The "quantum brain" proposal can revolutionize our understanding of cognition if proven valid. The core of the most common "quantum brain" mechanism is the appearance of correlated neuron triggering induced by quantum correlations between ions. In this work, we examine the preservation of the correlations created in the pre-synaptic neurons through the transfer of neurotransmitters across the synaptic cleft, a critical ingredient for the validity of the "quantum brain" hypothesis. We simulated the transport of two neurotransmitters at two different clefts, with the only assumption that they start simultaneously, and determined the difference in their first passage times. We show that in physiological conditions, the correlations are persistent even if the parameters of the two neurons are different.
The "quantum brain" proposal can revolutionize our understanding of cognition if proven valid. The core of the most common "quantum brain" mechanism is the appearance of correlated neuron triggering induced by quantum correlations between ions. In this work, we examine the preservation of the correlations created in the pre-synaptic neurons through the transfer of neurotransmitters across the synaptic cleft, a critical ingredient for the validity of the "quantum brain" hypothesis. We simulated the transport of two neurotransmitters at two different clefts, with the only assumption that they start simultaneously, and determined the difference in their first passage times. We show that in physiological conditions, the correlations are persistent even if the parameters of the two neurons are different.
In the struggle about the role of monism and dualism as basic concepts of the mind-brain relation the indeterminacy of quantum events has led an increasing number of authors to postulate quantum brain dynamics as the key towards a scientific understanding of consciousness. In most cases some specific form of macroscopic quantum states in the brain are invoked without giving details for their occurrence or persistence. This raises immediately the question how such states could survive thermal fluctuations in the hot and wet brain environment. In this paper we present a model for a quantum mechanical trigger which regulates synaptic exocytosis, the regulator for ordered brain activity. The model is based on a quantum mechanical tunnelling process which is stable against thermal fluctuations and consistent with the physiological conditions of the synaptic membrane.
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.
If neural synapse is a probabilistic event, then one would expect that this is the result of a chain of subprocesses that takes place in axon. Thus, one can adjudicate probabilistic quantities to each one of event previous to the releasing of neurotransmitters from synaptic vesicles. Indeed, one can argue that electrical interactions are responsible of neurotransmitters dynamics before them pass to cleft. Because this, the present paper has employed the formalism of quantum probabilities. Thus, quantum mechanics would apply to describe the entire process of synapse. Finally, it was associated a set of conditional probabilities that would led to state that neural synapse is a kind of conjunction of quantum events and conditional probabilities. Finally, probabilities are estimated exhibiting a logic agreement to common knowledge of synapse.
This article describes small neurotransmitters as particles of a spinless quantum field. That is, the particles are Bosons that e.g. can occupy equal energy levels. In addition, we consider the particles of the presynaptic region before exocytosis occur as elements of a grand canonical ensemble that is in a thermodynamic equilibrium. Thus, the particles obey the Bose-Einstein statistics, which also determines the corresponding information entropy and the corresponding density matrix. When the release of neurotransmitters occurs, the equilibrium collapses and the Bose-Einstein distribution transfers to the Poisson distribution. Moreover, the particles transmit as wave packets, with quantized energies and momenta, through the chemical synapses, where we also describe the effects of the quantum fluctuations. We mark this symmetry braking process that corresponds to a non-equilibrium phase transition by a threshold, which mainly depends on the mean of the particles number, with defined quanta. We model the connections of synaptic neurons of a population to a network by Hamiltonians that include both Bosons and Fermions and their interactions. Bosons are the carriers of messages (information) and Fermions are the switches, which forward these messages, with a modified content. The effects we observe in such a neural circuitry reveals a strong dependence of the solutions from the initial values and, more relevant, solutions with chaotic behavior exist. These circuitry-based ramifications together with possible internal malfunctioning of particular neurons (e.g. intermitted flow) of the network cause a sustainable reduction of the synaptic plasticity.
This paper presents a novel approach to simulating neuroplasticity by leveraging the principles of quantum decoherence. Traditional classical simulations of neural networks often struggle to accurately represent complex phenomena like long-range dependence and synaptic plasticity due to their inherent limitations in capturing quantum mechanical effects. We propose a framework where neuroplasticity processes are mapped onto quantum decoherence dynamics, offering a potentially more accurate and detailed simulation. The model incorporates control over decoherence rates and paths, allowing us to mimic key synaptic plasticity mechanisms. The core claim is that this approach, utilizing the precision of quantum decoherence, will enable the construction of computational models that genuinely reflect the underlying mechanisms of neuroplasticity, surpassing the accuracy constraints of classical simulations. Specifically, we will investigate scenarios where the temporal correlations within neuronal populations can be precisely modeled, leading to a deeper understanding of how neural networks adapt and learn. The simulation will focus on mechanisms like spike-timing-dependent plasticity (STDP) and persistent sodium currents, employing a system of coupled quantum oscillators to represent neurons and their synaptic connections. The results presented here demonstrate the potential of quantum simulations to revolutionize our understanding of neuroplasticity.
This article describes small neurotransmitters as particles of a spinless quantum field. That is, the particles are Bosons that e.g. can occupy equal energy levels. In addition, we consider the particles of the presynaptic region before exocytosis occur as elements of a grand canonical ensemble that is in a thermodynamic equilibrium. Thus, the particles obey the Bose-Einstein statistics, which also determines the corresponding information entropy and the corresponding density matrix. When the release of neurotransmitters occur, the equilibrium collapses and the Bose-Einstein distribution transfers to the Poisson distribution. Moreover, the particles transmit as wave packets, with quantized energies and momenta, through the chemical synapses, where we also describe the effects of the quantum fluctuations. We mark this symmetry braking process that corresponds to a non-equilibrium phase transition by a threshold, which mainly depends on the mean of the particles number, with defined quanta. We model the connections of synaptic neurons of a population to a network by Hamiltonians that include both Bosons and Fermions and their interactions. Bosons are the carriers of messages (information) and Fermions are the switches, which forward these messages, with a modified content. The effects we observe in such a neural circuitry reveals a strong dependence of the solutions from the initial values and, more relevant, solutions with chaotic behavior exist. These circuitry-based ramifications together with possible internal malfunctioning of particular neurons (e.g. intermitted flow) of the network cause a sustainable reduction of the synaptic plasticity.