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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
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.
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.
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