Neural Computation, Vol 10, 59-72, Copyright © 1998 by The MIT Press
State Dependent Weights for Neural Associative Memories
Ravi Kothari, Rohit Lotlikar and Marc Cathay
In this article we study the effect of dynamically modifying the weight
matrix on the performance of a neural associative memory. The dynamic
modification is implemented by adding, at each step, the outer product of
the current state, scaled by a suitable constant 
, to the
correlation weight matrix. For single-shot synchronous dynamics, we
analytically obtain the optimal value of . Although knowledge of
the noise percentage is required for calculating the optimal value of
, a fairly good choice of can be made even when the
amount of noise is not known. Experimental results are provided in support
of the analysis. The efficacy of the proposed modification is also
experimentally verified for the case of asynchronous updating with
transient length > 1.
