<title>Dynamic Autoassociative Neural Memory Performance VS. Capacity</title>

Abstract

A new capacity measure is proposed for the class of dynamic autoassociative neural memories (ANMs). The proposed associative memory capacity measure is based on the memory's performance vs. the number of stored memory vectors, where performance is defined in terms of the memory's error -correcting ability and its fundamental memories' attraction volumes. This new method of measuring ANM capacity is very effective when ANMs are used in pattern recognition and error -tolerant content -addressable memories. The proposed capacity/performance measure has been tested for several ANMs having the same dynamic Hopfield memory-like architecture, each employing a different recording technique. Correlation, generalized inverse (orthogonal), and Ho-Kashyap memory recordings have been investigated. Monte Carlo analysis has been performed on ANMs recorded with randomly generated patterns, in order to determine and compare performance characteristics and dynamics.