Efficient associative memory using small-world architecture

被引:97
作者
Bohland, JW [1 ]
Minai, AA [1 ]
机构
[1] Univ Cincinnati, ECECS Dept, Complex Adapt Syst Lab, Cincinnati, OH 45221 USA
关键词
associative memory; small-world networks;
D O I
10.1016/S0925-2312(01)00378-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most models of neural associative memory have used networks with broad connectivity. However, from both a neurobiological viewpoint and an implementation perspective, it is logical to minimize the length of inter-neural connections and consider networks whose connectivity is predominantly local. The "small-world networks" model described recently by Watts and Strogatz provides an interesting approach to this issue. In this paper, we show that associative memory networks with small-world architectures can provide the same retrieval performance as randomly connected networks while using a fraction of the total connection length. (C) 2001 Published by Elsevier Science B.V.
引用
收藏
页码:489 / 496
页数:8
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