Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory
Open Access
- 3 January 2012
- journal article
- research article
- Published by MDPI AG in Information
- Vol. 3 (1), 1-15
- https://doi.org/10.3390/info3010001
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
This publication has 41 references indexed in Scilit:
- Coherent Infomax as a Computational Goal for Neural SystemsBulletin of Mathematical Biology, 2010
- The free-energy principle: a unified brain theory?Nature Reviews Neuroscience, 2010
- Past-future information bottleneck in dynamical systemsPhysical Review E, 2009
- ComplexityScholarpedia, 2007
- Convergence of biological and psychological perspectives on cognitive coordination in schizophreniaBehavioral and Brain Sciences, 2003
- Contextually guided unsupervised learning using local multivariate binary processorsNeural Networks, 1998
- In search of common foundations for cortical computationBehavioral and Brain Sciences, 1997
- Activation Functions, Computational Goals, and Learning Rules for Local Processors with Contextual GuidanceNeural Computation, 1997
- The discovery of structure by multi-stream networks of local processors with contextual guidanceNetwork: Computation in Neural Systems, 1995
- Self-organizing neural network that discovers surfaces in random-dot stereogramsNature, 1992