文章基本信息

标题：Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory
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作者：William A. Phillips
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2012
卷号：3
期号：1
页码：1-15
DOI：10.3390/info3010001
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：self-organization; complexity; Coherent Infomax; Jaynes; probability theory; probabilistic inference; neural computation; information; context-sensitivity; coordination