"Small-World" networks is a term recently coined by Watts and Strogatz to describe networks which simultaneously exhibit a high degree of node clustering and short minimum path lengths between nodes. Such networks represent a very efficient architecture for achieving maximal internode communication with minimal connection length-a feature that is extremely important in highly connected physical networks, where interconnections consume most space. Neural networks-both in the brain and in hardware implementation-can benefit greatly from a small-world architecture, and there is evidence that this strategy is widely used in the nervous system. In this paper, we study the recall performance of associative memories with regard to their small-world characteristics. The results indicate that, indeed, a small-world approach can lead to networks with high performance and minimal interconnect requirements.
['Jason W. Bohland', 'Ali A. Minai']
Small-World model of associative memory