View(s) :
7 (0 ULiège)
Download(s) :
2 (0 ULiège)
A Synthesis of the Pribram Holonomic Theory of Vision With Quantum Associative Nets After Pre-Processing Using I.C.A. and Other Computational Models
p. 352-367
Abstract
Statistically-Independent Component Analysis (ICA) and sparseness-maximization net are models which maximally preserve information ("infomax"). Research of relevance of these algorithms for modeling image-processing in V1 is reported in comparison with the Holonomic Brain Theory by Pribram which advocates dendritic processing and its connection to quantum processing. "Infomax" models are presented and discussed as a possible early-processing gateway to higher visual processing involving quantum associative nets (Perus, 2000) and attractor dynamics.
Text
References
Bibliographical reference
Mitja Peruš, « A Synthesis of the Pribram Holonomic Theory of Vision With Quantum Associative Nets After Pre-Processing Using I.C.A. and Other Computational Models », CASYS, 10 | 2001, 352-367.
Electronic reference
Mitja Peruš, « A Synthesis of the Pribram Holonomic Theory of Vision With Quantum Associative Nets After Pre-Processing Using I.C.A. and Other Computational Models », CASYS [Online], 10 | 2001, Online since 10 July 2024, connection on 04 June 2025. URL : http://popups.uliege.be/3041-539x/index.php?id=1319
Author
Mitja Peruš
Institute BION, Stegne 21, SI-1000 Ljubljana, Slovenia
