Testing the critical brain hypothesis using a phenomelogical renormalization group

Abstract

We present a systematic study to test a recently introduced phenomenological renormalization group, proposed to coarse-grain data of neural activity from their correlation matrix. The approach allows, at least in principle, to establish whether the collective behavior of the network of spiking neurons is described by a non-Gaussian critical fixed point. We test this renormalization procedure in a variety of models focusing in particular on the contact process, which displays an absorbing phase transition at $\lambda ={\lambda }_c$ between a silent and an active state. We find that the results of the coarse-graining do not depend on the presence of long-range interactions, but some scaling features persist in the super-critical system up to a distance of $10\%$ from ${\lambda }_c$. Our results provide insights on the possible subtleties that one needs to consider when applying such phenomenological approaches directly to data to infer signatures of criticality.

arXiv comment9 pages, 8 figures

References in corpus (18)

• Emergent complex neural dynamics
• Scale-free correlations in bird flocks
• Optimal Dynamical Range of Excitable Networks at Criticality
• Are biological systems poised at criticality?
• Discovering Phase Transitions with Unsupervised Learning
• Colloquium: Criticality and dynamical scaling in living systems
• Maximum entropy models for antibody diversity
• Power-law statistics and universal scaling in the absence of criticality
• Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
• Information-based fitness and the emergence of criticality in living systems
• Coarse--graining, fixed points, and scaling in a large population of neurons
• Dynamic scaling in natural swarms
• Renormalization Group and Probability Theory
• Neutral theory and scale-free neural dynamics
• Reweighting in nonequilibrium simulations
• Homeostatic plasticity and emergence of functional networks in a whole-brain model at criticality
• Quasi-stationary simulation of the contact process
• Critical behavior of the contact process on small-world networks

Cited by in corpus (12)

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• Disentangling the critical signatures of neural activity
• Tackling the subsampling problem to infer collective properties from limited data
• The emergence of scale-free fires in Australia
• Information-driven transitions in projections of underdamped dynamics
• Information propagation in Gaussian processes on multilayer networks
• Criticality at work: scaling in the mouse cortex enhances performance
• Phase transitions in in vivo or in vitro populations of spiking neurons belong to different universality classes
• Mutual information in changing environments: non-linear interactions, out-of-equilibrium systems, and continuously-varying diffusivities
• Renormalization group for spectral collapse in random matrices with power-law variance profiles