Abstract

Background: Previous work by the author has developed a statistical mechanics of neocortical interactions (SMNI) describing short-term memory (STM) and electroencephalographic (EEG) data, using the author’s Adaptive Simulated Annealing (ASA) to fit such data. A closed-form time-dependent derivation of a quantum path-integral for calcium-ion wave-packets generated at tripartite neuron-astrocyte-neuron synaptic sites was used for interactions with Classical-physics SMNI. SMNI has used path-integral calculations with the author’s numerical PATHINT code, to understand short-term memory (STM).

Objective: Better fits to EEG data will be a strong indication that STM neocortical information processing in some subjects involves synchronous interaction between Quantum calcium-ion waves and Classical neural firings (also synchronous, but just among the neurons).

Method: Comparison will be made with Quantum computers and the author’s N-dimensional path-integral algorithm for quantum systems, qPATHINT, which runs on Classical computers: Quantum computers propagating calcium-ion wave-packets will be synchronized with Classical computers propagating SMNI via PATHINT. qPATHINT for calcium wave-packets also will be synchronized with PATHINT for SMNI, both running on Classical computers.

Results: If successful, there should be modest improvement of cost/objective functions used to fit EEG data with these models.

Conclusion: Quantum and Classical computers can run synchronized computations to investigate Quantum-Classical phenomena such as interactions between calcium ions and synchronous neural firings. Classical computers may deliver similar results using qPATHINT synchronized with PATHINT.

Keywords: Path Integral; Quantum Systems; Neuron Astrocyte Interactions; Multiscale Modeling; Quantum-Classical Computers

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Citation

Citation: Lester Ingber. "Quantum-Classical Interactions: Calcium Ions and Synchronous Neural Firings 2.1 (2020): 13-20.

Copyright

Copyright: © 2020 Lester Ingber. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.