High capacity, small world associative memory models
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.
Keywords: Associative memory; Connectivity; Genetic algorithm; Small-world
Document Type: Research Article
Affiliations: School of Computer Science, University of Hertfordshire, College Lane, Hatfield, AL10 9AB, UK
Publication date: 01 September 2006
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content