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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Neurogenesis Based in Quantum Mechanics Governed by Ions Interaction and Coherent States

Huber Nieto–Chaupis

Published Dec 6, 2022• 1 citation
Publisher

Abstract

Under the assumption that neurogenesis is a fully random process then some laws such as the ones belonging to quantum mechanics might be applied. In this paper, it is assumed that along the process of creation of new neurons, some phases such as proliferation and creation would consist in quantum coherent states in the sense that the formation of new neurons might be synchronized. Thus the ion-dependence formalism of quantum mechanics is employed, with a special emphasis to the usage of evolution operator Hamiltonian governed by Coulomb forces.

DOI
10.1109/bibm55620.2022.9995047

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Under the assumption that neurogenesis is a fully random process then some laws such as the ones belonging to quantum mechanics might be applied. In this paper, it is assumed that along the process of creation of new neurons, some phases such as proliferation and creation would consist in quantum coherent states in the sense that the formation of new neurons might be synchronized. Thus the ion-dependence formalism of quantum mechanics is employed, with a special emphasis to the usage of evolution operator Hamiltonian governed by Coulomb forces.
Inspired on the idea of Mathew P. A. Fisher about the possibility that quantum mechanics is playing some basic roles on the biochemical processes in the brain, this paper has opted by develop a theory based on the description of neurogenesis by using the formalism and concepts of quantum mechanics. To accomplish this, it is assumed that each one of the main steps towards to the formation of neuron is done in a probabilistic manner, so that the probability amplitude is calculated. Concretely, the main steps: proliferation, migration and differentiation are expressed in terms of amplitudes. Also, the evolution operator depends only on electrical interactions fom the fact of abundance of ions so that attractive or repulsion forces might be crucial to the completion of subprocesses previous to maturation of neuron. Based on the quantum mechanics probabilities simulations are presented and discussed.
The process of synaptic transmission is projected onto the territory of quantum mechanics. Thus, the electric interactions are seen as probabilistic events more than a deterministic one. In this manner, the neurotransmitter would obey quantum laws and their dynamics is governed by the evolution operator. Thus, synapse seen as an action of propagation would encompasses well to that the spatial propagator or Green function that might model the dyanamics of neurotransmitters along the cleft. This turns out to be instantaneous in coherence to the concept of neural transmission. Indeed under the quantum mechnics scenario it is found that the eigenvalues of energies of Hamiltonian consist in electric potentials between electrically charged neurotransmitters and receptors at the ion channels.
This paper proposes a novel neuron model leveraging the principles of quantum state superposition. Traditional neuron models, based on classical probability, struggle to accurately represent the complex information processing within biological neurons. This model introduces a fundamentally different approach by modeling synaptic connections as quantum states in superposition. The propagation of neural signals is then governed by the quantum mechanical interaction of these superimposed states. We define a mathematical framework incorporating key concepts from quantum mechanics, including superposition, entanglement, and quantum measurement, to describe the dynamics of this model. The core of the model lies in the equation: $ \frac{d| \Psi(t) \rangle}{dt} = \mathcal{H} | \Psi(t) \rangle $ where ∣Ψ(t)⟩| \Psi(t) \rangle∣Ψ(t)⟩ represents the quantum state of the neuron at time *t*, and H\mathcal{H}H is the Hamiltonian operator describing the neuron's dynamics. We explore scenarios involving multiple inputs and their influence on the superposition state. The model demonstrates the potential for enhanced information processing compared to conventional models, particularly in scenarios requiring parallel computation and robust information storage. Furthermore, we discuss the implications of this approach for understanding and potentially mimicking biological neural computation. The model presented offers a theoretical foundation for exploring quantum computation within biological systems.
This paper supplies the matter-side instance of general timelessness on standard-physics territory. It introduces no new equations and alters no predictive machinery of special relativity, general relativity, quantum mechanics, or quantum field theory. The claim is that the lawful structure underlying bound matter is timeless in a sense that standard physics already grants. Stationary states have no time-dependence in their structural content. Energy eigenstates evolve by an overall phase that contributes to no observable. Ground states are the canonical case of time-independent structure. The Hamiltonian governing a bound system is itself time-independent under the conditions in which bound states are defined. These features are not interpretive additions. They are how standard quantum mechanics describes bound systems. Null proper time is the photon's route to timeless lawful structure; stationary-state time-independence is the atom's. Timelessness is the general condition of lawful structure; spacetime registration is the special case. The present paper draws the ontological consequence the formalism already permits. The bound state is in force as a timeless lawful structure, registered in spacetime when invoked by lawful coupling with an admissible interactant. Persistence is the non-lapse of the lawful structure across timelike ordering. The c-bound governs invocation propagation, not any hidden internal process. No traveler-substance occupies the interval between detector interactions. No granule sits beneath the registration. No component action is required to maintain the state. The hydrogen ground state is the worked example. The electron is not orbiting; standard physics has said so for a century. The present paper draws the further ontological consequence: the atom is not running any internal process at all. The kinetic energy expectation value is a structural identity of the stationary state, not a time-average of dynamical activity. The formal ingredients are standard. The ontological reading is not. Standard quantum mechanics supplies lawful bound-state structure; the present paper reads matter as the registered availability of that structure rather than as primitive substance beneath registration. The bound system is real; the lawful structure is real; the registrations are real. What is not required, and not supplied by the formalism, is a substrate sitting beneath the registrations as their hidden material. The atom is the lawful availability of a stationary structure, registered when called on. The claim is interpretive only.
This paper introduces the Quantum Mechanics Simulation of Neurons Model (QMNN), a novel approach to simulating neuronal behavior based on fundamental principles of quantum mechanics. Traditional artificial neural networks (ANNs) often struggle to accurately replicate the complex dynamics observed in biological neurons due to their reliance on classical computational methods. QMNN leverages concepts such as superposition and entanglement, key features of quantum mechanics, to represent neuronal states and interactions. The model employs a discrete quantum system, where neuronal activity is represented by quantum states, allowing for inherent parallelism and potentially capturing phenomena currently inaccessible to classical neural simulations. This work details the core concepts of QMNN, outlining its mathematical formulation, key components, and potential advantages over conventional ANN approaches. We present a simplified model of a single neuron utilizing a quantum bit (qubit) to represent its state. The model's equations reflect the Schrödinger equation and the evolution of quantum states under influence of external stimuli. The simulation explores scenarios demonstrating the potential for increased computational efficiency and the ability to model phenomena like quantum coherence within neuronal networks. Further research will focus on scaling the model to multi-neuron networks and investigating the model's applicability to various neurological conditions.