Optimising pattern recovery in recurrent correlation associative memories

Addresses the problem of how to identify the optimal excitation function for the recurrent correlation associative memory. We present a model of pattern recovery which allows us to measure probability of bit-error. By minimising this measure we are able to numerically locate the excitation function which results in the minimum error of pattern recall. Additionally, we show that minimising a simpler measure of pattern overlap leads to an analytical expression for the excitation function which is exponential. We compare the performance of the numerical and exponential functions. This reveals that the more easily controlled exponential is only slightly poorer in its performance