High capacity, small world associative memory models
High capacity, small world associative memory models
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DOI：
10.1080/09540090600639339
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发表时间：
2006-09-01
影响因子：
5.3
通讯作者：
Adams, Rod
中科院分区：
文献类型：
--
作者：
Davey, Neil;Calcraft, Lee;Adams, Rod
联想记忆的模型通常具有完全连通性，或者如果稀释，则具有随机对称连通性。相比之下，生物神经系统主要具有局部的非对称连接性。在这里，我们研究稀疏网络的阈值单位，训练与感知器学习规则。这些单元被给定位置并被布置成环形。连接图通过小世界机制在局部到随机之间变化，任何两个神经元之间的路径长度都很短。连接性可以是对称的或非对称的。结果表明，具有非对称权值和非对称连通性的小世界网络作为联想记忆的性能最好。它还表明，在高度稀释的网络小世界体系结构将产生有效的有线联想记忆，仍然表现出良好的模式完成能力。
Models of associative memory usually have full connectivity or, if diluted, random symmetric connectivity. In contrast, biological neural systems have predominantly local, non-symmetric connectivity. Here we investigate sparse networks of threshold units, trained with the perceptron learning rule. The units are given position and are arranged in a ring. The connectivity graph varies between being local to random via a small world regime, with short path lengths between any two neurons. The connectivity may be symmetric or non-symmetric. The results show that it is the small world networks with non-symmetric weights and non-symmetric connectivity that perform best as associative memories. It is also shown that in highly dilute networks small world architectures will produce efficiently wired associative memories, which still exhibit good pattern completion abilities.