# Robustness of Griffiths effects in homeostatic connectome models

## TL;DR

This study demonstrates that Griffiths effects in large-scale brain network models are robust under certain modifications, with implications for understanding critical dynamics in neural systems.

## Contribution

It extends previous work by testing the robustness of Griffiths phases with refractory states and temporal disorder in a large human connectome model.

## Key findings

- Griffiths phase persists with refractory states and weak disorder.
- Temporal disorder reduces the Griffiths phase, leading to a mean-field transition.
- Inhibitory interactions restore activity avalanche distributions to experimental ranges.

## Abstract

I provide numerical evidence for the robustness of the Griffiths phase (GP) reported previously in dynamical threshold model simulations on a large human brain network with N=836733 connected nodes. The model, with equalized network sensitivity, is extended in two ways: introduction of refractory states or by randomized time dependent thresholds. The non-universal power-law dynamics in an extended control parameter region survives these modifications for a short refractory state and weak disorder. In case of temporal disorder the GP shrinks and for stronger heterogeneity disappears, leaving behind a mean-field type of critical transition. Activity avalanche size distributions below the critical point decay faster than in the original model, but the addition of inhibitory interactions sets it back to the range of experimental values.