Generic mesoscopic neural networks based on statistical mechanics of neocortical interactions

Abstract

A series of papers over the past decade [the most recent being L. Ingber, Phys. Rev. A 44, 4017 (1991)] has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions, demonstrating its capability in describing large-scale properties of short-term memory and electroencephalographic systematics. This methodology also defines an algorithm to construct a mesoscopic neural network, based on realistic neocortical processes and parameters, to record patterns of brain activity and to compute the evolution of this system. Furthermore, this algorithm is quite generic, and can be used to similarly process information in other systems, especially, but not limited to, those amenable to modeling by mathematical physics techniques alternatively described by path-integral Lagrangians, Fokker-Planck equations, or Langevin rate equations. This methodology is made possible and practical by a confluence of techniques drawn from SMNI itself, modern methods of functional stochastic calculus defining nonlinear Lagrangians, very fast simulated reannealing, and parallel-processing computation.