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15 pages, 149 KB  
Article
Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory
by William A. Phillips
Information 2012, 3(1), 1-15; https://doi.org/10.3390/info3010001 - 4 Jan 2012
Cited by 12 | Viewed by 9660
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Information and Energy/Matter)
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