Modeling Quantum Mechanical Observers via Neural-Glial Networks

TL;DR

This paper presents a neural-glial network model incorporating quantum mechanics to explore how the brain functions as a quantum observer, linking neural dynamics with quantum superposition retention.

Contribution

It introduces a novel neural-glial network model with a quantum Hamiltonian, demonstrating conditions under which the brain can maintain quantum superpositions as an observer.

Findings

Superposition retention time matches microscopic quantum systems.

Classical information entropy is used as a criterion for observation.

Large neural-glial networks can eliminate degrees of freedom via large N reduction.

Abstract

We investigate the theory of observers in the quantum mechanical world by using a novel model of the human brain which incorporates the glial network into the Hopfield model of the neural network. Our model is based on a microscopic construction of a quantum Hamiltonian of the synaptic junctions. Using the Eguchi-Kawai large N reduction, we show that, when the number of neurons and astrocytes is exponentially large, the degrees of freedom of the dynamics of the neural and glial networks can be completely removed and, consequently, that the retention time of the superposition of the wave functions in the brain is as long as that of the microscopic quantum system of pre-synaptics sites. Based on this model, the classical information entropy of the neural-glial network is introduced. Using this quantity, we propose a criterion for the brain to be a quantum mechanical observer.