  A series of papers 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. While not useful to yield insights at the single neuron level,
SMNI has demonstrated its capability in describing large-scale properties of
short-term memory and electroencephalographic (EEG) systematics. The necessity
of including nonlinear and stochastic structures in this development has been
stressed. Sets of EEG and evoked potential data were fit, collected to
investigate genetic predispositions to alcoholism and to extract brain
signatures of short-term memory. Adaptive Simulated Annealing (ASA), a global
optimization algorithm, was used to perform maximum likelihood fits of
Lagrangians defined by path integrals of multivariate conditional
probabilities. Canonical momenta indicators (CMI) are thereby derived for
individual's EEG data. The CMI give better signal recognition than the raw
data, and can be used to advantage as correlates of behavioral states. These
results give strong quantitative support for an accurate intuitive picture,
portraying neocortical interactions as having common algebraic or physics
mechanisms that scale across quite disparate spatial scales and functional or
behavioral phenomena, i.e., describing interactions among neurons, columns of
neurons, and regional masses of neurons.
