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Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory
Department of Psychology, University of Stirling, Stirling FK9 4LA, UK
Frankfurt Institute of Advanced Studies, Frankfurt, 60438, Germany
Received: 13 December 2011; in revised form: 28 December 2011 / Accepted: 29 December 2011 / Published: 4 January 2012
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.
Keywords: self-organization; complexity; Coherent Infomax; Jaynes; probability theory; probabilistic inference; neural computation; information; context-sensitivity; coordination
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MDPI and ACS Style
Phillips, W.A. Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory. Information 2012, 3, 1-15.
Phillips WA. Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory. Information. 2012; 3(1):1-15.
Phillips, William A. 2012. "Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory." Information 3, no. 1: 1-15.