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Quantum-like behavior without quantum physics II. A quantum-like model of neural network dynamics.

S A Selesnick1, Gualtiero Piccinini2

  • 1Department of Mathematics and Computer Science, University of Missouri - St. Louis, St. Louis, Missouri, 63121, USA. selesnick@mindspring.com.

Journal of Biological Physics
|June 28, 2018
PubMed
Summary

This study extends quantum-like principles to neural networks, offering a novel dynamical theory for neural computation. The research provides insights into brain pathologies and memory retrieval, analyzing neuron motifs for structural integrity.

Keywords:
InterneuronsMemoryNetworksNeuronsQuasispin models

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Area of Science:

  • Computational neuroscience
  • Theoretical neuroscience
  • Quantum cognition

Background:

  • Previous work established quantum-like behavior in simple neuron clusters.
  • Existing models often focus on biophysical details rather than information processing.

Purpose of the Study:

  • To extend the quantum-like approach to neural networks.
  • To develop a dynamical theory for neural computation.
  • To provide a novel mathematical foundation for neural dynamics.

Main Methods:

  • Developing a dynamical theory for neural networks.
  • Abstracting from biophysical details to focus on information processing.
  • Analyzing energy-like eigenstates of three-neuron motifs.

Main Results:

  • The theory offers predictions for neurological disorders like schizophrenia, dementias, and epilepsy.
  • A model for memory retrieval mechanisms is proposed.
  • Quantum-like superposition in neuron motifs is linked to structural integrity.

Conclusions:

  • The developed theory provides a novel framework for understanding neural dynamics and computation.
  • The quantum-like approach has implications for understanding brain function and dysfunction.
  • Further analysis of neuron motifs supports the theory's principles.