiments of the human brain. The dynamical critical region occurs in an extended control parameter space without the assumption of self organized criticality.