Simulation of chaotic EEG patterns with a dynamic model of the olfactory system
Biological Cybernetics
Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]
ACM Transactions on Mathematical Software (TOMS)
Application of statistical mechanics methodology to term-structure bond-pricing models
Mathematical and Computer Modelling: An International Journal
Statistical mechanics of combat with human factors
Mathematical and Computer Modelling: An International Journal
Mathematical comparison of combat computer models to exercise data
Mathematical and Computer Modelling: An International Journal
Very fast simulated re-annealing
Mathematical and Computer Modelling: An International Journal
Genetic Algorithms and Very Fast Simulated Reannealing: A comparison
Mathematical and Computer Modelling: An International Journal
Multiple scales of statistical physics of the neocortex: Application to electroencephalography
Mathematical and Computer Modelling: An International Journal
Data mining and knowledge discovery via statistical mechanics in nonlinear stochastic systems
Mathematical and Computer Modelling: An International Journal
Simulated annealing: Practice versus theory
Mathematical and Computer Modelling: An International Journal
Path-integral evolution of chaos embedded in noise: Duffing neocortical analog
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ideas By Statistical Mechanics (ISM)
Journal of Integrated Design & Process Science
Data mining and knowledge discovery via statistical mechanics in nonlinear stochastic systems
Mathematical and Computer Modelling: An International Journal
A simple options training model
Mathematical and Computer Modelling: An International Journal
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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.