Associative memory with small world connectivity built on watts-strogatz model

Authors:
Xu Zhi;Gao Jun;Shao Jing;Zhou Yajin
Affiliations:
Institute of Intelligent Machines, Chinese Academy of Sciences, Center for Biomimetic Sensing and Control Research, Anhui Hefei, China;Institute of Intelligent Machines, Chinese Academy of Sciences, Center for Biomimetic Sensing and Control Research, Anhui Hefei, China;Institute of Intelligent Machines, Chinese Academy of Sciences, Center for Biomimetic Sensing and Control Research, Anhui Hefei, China;Institute of Intelligent Machines, Chinese Academy of Sciences, Center for Biomimetic Sensing and Control Research, Anhui Hefei, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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Abstract

Most models of neural associative memory have used networks with full connectivity. However, this seems unrealistic from a neuroanatomical perspective and a VLSI implementation viewpoint. In this study, we built a new associative memory network based on the Watts-Strogatz model. The results indicate that this new network can recall the memorized patterns even with only a small fraction of total connections and is more sufficient than other networks with sparse topologies, such as randomly connected network and regularly network.