142 research outputs found
Statistical mechanics of neocortical interactions: large-scale EEG influences on molecular processes
Recent calculations further supports the premise that large-scale synchronous
firings of neurons may affect molecular processes. The context is scalp
electroencephalography (EEG) during short-term memory (STM) tasks. The
mechanism considered is (SI units)
coupling, where is the momenta of free waves
the charge of in units of the electron charge, and
the magnetic vector potential of current from
neuronal minicolumnar firings considered as wires, giving rise to EEG. Data has
processed using multiple graphs to identify sections of data to which
spline-Laplacian transformations are applied, to fit the statistical mechanics
of neocortical interactions (SMNI) model to EEG data, sensitive to synaptic
interactions subject to modification by waves.Comment: Accepted for publication in Journal of Theoretical Biolog
Statistical mechanics of neocortical interactions: EEG eigenfunctions of short-term memory
This paper focuses on how bottom-up neocortical models can be developed into
eigenfunction expansions of probability distributions appropriate to describe
short-term memory in the context of scalp EEG. The mathematics of
eigenfunctions are similar to the top-down eigenfunctions developed by Nunez,
albeit they have different physical manifestations. The bottom-up
eigenfunctions are at the local mesocolumnar scale, whereas the top-down
eigenfunctions are at the global regional scale. However, as described in
several joint papers, our approaches have regions of substantial overlap, and
future studies may expand top-down eigenfunctions into the bottom-up
eigenfunctions, yielding a model of scalp EEG that is ultimately expressed in
terms of columnar states of neocortical processing of attention and short-term
memory.Comment: 5 PostScript page
Statistical mechanics of neocortical interactions: High resolution path-integral calculation of short-term memory
We present high-resolution path-integral calculations of a previously
developed model of short-term memory in neocortex. These calculations, made
possible with supercomputer resources, supplant similar calculations made in L.
Ingber, Phys. Rev. E 49, 4652 (1994), and support coarser estimates made in L.
Ingber, Phys. Rev. A 29, 3346 (1984). We also present a current experimental
context for the relevance of these calculations using the approach of
statistical mechanics of neocortical interactions, especially in the context of
electroencephalographic data.Comment: 35 PostScript pages, including 14 figure
Optimization of Trading Physics Models of Markets
We describe an end-to-end real-time S&P futures trading system. Inner-shell
stochastic nonlinear dynamic models are developed, and Canonical Momenta
Indicators (CMI) are derived from a fitted Lagrangian used by outer-shell
trading models dependent on these indicators. Recursive and adaptive
optimization using Adaptive Simulated Annealing (ASA) is used for fitting
parameters shared across these shells of dynamic and trading models
Forecasting COVID-19 with Importance-Sampling and Path-Integrals
Background Forecasting nonlinear stochastic systems most often is quite difficult without giving in to temptations to simply simplify models for the sake of permitting simple computations Objective Here two basic algorithms Adaptive Simulated Annealing ASA and path-integral codes PATHINT PATHTREE and their quantum generalizations qPATHINT qPATHTREE are suggested as being useful to fit COVID-19 data and to help predict spread or control of this pandemic Multiple variables are considered e g potentially including ethnicity population density obesity deprivation pollution race environmental temperature Method ASA and PATHINT PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience financial markets and combat analysis Results Not only can selected systems in these three disciplines be aptly modeled but results of detailed calculations have led to new results and insights not previously obtaine
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