Measuring the complexity of consciousness
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- dc.contributor.author Arsiwalla, Xerxes D.
- dc.contributor.author Verschure, Paul F. M. J.
- dc.date.accessioned 2021-06-21T08:36:35Z
- dc.date.available 2021-06-21T08:36:35Z
- dc.date.issued 2018
- dc.description.abstract The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.
- dc.description.sponsorship This work has been supported by the European Research Council's CDAC project: The Role of Consciousness in Adaptive Behavior: A Combined Empirical, Computational and Robot based Approach (ERC-2013- ADG 341196).
- dc.format.mimetype application/pdf
- dc.identifier.citation Arsiwalla XD, Verschure PFMJ. Measuring the complexity of consciousness. Front Neurosci. 2018;12:424. DOI: 10.3389/fnins.2018.00424
- dc.identifier.doi http://dx.doi.org/10.3389/fnins.2018.00424
- dc.identifier.issn 1662-4548
- dc.identifier.uri http://hdl.handle.net/10230/47953
- dc.language.iso eng
- dc.publisher Frontiers
- dc.relation.ispartof Frontiers in Neuroscience. 2018;12:424
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/341196
- dc.rights © 2018 Arsiwalla and Verschure. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) https://creativecommons.org/licenses/by/4.0/. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Consciousness in the clinic
- dc.subject.keyword Computational neuroscience
- dc.subject.keyword Complexity measures
- dc.subject.keyword Information theory
- dc.subject.keyword Clinical scales
- dc.title Measuring the complexity of consciousness
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion