TY  - JOUR
A1  - Phillips, William A.
T1  - Self-organized complexity and Coherent Infomax from the viewpoint of Jaynes’s probability theory
T2  - Information
N2  - 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.
KW  - self-organization
KW  - complexity
KW  - Coherent Infomax
KW  - Jaynes
KW  - probability theory
KW  - probabilistic inference
KW  - neural computation
KW  - information
KW  - context-sensitivity
KW  - coordination
Y1  - 2012
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/25312
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-253122
SN  - 2078-2489
VL  - 3.2012
IS  - 1
SP  - 1
EP  - 15
PB  - Molecular Diversity Preservation International (MDPI)
CY  - Basel
ER  -