Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/20494
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dc.contributor.authorPhillips, William-
dc.date.accessioned2018-01-09T04:08:19Z-
dc.date.available2018-01-09T04:08:19Z-
dc.date.issued2012-01-
dc.identifier.urihttp://hdl.handle.net/1893/20494-
dc.description.abstractThis paper discusses concepts of self-organized complexity and the theory of Coherent Infomax in the light of Jaynes’s probability theory. Coherent Infomax, shows, in principle, how adaptively self-organized complexity can be preserved and improved by using probabilistic inference that is context-sensitive. It argues that neural systems do this by combining local reliability with flexible, holistic, context-sensitivity. Jaynes argued that the logic of probabilistic inference shows it to be based upon Bayesian and Maximum Entropy methods or special cases of them. He presented his probability theory as the logic of science; here it is considered as the logic of life. It is concluded that the theory of Coherent Infomax specifies a general objective for probabilistic inference, and that contextual interactions in neural systems perform functions required of the scientist within Jaynes’s theory.en_UK
dc.language.isoen-
dc.publisherMDPI-
dc.relationPhillips W (2012) Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes's Probability Theory, Information, 3 (1), pp. 1-15.-
dc.rights© 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).-
dc.subjectself-organizationen_UK
dc.subjectcomplexityen_UK
dc.subjectCoherent Infomaxen_UK
dc.subjectJaynesen_UK
dc.subjectprobability theoryen_UK
dc.subjectprobabilistic inferenceen_UK
dc.subjectneural computationen_UK
dc.subjectinformationen_UK
dc.subjectcontext-sensitivityen_UK
dc.subjectcoordinationen_UK
dc.titleSelf-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes's Probability Theoryen_UK
dc.typeJournal Articleen_UK
dc.identifier.doihttp://dx.doi.org/10.3390/info3010001-
dc.citation.jtitleInformation-
dc.citation.issn2078-2489-
dc.citation.volume3-
dc.citation.issue1-
dc.citation.spage1-
dc.citation.epage15-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPublisher version (final published refereed version)-
dc.author.emailw.a.phillips@stir.ac.uk-
dc.contributor.affiliationPsychology-
dc.identifier.isi000214790800001-
Appears in Collections:Psychology Journal Articles

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