Computer Science – Neural and Evolutionary Computing
Scientific paper
2006-12-30
pp. 17-22 in : V. Capasso (Ed.): Mathematical Modeling & Computing in Biology and Medicine, Miriam Series, Progetto Leonardo,
Computer Science
Neural and Evolutionary Computing
6 pages, 3 figures. ESMTB 2002 Milano. For the extended journal version see cond-mat/0208414
Scientific paper
Self-Organizing Maps are models for unsupervised representation formation of cortical receptor fields by stimuli-driven self-organization in laterally coupled winner-take-all feedforward structures. This paper discusses modifications of the original Kohonen model that were motivated by a potential function, in their ability to set up a neural mapping of maximal mutual information. Enhancing the winner update, instead of relaxing it, results in an algorithm that generates an infomax map corresponding to magnification exponent of one. Despite there may be more than one algorithm showing the same magnification exponent, the magnification law is an experimentally accessible quantity and therefore suitable for quantitative description of neural optimization principles.
No associations
LandOfFree
Magnification Laws of Winner-Relaxing and Winner-Enhancing Kohonen Feature Maps does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Magnification Laws of Winner-Relaxing and Winner-Enhancing Kohonen Feature Maps, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Magnification Laws of Winner-Relaxing and Winner-Enhancing Kohonen Feature Maps will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-486054