Paper Info
Title
Self-Organized Complexity And Coherent Infomax From The Viewpoint Of Jaynes'S Probability Theory
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.
Year
DOI
Venue
2012
10.3390/info3010001
INFORMATION
Keywords
Field
DocType
self-organization, complexity, Coherent Infomax, Jaynes, probability theory, probabilistic inference, neural computation, information, context-sensitivity, coordination
Probabilistic inference,Computer science,Self-organization,Models of neural computation,Theoretical computer science,Neural system,Artificial intelligence,Principle of maximum entropy,Probability theory,Infomax,Machine learning,Bayesian probability
Journal
Volume
Issue
ISSN
3
1
2078-2489
Citations 
PageRank 
References 
2
0.66
10
Authors
1
Name
Order
Citations
PageRank
William A. Phillips14016.54