Share Email Print

Proceedings Paper

Storing temporal sequences of patterns in neural networks
Author(s): Dilip Krishnaswamy; Kishan Mehrotra; Chilukuri K. Mohan; Sanjay Ranka
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper presents neural network models for storing terminating and cyclic temporal sequences of patterns under synchronous, sequential and asynchronous dynamics. We use fully interconnected neural networks with asymmetric weight connections for synchronous and sequential dynamics and a layered neural network with feedback for asynchronous dynamics. The network were successfully implemented and the number of patterns that could be stored and recalled was approximately 12% of the size of the patterns in the network.

Paper Details

Date Published: 29 October 1993
PDF: 7 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162028
Show Author Affiliations
Dilip Krishnaswamy, Syracuse Univ. (United States)
Kishan Mehrotra, Syracuse Univ. (United States)
Chilukuri K. Mohan, Syracuse Univ. (United States)
Sanjay Ranka, Syracuse Univ. (United States)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top