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Gradient Echo Quantum Memory in Warm Atomic Vapor
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Quantum-like behavior without quantum physics III : Logic and memory.

Stephen Selesnick1, Gualtiero Piccinini2

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

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|October 17, 2019
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Summary
This summary is machine-generated.

This study explores quantum-like neural networks, revealing that synchronicity enhances memory binding and storage. The model simulates pattern completion, with failures mirroring conditions like Alzheimer's disease.

Keywords:
MemoryNetworksPattern completionSequent calculusStorageSynchronicityTsien’s power-of-two law

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Area of Science:

  • Computational Neuroscience
  • Quantum Cognition

Background:

  • Previous work established quantum-like neural networks.
  • Understanding inferential and memory functions is crucial.

Purpose of the Study:

  • Investigate inferential and memory functions of quantum-like neural networks.
  • Formalize network rules using a logical apparatus.
  • Explore memory storage mechanisms.

Main Methods:

  • Employed Gentzen sequent calculus to codify combinatory rules.
  • Developed an algorithmic fragment simulating pattern completion.
  • Tested the model by analyzing memory deficit simulations.

Main Results:

  • Formal proof that synchronicity promotes neural binding and memory storage.
  • Algorithmic fragment successfully simulates pattern completion.
  • Model spontaneously generates 'power-of-two' wiring and computational logic observed in brain circuits.
  • Failure analysis links model deficits to Alzheimer's, schizophrenia, and autism.

Conclusions:

  • Synchronicity is a key factor in neural memory.
  • The model provides a computational framework for understanding memory functions.
  • Quantum-like neural networks offer insights into brain computation and pathologies.