Efficient architectures for sparsely-connected high capacity associative memory models

In physical implementations of associative memory, wiring costs play a significant role in shaping patterns of connectivity. In this study of sparsely-connected associative memory, a range of architectures is explored in search of optimal connection strategies which maximise pattern-completion performance, while at the same time minimising wiring costs. It is found that architectures in which the probability of connection between any two nodes is based on relatively tight Gaussian and exponential distributions perform well, and that for optimum performance, the width of the Gaussian distribution should be made proportional to the number of connections per node. It is also established from a study of other connection strategies that distal connections are not necessary for good pattern-completion performance. Convergence times and network scalability are also addressed in the wide ranging study.