Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Media and Networks

Quantum Effects in Synaptic Activity: Challenging the Deterministic Model of Neural Processes

Published Jul 22, 2024• 0 citations
Publisher

Abstract

In this study, we explore the impact of quantum effects on synaptic activity and their implications for the deterministic model of neural processes. By simulating synaptic events in a large-scale neural network, we demonstrate how small probabilistic quantum events can accumulate and introduce variability in neural behavior. Our model incorporates Dirac delta functions to simulate sudden changes in synaptic probabilities, creating a non-linear, seesaw effect in the cumulative synaptic activity curve. The results reveal that even minimal quantum effects, when scaled to the vast number of synapses in the human brain, can lead to significant dissociations in neural activity. These findings challenge the traditional deterministic view, suggesting that neural processes—and by extension, decision-making and movements—may inherently incorporate elements of randomness. This probabilistic perspective aligns with theories of free will and underscores the need for further empirical research to validate the role of quantum mechanics in neuroscience. Our study provides a theoretical framework for understanding the complex interplay between quantum effects and neural dynamics, opening new avenues for exploring the nature of consciousness and the fundamental principles governing reality.

DOI
10.33140/ijmn.02.07.03

Related papers

Quantum Entanglement Modeling in Neural Simulation

Jincheng Zhang

This paper investigates the potential of quantum entanglement to significantly enhance the accuracy of neural simulations, particularly within complex neural networks. The core claim posits that leveraging quantum entanglement can provide a novel approach to modeling neuronal behavior, moving beyond traditional simulation methods. The proposed mechanism involves mapping synaptic connections within neurons to quantum bits (Qubits) and utilizing entanglement to perform parallel computations across multiple synaptic connections. Quantum measurements are then employed to simulate the probabilistic nature of neuronal firing. This research explores a fundamental shift – directly applying quantum mechanical principles to neural simulation rather than treating them as mere heuristic optimization tools. The theoretical framework developed presents a pathway for exploring the impact of quantum effects on neural processing and potentially leading to more accurate and efficient models of biological neural networks. This work lays the groundwork for future investigations into the quantum computational properties of the brain.

•Sep 1, 2026•0 citations

Quantum Simulation of Synaptic Plasticity Mechanisms in Neural Circuits

Kayra Ügümü

Synaptic plasticity—the capacity of neural circuits to strengthen, weaken, or reconfigure connections in response to activity—is fundamental to learning, memory formation, and adaptive behavior. Classical computational models have advanced the understanding of plasticity, but they struggle to fully capture the high-dimensional, nonlinear, and quantum-biophysical processes that shape synaptic dynamics at molecular and network scales. This study explores a quantum simulation framework for modeling synaptic plasticity mechanisms by leveraging variational quantum circuits, Hamiltonian-based learning rules, and quantum state representations of neuronal interactions. The proposed approach encodes synaptic weight updates, spike-timing–dependent plasticity (STDP), and Hebbian/anti-Hebbian processes within quantum operators capable of expressing richer correlation structures than classical models. Preliminary results demonstrate that quantum simulations can represent multi-synapse entanglement, non-Markovian memory traces, and complex attractor transitions that approximate biological synaptic behaviors with enhanced precision. These findings highlight the emerging role of quantum computational tools in uncovering new aspects of neural adaptability and advancing next-generation computational neuroscience.

Next frontier.•Dec 1, 2025•0 citations

Do mental events cause neural events analogously to the probability fields of quantum mechanics?

John Carew Eccles

If non-material mental events, such as the intention to carry out an action, are to have an effective action on neural events in the brain, it has to be at the most subtle and plastic level of these events. In the first stage of our enquiry an introduction to conventional synaptic theory leads on to an account of the manner of operation of the ultimate synaptic units. These units are the synaptic boutons that, when excited by an all-or-nothing nerve impulse, deliver the total contents of a single synaptic vesicle, not regularly, but probabilistically. This quantal emission of the synaptic transmitter molecules (about 5000-10 000) is the elementary unit of the transmission process from one neuron to another. In the second stage this refined physiological analysis leads on to an account of the ultrastructure of the synapse, which gives clues as to the manner of its unitary probabilistic operation. The essential feature is that the effective structure of each bouton is a paracrystalline presynaptic vesicular grid with about 50 vesicles, which acts probabilistically in vesicular (quantal) release. In the third stage it is considered how a non-material mental event, such as an intention to move, could influence the subtle probabilistic operations of synaptic boutons. On the biological side, attention is focused on the paracrystalline presynaptic vesicular grids as the targets for non-material mental events. On the physical side, attention is focused on the probabilistic fields of quantum mechanics which carry neither mass nor energy, but which nevertheless can exert effective action at microsites. The new light on the mind-brain problem came from the hypothesis that the non-material mental events, the 'World 2' of Popper, relate to the neural events of the brain (the 'World 1' of matter and energy) by actions in conformity with quantum theory. This hypothesis that mental events act on probabilistic synaptic events in a manner analogous to the probability fields of quantum mechanics seems to open up an immense field of scientific investigation both in quantum physics and in neuroscience.

