An associative memory is essentially a mechanism for storing pairs of information patterns u¯ and v¯, so that at a later time presentation of one pattern, u¯, will result in recall of the other, v¯. These information patterns are represented here as time-varying vectors, u¯, v¯, of n discrete binary or analogue elements. These vectors could encode, for example, pixel intensities from a n×n 2-D image, strings of characters from a textual database, speech spectral samples, the state variables of a control system, features extracted from a scene, or the output of a robot's sensors. A large number of paradigms have been advanced for implementing associative memory. These1,2 range from purely deterministic matrix multipliers and content-addressable digital memories to holography and randomly interconnected neural network models. The various associative memory schemes offer differing performance capabilities in terms of such measures as1,2 information capacity, the ability to recall v¯ when presented with only a similar (e.g., partial or distorted) version of the key pattern u¯, discrimination ability between significantly different u's, information pattern crosstalk, hardware fault-tolerance, pretaught versus adaptive self-organizing capabilties, implementational complexity, and retentiveness/forgeting of old associations. It is sometimes desirable to distinguish an autoassociative memory, where u¯ recalls itself, from a heteroassociative memory, where u¯ and v¯ differ.