## Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG (1998)

Venue: | Mathl. Computer Modelling |

Citations: | 13 - 10 self |

### BibTeX

@ARTICLE{Ingber98statisticalmechanics,

author = {Lester Ingber},

title = {Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG},

journal = { Mathl. Computer Modelling},

year = {1998},

volume = {27},

pages = {33--64}

}

### Years of Citing Articles

### OpenURL

### Abstract

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. This paper adds to these previous investigations two important aspects, a description of how the CMI may be used in source localization, and calculations using previously ASA-fitted parameters in out-of-sample data.