HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Chengxiang, Z. Articles by Singh, M. P. Search for Related Content PubMed PubMed Citation Articles by Chengxiang, Z. Articles by Singh, M. P.

Retrieval Properties of a Hopfield Model with Random Asymmetric Interactions

Department of Physics, Indian Institute of Science, Bangalore 560012, India

Chandan Dasgupta

Department of Physics, Indian Institute of Science, Bangalore 560012, India

Manoranjan P. Singh

Laser Programme, Centre for Advanced Technology, Indore 452013, India

The process of pattern retrieval in a Hopfield model in which a random antisymmetric component is added to the otherwise symmetric synaptic matrix is studied by computer simulations. The introduction of the antisymmetric component is found to increase the fraction of random inputs that converge to the memory states. However, the size of the basin of attraction of a memory state does not show any significant change when asymmetry is introduced in the synaptic matrix. We show that this is due to the fact that the spurious fixed points, which are destabilized by the introduction of asymmetry, have very small basins of attraction. The convergence time to spurious fixed-point attractors increases faster than that for the memory states as the asymmetry parameter is increased. The possibility of convergence to spurious fixed points is greatly reduced if a suitable upper limit is set for the convergence time. This prescription works better if the synaptic matrix has an antisymmetric component.

This article has been cited by other articles:

				J. F. Murray and K. Kreutz-Delgado Visual recognition and inference using dynamic overcomplete sparse learning. Neural Comput., September 1, 2007; 19(9): 2301 - 2352. [Abstract] [Full Text] [PDF]