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NeuroQuantology

Explaining Dopamine Deficiency of Spiking Neurons Based on Quantum Superposition Theory

Mohammad Daneshzand, Jasem Almotiri, Rania Baashirah, Khaled M. Elleithy

Published Jun 3, 2015• 0 citations
Publisher

Abstract

In all mammalian brain billions of neurons exist and these neurons are working together through synapses and spike responses. Through numerous models presented to describe neuronal response and how the membrane potential would vary due to different tasks, still a big question stand still in the field of neuroscience. Why a neuron can produce different types of spikes under same conditions? Here we build up a new model of spiking neuron based on Izhikevich model which can generate all possible neuronal spikes and we try to answer this question by putting the concept of quantum superposition into account. We showed that unknown and random behavior of neuronal spikes through time can be described by this quantum fact that a neuron can be in many states at a same time, therefore any spike patterns or combination of spike patterns is of interest. Based on neuronal spikes patterns, sometimes we observed an unknown pattern can be explained by this phenomena that a neuron can exist partially in a physical state but when it’s response to an stimuli is recorded the result would be only one of the possible states or a linear combination of some states.

DOI
10.14704/nq.2015.13.2.853

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In all mammalian brain billions of neurons exist and these neurons are working together through synapses and spike responses. Through numerous models presented to describe neuronal response and how the membrane potential would vary due to different tasks, still a big question stand still in the field of neuroscience. Why a neuron can produce different types of spikes under same conditions? Here we build up a new model of spiking neuron based on Izhikevich model which can generate all possible neuronal spikes and we try to answer this question by putting the concept of quantum superposition into account. We showed that unknown and random behavior of neuronal spikes through time can be described by this quantum fact that a neuron can be in many states at a same time, therefore any spike patterns or combination of spike patterns is of interest. Based on neuronal spikes patterns, sometimes we observed an unknown pattern can be explained by this phenomena that a neuron can exist partially in a physical state but when it’s response to an stimuli is recorded the result would be only one of the possible states or a linear combination of some states.
The recent wave of interest to modeling the process of decision making with the aid of the quantum formalism gives rise to the following question: 'How can neurons generate quantum-like statistical data?' (There is a plenty of such data in cognitive psychology and social science). Our model is based on quantum-like representation of uncertainty in generation of action potentials. This uncertainty is a consequence of complexity of electrochemical processes in the brain; in particular, uncertainty of triggering an action potential by the membrane potential. Quantum information state spaces can be considered as extensions of classical information spaces corresponding to neural codes; e.g., 0/1, quiescent/firing neural code. The key point is that processing of information by the brain involves superpositions of such states. Another key point is that a neuronal group performing some psychological function F is an open quantum system. It interacts with the surrounding electrochemical environment. The process of decision making is described as decoherence in the basis of eigenstates of F. A decision state is a steady state. This is a linear representation of complex nonlinear dynamics of electrochemical states. Linearity guarantees exponentially fast convergence to the decision state.
This chapter discusses several previously published results on possible applications of quantum information theory to neuroscience and will present a number of open questions that deserve further study. Applying the quantum information theory of consciousness to modeling the electric processes that occur in neuronal networks in the brain is an open scientific problem that deserves further study. The quantum behavior of individual ion channels is clearly manifested in the discrete changes observed in their channel conductance. Neurons communicate with each other at synaptic contacts that permit the transmission of electric or chemical signals from one neuron to another. Electrical synapses provide a direct electrical coupling between the two neurons through gap junctions that are nanopores constructed from connexin proteins. Multiple protein subunits, each possessing its own geometric shape, may further bind into protein complexes with a quaternary structure. Voltage-gated ion channels provide a typical example of protein complexes that are composed of multiple protein subunits.
