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Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes's Probability Theory
Authors: Phillips, William
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Keywords: self-organization
Coherent Infomax
probability theory
probabilistic inference
neural computation
Issue Date: Jan-2012
Publisher: MDPI
Citation: Phillips W (2012) Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes's Probability Theory, Information, 3 (1), pp. 1-15.
Abstract: This 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.
Type: Journal Article
DOI Link:
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 (
Affiliation: Psychology

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