Evidence for Quasicritical Brain Dynamics

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:

• 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.