Abstract A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical “quantum decision tree” to “decide” whether to excite or inhibit the synapse. When a neurotransmitter binds to its corresponding receptor, it is the equivalent of randomly choosing different "strategies"; a “strategy" has two actions to take: excite or inhibit the synapse with a certain probability. The genetic programming can be applied for learning the observed data sequence to simulate the synaptic message transmission.
Abstract A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical “quantum decision tree” to “decide” whether to excite or inhibit the synapse. When a neurotransmitter binds to its corresponding receptor, it is the equivalent of randomly choosing different "strategies"; a “strategy" has two actions to take: excite or inhibit the synapse with a certain probability. The genetic programming can be applied for learning the observed data sequence to simulate the synaptic message transmission.
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.
A probabilistic model of Synapse is proposed. In concrete, synapse is proposed as a Bayesian probability that requires the quantum probabilities for both: the interaction of Calcium 2+ in axon area, and the releasing of neurotransmitters. In this way, all available interactions that are carry out at axon zone, are dictated by quantum events. Once neurotransmitters are released from synaptic vesicles, their dynamics might be dictated by quantum probabilities based in a Hamiltonian. The diverse scenarios and their respective distribution of probabilities are simulated. From proposed theory, it is suggested that synapse would have a notable dependence on the accumulation of electric charges before and after the pass of neurotransmitters from a neuron to adjacent one. From the results, it is possible to argue that synapse is more than a biological phenomenon, a multidisciplinary event, that emphasizes the importance of transporting of electric charges with a potential dependence on probabilities, more than trivial determinism.
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.
Descriptions of neurotransmitter cycles in chemical synapses are generally accomplished in the field of macroscopic molecular biology. This paper proposes a new theoretical approach to model these cycles with methods of the non-relativistic quantum field theory (QFT) which is applicable on small neurotransmitters of nano size like amino acids or amines. The whole cycle is subdivided into the standard five phases: uptake, axonal transport, release and reception. Our ansatz is concentrated to quantum effects, which are relevant in molecular processes. Examples are quantization of momentums and energies of all small transmitters, definition of the density-based quantum information, quantization of molecular currents, because densities of them generate quantized particles. Our model of the neurotransmitter cycle of chemical synapses was created by the emphasis of possible essential quantum effects; therefore, we neglect many additional molecular aspects that do not lead us to quantum impacts. We elucidate the ramification of our quantum-based approach by the definition of particular Hamiltonians for each of the five phases of the neurotransmitter cycles and by the calculation of the corresponding molecular dynamics. The transformation from the particle representation to usual wave functions yields the probability to find at the same time neurotransmitters of different energy states at different positions. Our results have far-reaching implications and may initiate animated discussions. The validation or the disconfirmation of our hypothesis is still open.
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.
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.