Topology-dependent coalescence controls scaling exponents in finite networks

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, cortical networks have distinct nonrandom features and spatial organization that is known to affect 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 quasicritical dynamics in structured networks. This quasicritical 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. Published by the American Physical Society 2024