The modeling of neurobiologic brain functions such as, cognition and consciousness are important research challenges having applications to other disciplines such as, bio-inspired soft computing and, adaptive distributed computing systems. The functions of brain and distributed computing structures have resemblances. This paper proposes a computational model of cognitive function and consciousness by using algebraic methods. Furthermore, the quantum mechanical basis of the cognitive model is formulated by using linear Hermitian. A set of choice functions is computed following the neurodynamics. The neuronal excitation is modeled by using trigonometric wave functions closely matching the neuronal firing. This paper illustrates an analytical approach to model neuronal excitation and, associated cognitive functions having quantum mechanical behaviour.
The modeling of neurobiologic brain functions such as, cognition and consciousness are important research challenges having applications to other disciplines such as, bio-inspired soft computing and, adaptive distributed computing systems. The functions of brain and distributed computing structures have resemblances. This paper proposes a computational model of cognitive function and consciousness by using algebraic methods. Furthermore, the quantum mechanical basis of the cognitive model is formulated by using linear Hermitian. A set of choice functions is computed following the neurodynamics. The neuronal excitation is modeled by using trigonometric wave functions closely matching the neuronal firing. This paper illustrates an analytical approach to model neuronal excitation and, associated cognitive functions having quantum mechanical behaviour.
Quantum consciousness concerns both the possible role that quantum mechanics has for understanding consciousness as well as the role that consciousness has for interpreting quantum physics. Quantum brain theories hypothesize that quantum physical processes occur within and between the neurons of the brain and have important effects on cognition as well as consciousness. Quantum cognition is a growing new field in cognitive science concerned with the application of the mathematical principles of quantum theory to human judgment and decision-making behaviour. What do all these theories have to do with each other? Quantum theories of consciousness have more to say about quantum physics than cognitive psychology and conscious experiences. Quantum brain theories have not been sufficiently ‘scaled up’ to provide clear implications for how quantum physical processes generate more complex cognition. Quantum cognition theories have avoided addressing fundamental issues about consciousness and have remained agnostic with respect to the quantum brain hypothesis. This article will address the problem of connecting these ideas together by connecting quantum cognition to the other two topics.
Biological neuronal networks represent a fundamental component of complex systems, including the brain. Simulating these networks accurately and efficiently is a significant challenge, largely due to the intricate, non-linear interactions between neurons. Traditional simulation methods, often relying on numerical integration techniques like the Hodgkin-Huxley model, are computationally intensive, particularly when scaling to large networks. This paper proposes a novel approach to neuronal network simulation inspired by quantum mechanics, specifically leveraging concepts of superposition and entanglement. We introduce a framework that utilizes quantum-inspired algorithms to represent and evolve neuronal states, aiming to dramatically reduce computational overhead. The core idea involves mapping neuronal dynamics onto a quantum system, where neurons are represented by qubits, and synaptic connections are modeled as quantum gates. The simulation process then utilizes quantum algorithms to evolve the system, effectively capturing the parallel and interconnected nature of neuronal networks. We present a mathematical formulation of the simulation process, focusing on the evolution equation for neuronal states. We explore key concepts like wave function collapse and measurement in the context of neuronal network simulation, offering a potential pathway for more realistic and efficient modeling of biological neural systems. The ultimate goal is to provide a computationally tractable method for simulating large-scale neuronal networks, leading to improved understanding of brain function and potentially facilitating the development of advanced neurotechnologies. The presented framework offers a distinct alternative to conventional methods, promising significant advantages in terms of speed and scalability.
