February 20: Jennifer Sarah Goldman, PhD (McGill University, Montreal Neurological Institute and Hospital) will give us a talk entitled “Spectral Organization of Human Brain Activity”
A mechanistic understanding of the mind remains one of the most substantive problems in science, but the probabilistic nature of neural signals seems to occlude a deep understanding of brain activity. Between behavioral states and individual subjects, neural electromagnetic signals vary in magnitude, complexity, and spectral composition [1,2] . D.O. Hebb [3] posited that reverberations produced by transiently communicating cell assemblies could underpin neural network communication. G.M. Edelman [4] hypothesized that re-entrant, reiterated activity in recurrent neural architectures (loops) could select communicating neural assemblies. W. Singer and others have shown cognition-dependent phase coherence of disparate neural networks on conserved frequencies [5] . However, no consensus has been reached and organizing principles remain somewhat obscured in skewed, high-dimensional signals. Neural network activity is comprised of rhythmic and arrhythmic components. The arrhythmic component reflects fluctuation of brain activity at all discernible frequencies [1] , described by power spectra of the form 1/f. Such heavy-tailed, power law distributions are often found in self-organizing, complex systems [6] . Deviating above the power law, resonant modes have particular time constants, representing rhythmic brain activity with relatively more energy than expected by their frequency position in the heavy-tailed distribution, however, the organization of modes has remained highly controversial [1,7–14] . In this lecture I will present my analysis of human MEG data, leading to the identification of cognitive state dependent, self-similar structures of neural resonance revealing keystone positions for especially powerful modes with distinct mathematical relationships. Next, I will show how the definition of entropy and energy in the modes is consistent with ‘thermodynamic’ state variables, opening the possibility that definitions of ‘work’ and ‘order’ may also be useful for understanding basic functions of the brain.
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