Proceedings of the Royal Society B Biological Sciences•May 22, 1986•186 citations

Probabilistic Neural Synapse Based in Quantum Mechanics

Huber Nieto–Chaupis

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.

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)•Dec 7, 2022•1 citation

Quantum Entanglement-Based Simulation of Information Encoding and Decoding in Biological Systems

Jincheng Zhang

This research investigates the potential of utilizing quantum entanglement to simulate information encoding and decoding mechanisms within biological neural networks. The core premise is that quantum phenomena, specifically entanglement, may play a more significant role in biological neuronal processing than previously acknowledged. This work proposes a novel approach employing quantum bits (qubits) to represent individual neurons, with connections between neurons established through quantum entanglement. The simulation aims to mimic the processes of synaptic transmission and signal propagation observed in biological neural networks. Mathematical formulations describe the evolution of the entangled qubit states under influence of simulated neuronal inputs, and the measurement processes are modeled to represent the observed output signals. This research offers a theoretical framework for exploring the interplay between quantum mechanics and biological neural systems, potentially leading to a deeper understanding of the fundamental principles governing brain function. The study utilizes a discrete-time simulation approach, allowing for a detailed analysis of the dynamics of the entangled neural network. Key performance indicators, such as the fidelity of information transfer and the robustness of the simulated network, are explored. The results suggest that quantum entanglement can provide a viable model for simulating complex neural processes, warranting further investigation into the potential for quantum computing to revolutionize our understanding of the brain.

•Aug 28, 2026•0 citations

A Stochastic Quantum Neural Network Model for Ai

Gautier-Edouard Filardo, Thibaut Heckmann

Artificial intelligence (AI) has drawn significant inspiration from neuroscience to develop artificial neural network (ANN) models. However, these models remain constrained by the Von Neumann architecture and struggle to capture the complexity of the biological brain. Quantum computing, with its foundational principles of superposition, entanglement, and unitary evolution, offers a promising alternative approach to modeling neural dynamics. This paper explores the possibility of a neuro-quantum model of the brain by introducing a stochastic quantum approach that incorporates random fluctuations of neuronal processing within a quantum framework. We propose a mathematical formalization of stochastic quantum neural networks (QNNS), where qubits evolve according to stochastic differential equations inspired by biological neuronal processes. We also discuss challenges related to decoherence, qubit stability, and implications for AI and computational neuroscience.

