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QLCA and Entangled States as Single-Neuron Activity Generators.

Yehuda Roth1

  • 1Oranim Academic College, Science Department, Kiryat Tiv'on, Israel.

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|June 21, 2021
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Summary
This summary is machine-generated.

This study introduces a quantum-like cellular automaton (QLCA) model to describe neuron activity and consciousness. The research explores how quantum-like measurements within this framework can explain neuronal decision-making processes.

Keywords:
QCA operatorscollapseeigenconceptsentanglementquantum cellular automataquantum observersingle neuron activity

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Area of Science:

  • Computational Neuroscience
  • Quantum Information Theory
  • Cognitive Science

Background:

  • Neurons receive input information via dendrites, influencing their firing behavior.
  • Previous work linked cellular automata (CA) descriptions of neurons to entangled states.
  • Understanding the computational principles underlying neuronal function is crucial.

Purpose of the Study:

  • To provide a quantum-like description of neuron activity using quantum-like cellular automaton (QLCA) concepts.
  • To propose a model linking quantum-like measurement to consciousness.
  • To implement QLCA formalism for describing single neuron activity.

Main Methods:

  • Review and presentation of entanglement concepts within QLCA terminology.
  • Development of a toy model to illustrate QLCA theory adapted to neuron activity.
  • Implementation of QLCA formalism for single neuron modeling.

Main Results:

  • A novel quantum-like framework for describing neuron activity and decision-making is presented.
  • The study establishes a connection between quantum-like measurement and consciousness.
  • The proposed QLCA model offers a new perspective on neuronal information processing.

Conclusions:

  • QLCA formalism provides a suitable framework for modeling single neuron activity.
  • The quantum-like measurement concept offers a potential pathway to understanding consciousness.
  • This approach bridges computational neuroscience and quantum information theory.