|Appears in Collections:||Psychology Book Chapters and Sections|
|Peer Review Status:||Refereed|
|Title:||How do neural systems use probabilistic inference that is context-sensitive to create and preserve organized complexity?|
|Citation:||Phillips W (2012) How do neural systems use probabilistic inference that is context-sensitive to create and preserve organized complexity?. In: Simeonov PL, Smith LS, Ehresmann AC (ed.). Integral Biomathics: Tracing the Road to Reality, Berlin Heidelberg: Springer, pp. 63-69.|
|Abstract:||This paper claims that biological systems will more effectively create organized complexity if they use probabilistic inference that is context-sensitive. It argues that neural systems combine local reliability with flexible, holistic, context-sensitivity, and a theory, Coherent Infomax, showing, in principle, how this can be done is outlined. Ways in which that theory needs further development are noted, and its relation to Friston’s theory of free energy reduction is discussed.|
|Rights:||The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.|
|Phillips ACIB2012.pdf||165.01 kB||Adobe PDF||Under Embargo until 31/12/2999 Request a copy|
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependant on the depositor still being contactable at their original email address.
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact firstname.lastname@example.org providing details and we will remove the Work from public display in STORRE and investigate your claim.