q-bio.NCCross-listedarXiv:2511.11609•Nov 4, 2025•0 citations
In this study, we explore the impact of quantum effects on synaptic activity and their implications for the deterministic model of neural processes. By simulating synaptic events in a large-scale neural network, we demonstrate how small probabilistic quantum events can accumulate and introduce variability in neural behavior. Our model incorporates Dirac delta functions to simulate sudden changes in synaptic probabilities, creating a non-linear, seesaw effect in the cumulative synaptic activity curve. The results reveal that even minimal quantum effects, when scaled to the vast number of synapses in the human brain, can lead to significant dissociations in neural activity. These findings challenge the traditional deterministic view, suggesting that neural processes—and by extension, decision-making and movements—may inherently incorporate elements of randomness. This probabilistic perspective aligns with theories of free will and underscores the need for further empirical research to validate the role of quantum mechanics in neuroscience. Our study provides a theoretical framework for understanding the complex interplay between quantum effects and neural dynamics, opening new avenues for exploring the nature of consciousness and the fundamental principles governing reality.
This paper investigates the potential of quantum entanglement to significantly enhance the accuracy of neural simulations, particularly within complex neural networks. The core claim posits that leveraging quantum entanglement can provide a novel approach to modeling neuronal behavior, moving beyond traditional simulation methods. The proposed mechanism involves mapping synaptic connections within neurons to quantum bits (Qubits) and utilizing entanglement to perform parallel computations across multiple synaptic connections. Quantum measurements are then employed to simulate the probabilistic nature of neuronal firing. This research explores a fundamental shift – directly applying quantum mechanical principles to neural simulation rather than treating them as mere heuristic optimization tools. The theoretical framework developed presents a pathway for exploring the impact of quantum effects on neural processing and potentially leading to more accurate and efficient models of biological neural networks. This work lays the groundwork for future investigations into the quantum computational properties of the brain.
Synaptic plasticity—the capacity of neural circuits to strengthen, weaken, or reconfigure connections in response to activity—is fundamental to learning, memory formation, and adaptive behavior. Classical computational models have advanced the understanding of plasticity, but they struggle to fully capture the high-dimensional, nonlinear, and quantum-biophysical processes that shape synaptic dynamics at molecular and network scales. This study explores a quantum simulation framework for modeling synaptic plasticity mechanisms by leveraging variational quantum circuits, Hamiltonian-based learning rules, and quantum state representations of neuronal interactions. The proposed approach encodes synaptic weight updates, spike-timing–dependent plasticity (STDP), and Hebbian/anti-Hebbian processes within quantum operators capable of expressing richer correlation structures than classical models. Preliminary results demonstrate that quantum simulations can represent multi-synapse entanglement, non-Markovian memory traces, and complex attractor transitions that approximate biological synaptic behaviors with enhanced precision. These findings highlight the emerging role of quantum computational tools in uncovering new aspects of neural adaptability and advancing next-generation computational neuroscience.
If non-material mental events, such as the intention to carry out an action, are to have an effective action on neural events in the brain, it has to be at the most subtle and plastic level of these events. In the first stage of our enquiry an introduction to conventional synaptic theory leads on to an account of the manner of operation of the ultimate synaptic units. These units are the synaptic boutons that, when excited by an all-or-nothing nerve impulse, deliver the total contents of a single synaptic vesicle, not regularly, but probabilistically. This quantal emission of the synaptic transmitter molecules (about 5000-10 000) is the elementary unit of the transmission process from one neuron to another. In the second stage this refined physiological analysis leads on to an account of the ultrastructure of the synapse, which gives clues as to the manner of its unitary probabilistic operation. The essential feature is that the effective structure of each bouton is a paracrystalline presynaptic vesicular grid with about 50 vesicles, which acts probabilistically in vesicular (quantal) release. In the third stage it is considered how a non-material mental event, such as an intention to move, could influence the subtle probabilistic operations of synaptic boutons. On the biological side, attention is focused on the paracrystalline presynaptic vesicular grids as the targets for non-material mental events. On the physical side, attention is focused on the probabilistic fields of quantum mechanics which carry neither mass nor energy, but which nevertheless can exert effective action at microsites. The new light on the mind-brain problem came from the hypothesis that the non-material mental events, the 'World 2' of Popper, relate to the neural events of the brain (the 'World 1' of matter and energy) by actions in conformity with quantum theory. This hypothesis that mental events act on probabilistic synaptic events in a manner analogous to the probability fields of quantum mechanics seems to open up an immense field of scientific investigation both in quantum physics and in neuroscience.
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 research investigates the potential of utilizing quantum entanglement to simulate information encoding and decoding mechanisms within biological neural networks. The core premise is that quantum phenomena, specifically entanglement, may play a more significant role in biological neuronal processing than previously acknowledged. This work proposes a novel approach employing quantum bits (qubits) to represent individual neurons, with connections between neurons established through quantum entanglement. The simulation aims to mimic the processes of synaptic transmission and signal propagation observed in biological neural networks. Mathematical formulations describe the evolution of the entangled qubit states under influence of simulated neuronal inputs, and the measurement processes are modeled to represent the observed output signals. This research offers a theoretical framework for exploring the interplay between quantum mechanics and biological neural systems, potentially leading to a deeper understanding of the fundamental principles governing brain function. The study utilizes a discrete-time simulation approach, allowing for a detailed analysis of the dynamics of the entangled neural network. Key performance indicators, such as the fidelity of information transfer and the robustness of the simulated network, are explored. The results suggest that quantum entanglement can provide a viable model for simulating complex neural processes, warranting further investigation into the potential for quantum computing to revolutionize our understanding of the brain.
Artificial intelligence (AI) has drawn significant inspiration from neuroscience to develop artificial neural network (ANN) models. However, these models remain constrained by the Von Neumann architecture and struggle to capture the complexity of the biological brain. Quantum computing, with its foundational principles of superposition, entanglement, and unitary evolution, offers a promising alternative approach to modeling neural dynamics. This paper explores the possibility of a neuro-quantum model of the brain by introducing a stochastic quantum approach that incorporates random fluctuations of neuronal processing within a quantum framework. We propose a mathematical formalization of stochastic quantum neural networks (QNNS), where qubits evolve according to stochastic differential equations inspired by biological neuronal processes. We also discuss challenges related to decoherence, qubit stability, and implications for AI and computational neuroscience.