Topology-dependent coalescence controls scaling exponents in finite networks

TL;DR

This paper demonstrates that network topology and size significantly influence observed critical exponents in neural avalanches, revealing that apparent criticality can arise from structured networks' quasi-critical dynamics rather than true critical points.

Contribution

It shows how topology-dependent coalescence explains the variation in critical exponents, emphasizing the importance of considering network structure in criticality assessments.

Findings

Empirical exponents vary with network topology and size.

Structured networks can exhibit scale-free behavior with mean-field exponents.

Quasi-critical dynamics can mimic true criticality in small networks.

Abstract

Multiple studies of neural avalanches across different data modalities led to the prominent hypothesis that the brain operates near a critical point. The observed exponents often indicate the mean-field directed-percolation universality class, leading to the fully-connected or random network models to study the avalanche dynamics. However, the cortical networks have distinct non-random features and spatial organization that is known to affect the critical exponents. Here we show that distinct empirical exponents arise in networks with different topology and depend on the network size. In particular, we find apparent scale-free behavior with mean-field exponents appearing as quasi-critical dynamics in structured networks. This quasi-critical dynamics cannot be easily discriminated from an actual critical point in small networks. We find that the local coalescence in activity dynamics can explain the distinct exponents. Therefore, both topology and system size should be considered when assessing criticality from empirical observables.