Quantum-like modeling (QLM) – quantum theory applications outside of physics – are intensively developed with applications in biology, cognition, psychology, and decision-making. For cognition, QLM should be distinguished from quantum reductionist models in the spirit of Hameroff and Penrose and well as Umezawa and Vitiello. QLM is not concerned with just quantum physical processes in the brain but also QL information processing by macroscopic neuronal structures. Although QLM of cognition and decision-making has seen some success, it suffers from a knowledge gap that exists between oscillatory neuronal network functioning in the brain and QL behavioral patterns. Recently, steps toward closing this gap have been taken using the generalized probability theory and prequantum classical statistical field theory (PCSFT) – a random field model beyond the complex Hilbert space formalism. PCSFT is used to move from the classical ``oscillatory cognition'' of the neuronal networks to QLM for decision-making. In this study, we addressed the most difficult problem within this construction: QLM for entanglement generation by classical networks, i.e., “mental entanglement.” We started with the observational approach to entanglement based on operator algebras describing “local observables” and bringing into being the tensor product structure in the space of QL states. Moreover, we applied the standard states entanglement approach: entanglement generation by spatially separated networks in the brain. Finally, we discussed possible future experiments on “mental entanglement” detection using the EEG/MEG technique.
We present the quantum-like model of information processing by the brain’s neural networks. The model does not refer to genuine quantum processes in the brain. In this model, uncertainty generated by the action potential of a neuron is represented as quantum-like superposition of the basic mental states corresponding to a neural code. Neuron’s state space is described as complex Hilbert space (quantum information representation). The brain’s psychological functions perform self-measurements by extracting concrete answers to questions (solutions of problems) from quantum information states. This extraction is modeled in the framework of open quantum systems theory. In this way, it is possible to proceed without appealing to the state’s collapse. Dynamics of the state of psychological function F is described by the quantum master equation. Its stationary states represent classical statistical mixtures of possible outputs of F (decisions). This model can be used for justification of quantum-like modeling cognition and decision-making. The latter is supported by plenty of statistical data collected in cognitive psychology.
Quantum-like modeling (QLM) – quantum theory applications outside of physics – are intensively developed with applications in biology, cognition, psychology, and decision-making. For cognition, QLM should be distinguished from quantum reductionist models in the spirit of Hameroff and Penrose and well as Umezawa and Vitiello. QLM is not concerned with just quantum physical processes in the brain but also QL information processing by macroscopic neuronal structures. Although QLM of cognition and decision-making has seen some success, it suffers from a knowledge gap that exists between oscillatory neuronal network functioning in the brain and QL behavioral patterns. Recently, steps toward closing this gap have been taken using the generalized probability theory and prequantum classical statistical field theory (PCSFT) – a random field model beyond the complex Hilbert space formalism. PCSFT is used to move from the classical ``oscillatory cognition'' of the neuronal networks to QLM for decision-making. In this study, we addressed the most difficult problem within this construction: QLM for entanglement generation by classical networks, i.e., “mental entanglement.” We started with the observational approach to entanglement based on operator algebras describing “local observables” and bringing into being the tensor product structure in the space of QL states. Moreover, we applied the standard states entanglement approach: entanglement generation by spatially separated networks in the brain. Finally, we discussed possible future experiments on “mental entanglement” detection using the EEG/MEG technique.
Quantum-like modeling (QLM)– quantum theory applications outside of physics– are intensively developed with applications in biology, cognition, psychology, and decision-making. For cognition, QLM should be distinguished from quantum reductionist models in the spirit of Hameroff and Penrose and well as Umezawa and Vitiello. QLM is not concerned with just quantum physical processes in the brain but also QL information processing by macroscopic neuronal structures. Although QLM of cognition and decision-making has seen some success, it suffers from a knowledge gap that exists between oscillatory neuronal network functioning in the brain and QL behavioral patterns. Recently, steps toward closing this gap have been taken using the generalized probability theory and prequantum classical statistical field theory (PCSFT)– a random field model beyond the complex Hilbert space formalism. PCSFT is used to move from the classical “oscillatory cognition” of the neuronal networks to QLM for decision-making. In this study, we addressed the most difficult problem within this construction: QLM for entanglement generation by classical networks, i.e., “mental entanglement”. We started with the observational approach to entanglement based on operator algebras describing “local observables” and bringing into being the tensor product structure in the space of QL states. Moreover, we applied the standard states entanglement approach: entanglement generation by spatially separated networks in the brain. Finally, we discussed possible future experiments on “mental entanglement” detection using the EEG/MEG technique.