Quantum-like (QL) modeling, one of the outcomes of the quantum information revolution, extends quantum theory methods beyond physics to decision theory and cognitive psychology. While effective in explaining paradoxes in decision making and effects in cognitive psychology, such as conjunction, disjunction, order, and response replicability, it lacks a direct link to neural information processing in the brain. This study bridges neurophysiology, neuropsychology, and cognitive psychology, exploring how oscillatory neuronal networks give rise to QL behaviors. Inspired by the computational power of neuronal oscillations and quantum-inspired computation (QIC), we propose a quantum-theoretical framework for coupling of cognition/decision making and neural oscillations - {\it QL oscillatory cognition.} This is a step, may be very small, towards clarification of the relation between mind and matter and the nature of perception and cognition. We formulate four conjectures within QL oscillatory cognition and in principle they can checkedAsanoexperimentally. But such experimental tests need further theoretical and experimental elaboration. One of the conjectures (Conjecture 4) is on resolution of the binding problem by exploring QL states entanglement generated by the oscillations in a few neuronal networks. Our findings suggest that fundamental cognitive processes align with quantum principles, implying that humanoid AI should process information using quantum-theoretic laws. Quantum-Like AI (QLAI) can be efficiently realized via oscillatory networks performing QIC.
The question of whether quantum mechanical phenomena play a functional role in brain activity and the emergence of consciousness remains one of the most controversial yet intellectually stimulating debates at the intersection of physics, neuroscience, and philosophy of mind. This review systematically examines the principal theoretical frameworks proposing quantum-level mechanisms in neural computation, including the Orchestrated Objective Reduction (Orch OR) hypothesis by Penrose and Hameroff, the quantum brain hypothesis of Stapp, quantum coherence models in microtubules, and quantum field theories of consciousness. We critically evaluate the current state of empirical evidence—including recent quantum biology findings, the challenge of decoherence in warm, biological systems, and experimental observations of quantum effects in biological systems—alongside computational models that bridge quantum formalism and neural network dynamics. Furthermore, we discuss the implications of quantum cognition models for understanding perception, decision-making, and memory. Despite significant scientific skepticism, emerging evidence from quantum biology and advanced neuroimaging technologies provides tentative support for quantum processes in neural substrates. We conclude by identifying critical gaps in current knowledge and outlining future research directions that may resolve the debate, including proposals for experimental paradigms using quantum sensing technologies applied to neural tissue. This review aims to serve as a foundational reference for researchers across disciplines approaching the quantum mind hypothesis with scientific rigor.
Quantum-like (QL) modeling, one of the outcomes of the quantum information revolution, extends quantum theory methods beyond physics to decision theory and cognitive psychology. While effective in explaining paradoxes in decision making and effects in cognitive psychology, such as conjunction, disjunction, order, and response replicability, it lacks a direct link to neural information processing in the brain. This study bridges neurophysiology, neuropsychology, and cognitive psychology, exploring how oscillatory neuronal networks give rise to QL behaviors. Inspired by the computational power of neuronal oscillations and quantum-inspired computation (QIC), we propose a quantum-theoretical framework for coupling of cognition/decision making and neural oscillations - QL oscillatory cognition. This is a step, may be very small, toward clarification of the relation between mind and matter and the nature of perception and cognition. We formulate four conjectures within QL oscillatory cognition and in principle they can be checked experimentally. But such experimental tests need further theoretical and experimental elaboration. One of the conjectures (Conjecture 4) is on resolution of the binding problem by exploring QL states entanglement generated by the oscillations in a few neuronal networks. Our findings suggest that fundamental cognitive processes align with quantum principles, implying that humanoid AI should process information using quantum-theoretic laws. Quantum-Like AI (QLAI) can be efficiently realized via oscillatory networks performing QIC.
The quantum theory of mind allows a shift from the Mind/Brain metaphysical problem to the Quantum Mind/Classical Brain scientific problem: how could systematic and coherent quantum processes - assumed to be the physical support of our conscious experiences - occur in a macroscopic system as the brain? I discuss a solution based on a neurobiological model that attributes to quantum computation in intra-neuronal signal-transduction pathways the role of directly supporting phenomenal experience. In this model, quantum coherence is created or prepared by classical mechanisms as recurrent neuronal networks, oscillatory synchrony and gated membrane channels, thus avoiding common theoretical constraints for the existence of quantum communication and computation, as ultra-cold temperatures and quasi-isolation from the environment.