Preprint Brief Report Version 1 Preserved in Portico This version is not peer-reviewed

Parameterization of Quantum Interactions

Version 1 : Received: 11 November 2023 / Approved: 13 November 2023 / Online: 13 November 2023 (08:51:19 CET)
Version 2 : Received: 17 November 2023 / Approved: 17 November 2023 / Online: 17 November 2023 (07:56:21 CET)

How to cite: Ingber, L. Parameterization of Quantum Interactions. Preprints 2023, 2023110784. https://doi.org/10.20944/preprints202311.0784.v1 Ingber, L. Parameterization of Quantum Interactions. Preprints 2023, 2023110784. https://doi.org/10.20944/preprints202311.0784.v1

Abstract

Background: Previous papers have developed a statistical mechanics of neocortical interactions (SMNI) fit to short-term memory and EEG data (Ingber, 2018). Adaptive Simulated Annealing (ASA) was used for all fits to data. A numerical path-integral for quantum systems, qPATHINT, was used. Objective: The quantum path-integral for Calcium ions was used to derive a closed-form analytic solution at arbitrary time. The quantum effects is parameterized here, whereas the previous 2018 paper applied a nominal ratio of 1/2 to these effects. Method: Methods of mathematical-physics for optimization and for path integrals in classical and quantum spaces are used. The quantum path-integral is used to derive a closed-form analytic solution at arbitrary time, and is used to calculate interactions with classical-physics SMNI interactions among scales. Results: The mathematical-physics and computer parts of the study are successful, in that three cases with Subjects (blind to this author) after 1,000,000 visits to the cost function gave: Subject-07 = 0.04, Subject-08 = 0.55, and Subject-09 = 1.00. All other 9 Subjects gave 0.

Keywords

path integral; quantum systems; multiscale modeling; supercomputer

Subject

Biology and Life Sciences, Neuroscience and Neurology

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