Statistical Mechanics of Neocortical Interactions: Testing Theories with Multiple Imaging Data

Abstract

To test theories of neocortical processing of macroscopic phenomena, e.g., attention and consciousness, based on relatively microscopic neocortical interactions, e.g., synaptic and neuronal interactions, both theoretical and experimental designs must account for the nonlinear nonequilibrium multivariate stochastic nature of these phenomena. Statistical mechanics of neocortical interactions (SMNI) is one theoretical description that has been tested on short-term memory and electroencephalographic data, and is proposed as a framework to test other theories and mechanisms of neocortical activity. Copula analysis of multivariate stochastic systems, previously developed for financial markets, is an important approach to developing a portfolio of physiological indicators to create cost/objective functions to fit experimental data, e.g., multiple synchronous imaging data.