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Quantum probability in decision making from quantum information representation of neuronal states
Linnaeus University, Faculty of Technology, Department of Mathematics. Natl Res Univ Informat Technol Mech & Opt ITMO, Russia. (Int Ctr Math Modeling Phys & Cognit Sci)ORCID iD: 0000-0002-9857-0938
City Univ London, UK.
City Univ London, UK.
Tokyo Univ Sci, Japan;Chiba Univ, Japan.
2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 16225Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018. Vol. 8, article id 16225
National Category
Mathematics
Research subject
Natural Science, Mathematics
Identifiers
URN: urn:nbn:se:lnu:diva-78837DOI: 10.1038/s41598-018-34531-3ISI: 000448950500077PubMedID: 30385809Scopus ID: 2-s2.0-85055916074OAI: oai:DiVA.org:lnu-78837DiVA, id: diva2:1263538
Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2019-08-29Bibliographically approved

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Khrennikov, Andrei

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