Griffiths phases and the stretching of criticality in brain networks

Abstract

Hallmarks of criticality, such as power-laws and scale invariance, have been empirically found in cortical networks and it has been conjectured that operating at criticality entails functional advantages, such as optimal computational capabilities, memory, and large dynamical ranges. As critical behavior requires a high degree of fine tuning to emerge, some type of self-tuning mechanism needs to be invoked. Here we show that, taking into account the complex hierarchical-modular architecture of cortical networks, the singular critical point is replaced by an extended critical-like region which corresponds --in the jargon of statistical mechanics-- to a Griffiths phase. Using computational and analytical approaches, we find Griffiths phases in synthetic hierarchical networks and also in empirical brain networks such as the human connectome and the caenorhabditis elegans one. Stretched critical regions, stemming from structural disorder, yield enhanced functionality in a generic way, facilitating the task of self-organizing, adaptive, and evolutionary mechanisms selecting for criticality.

Final version. A misprint in Equation (2) was corrected. Supplementary Information included

References in corpus (6)

• Power-law distributions in empirical data
• Emergent complex neural dynamics
• Dynamical synapses causing self-organized criticality in neural networks
• Rare region effects at classical, quantum, and non-equilibrium phase transitions
• Griffiths phases on complex networks
• Criticality of spreading dynamics in hierarchical cluster networks without inhibition

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