7. Associative Neural Memories

7.0 Introduction

This chapter is concerned with associative learning and retrieval of information (vector patterns) in neural-like networks. These networks are usually referred to as associative neural memories (or associative memories) and they represent one of the most extensively analyzed class of artificial neural networks. Various associative memory architectures are presented with emphasis on dynamic (recurrent) associative memory architectures. These memories are treated as nonlinear dynamical systems where information retrieval is realized as an evolution of the system's state in a high-dimensional state space. Dynamic associative memories (DAM's) are a class of recurrent artificial neural networks which utilize a learning/recording algorithm to store vector patterns (usually binary patterns) as stable memory states. The retrieval of these stored "memories" is accomplished by first initializing the DAM with a noisy or partial input pattern (key) and then allowing the DAM to perform a collective relaxation search to arrive at the stored memory which is best associated with the input pattern.

The chapter starts by presenting some simple networks which are capable of functioning as associative memories and derives the necessary conditions for perfect storage and retrieval of a given set of memories. The chapter continues by presenting additional associative memory models with particular attention given to DAM's. The characteristics of high-performance DAM's are defined, and stability, capacity, and retrieval dynamics of various DAM's are analyzed. Finally, the application of a DAM to the solution of combinatorial optimization problems is described.

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