"The Physics of Brain Science: Quasicriticality, An Organizing Principle"
Empirical evidence suggests that living neural networks operate near a continuous phase transition, conjectured to be an optimal point for information storage and processing. Applying theoretical approaches, however, is challenging since vital features of neural networks present numerous obstacles to the applicability of traditional statistical physics tools, many of which have not yet been adapted to neuroscience. I will describe a simple cellular automaton model which allows for the characterization of the out-of-equilibrium transition and demonstrates an explicit symmetry breaking due to sensory stimuli. The resulting testable quasicriticality hypothesis that living neural networks cannot operate at optimality—and instead operate along a line of relative optimality depending on the influence of their sensory environment—can be applied to understand neurological disorders like autism. Finally, I present my vision for how nonequilibrium statistical physics can be applied to further develop our understanding of brain dynamics and what tools need to be developed.
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