Numéro
J. Phys. I France
Volume 2, Numéro 5, May 1992
Page(s) 615 - 624
DOI https://doi.org/10.1051/jp1:1992105
DOI: 10.1051/jp1:1992105
J. Phys. I France 2 (1992) 615-624

Adaptive architectures for Hebbian network models

Karl E. Kürten

Institut für Neuroinformatik, Ruhr-Universität Bochum, D-4630 Bochum, Germany


(Received 13 December 1991, accepted in final form 24 January 1992)

Abstract
We present a novel produce which allows a neural network to evolve to a quasi-optimal connectivity such that information to be memorized is engraved as efficiently as possible. The emerging connectivity structure of the resulting partially connected network depends strongly on the information the network is asked to memorize. A stability measure of the quality of storage as a function of the degree of the connectivity is shown to attain a maximum value, which lies substantially above the stability of the traditional fully connected system.



© Les Editions de Physique 1992

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.