dorsal/arxiv
View SchemaStatistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG
| Authors | Lester Ingber |
|---|---|
| Categories | |
| ArXiv ID | physics/0001053 |
| URL | https://arxiv.org/abs/physics/0001053 |
| Journal | Mathematical Computer Modelling 27 (1998) 33-64 |
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.
{
"annotation_id": "01653c62-8abb-4ece-8c0a-f7ae1d46fdd5",
"date_created": "2026-03-02T18:00:29.214000Z",
"date_modified": "2026-03-02T18:00:29.214000Z",
"file_hash": "389cdb13335689932737d1659f512bb3aa89fb3b6e1ab9e55dd14d1879e5d9ef",
"private": false,
"record": {
"abstract": "A series of papers has developed a statistical mechanics of neocortical\ninteractions (SMNI), deriving aggregate behavior of experimentally observed\ncolumns of neurons from statistical electrical-chemical properties of synaptic\ninteractions. While not useful to yield insights at the single neuron level,\nSMNI has demonstrated its capability in describing large-scale properties of\nshort-term memory and electroencephalographic (EEG) systematics. The necessity\nof including nonlinear and stochastic structures in this development has been\nstressed. Sets of EEG and evoked potential data were fit, collected to\ninvestigate genetic predispositions to alcoholism and to extract brain\nsignatures of short-term memory. Adaptive Simulated Annealing (ASA), a global\noptimization algorithm, was used to perform maximum likelihood fits of\nLagrangians defined by path integrals of multivariate conditional\nprobabilities. Canonical momenta indicators (CMI) are thereby derived for\nindividual\u0027s EEG data. The CMI give better signal recognition than the raw\ndata, and can be used to advantage as correlates of behavioral states. These\nresults give strong quantitative support for an accurate intuitive picture,\nportraying neocortical interactions as having common algebraic or physics\nmechanisms that scale across quite disparate spatial scales and functional or\nbehavioral phenomena, i.e., describing interactions among neurons, columns of\nneurons, and regional masses of neurons. This paper adds to these previous\ninvestigations two important aspects, a description of how the CMI may be used\nin source localization, and calculations using previously ASA-fitted parameters\nin out-of-sample data.",
"arxiv_id": "physics/0001053",
"authors": [
"Lester Ingber"
],
"categories": [
"physics.bio-ph",
"physics.data-an",
"q-bio"
],
"journal_ref": "Mathematical Computer Modelling 27 (1998) 33-64",
"title": "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG",
"url": "https://arxiv.org/abs/physics/0001053"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "9a2888a2-4bbf-4603-86b5-3cd0cce36358",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}