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Evidence for Quasicritical Brain Dynamics.

Leandro J Fosque¹, Rashid V Williams-García², John M Beggs¹

¹Department of Physics, Indiana University, Bloomington, Indiana 47405, USA.

Physical Review Letters

|March 22, 2021

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The brain

Area of Science:

Computational Neuroscience
Complex Systems

Background:

The cerebral cortex is theorized to operate near a critical point, a state conducive to complex information processing.
However, experimental estimation of critical exponents, which define universality classes, yields inconsistent results across species, individuals, and conditions.
Despite variations, these exponents consistently adhere to a dynamical scaling relation, posing a theoretical puzzle.

Purpose of the Study:

To reconcile the observed variability in critical exponents with the theoretical framework of brain dynamics.
To introduce and validate the theory of quasicriticality as an explanation for the cortex's near-critical behavior.
To predict how cortical dynamics change under external stimuli according to quasicriticality.

Main Methods:

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Developed and applied the theory of quasicriticality to model cortical dynamics.

Utilized computational simulations to explore predictions of the quasicriticality theory.

Analyzed experimental data to validate theoretical predictions against empirical observations.

Main Results:

The theory of quasicriticality explains the paradoxical variability in critical exponents.
Quasicriticality predicts a departure from criticality along a Widom line as external stimuli increase.
Exponents decrease in absolute value but maintain approximate adherence to a dynamical scaling relation.

Conclusions:

Quasicriticality provides a unifying framework for understanding brain dynamics near criticality.
The theory accounts for observed variations in critical exponents while preserving dynamical scaling.
Further testable predictions are proposed, offering avenues for future experimental validation.