Disentangling the critical signatures of neural activity

Abstract

The critical brain hypothesis has emerged as an attractive framework to understand neuronal activity, but it is still widely debated. In this work, we analyze data from a multi-electrodes array in the rat's cortex and we find that power-law neuronal avalanches satisfying the crackling-noise relation coexist with spatial correlations that display typical features of critical systems. In order to shed a light on the underlying mechanisms at the origin of these signatures of criticality, we introduce a paradigmatic framework with a common stochastic modulation and pairwise linear interactions inferred from our data. We show that in such models power-law avalanches that satisfy the crackling noise relation emerge as a consequence of the extrinsic modulation, whereas scale-free correlations are solely determined by internal interactions. Moreover, this disentangling is fully captured by the mutual information in the system. Finally, we show that analogous power-law avalanches are found in more realistic models of neural activity as well, suggesting that extrinsic modulation might be a broad mechanism for their generation.

References in corpus (7)

• Power-law distributions in empirical data
• Self-Organized Criticality model for Brain Plasticity
• Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
• Hysteresis, neural avalanches and critical behaviour near a first-order transition of a spiking neural network
• A simple unified view of branching process statistics: random walks in balanced logarithmic potentials
• Testing statistical laws in complex systems
• Mutual information disentangles interactions from changing environments

Cited by in corpus (7)

• Information propagation in multilayer systems with higher-order interactions across timescales
• Tuning transduction from hidden observables to optimize information harvesting
• Tracking the distance to criticality in systems with unknown noise
• Information-driven transitions in projections of underdamped dynamics
• Information propagation in Gaussian processes on multilayer networks
• Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
• Mutual information in changing environments: non-linear interactions, out-of-equilibrium systems, and continuously-varying diffusivities