Overlapping decompositions in the design of associative memories

This paper is concerned with the design of neural networks to be used as associative memories. The idea of overlapping decompositions, which is extensively used in the solution of large-scale problems as a method of reducing the computational work, is applied to discrete-time neural networks with binary neurons. It is shown that if the desired memory matrix accepts a suitable overlapping decomposition, then the problem can be solved by synthesizing a number of smaller networks independently. The concept is illustrated with two examples.