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A modular attractor associative memory with patchy connectivity and weight pruning
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.
2013 (English)In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 24, no 4, 129-150 p.Article in journal (Refereed) Published
Abstract [en]

An important research topic in neuroscience is the study of mechanisms underlying memory and the estimation of the information capacity of the biological system. In this report we investigate the performance of a modular attractor network with recurrent connections similar to the cortical long-range connections extending in the horizontal direction. We considered a single learning rule, the BCPNN, which implements a kind of Hebbian learning and we trained the network with sparse random patterns. The storage capacity was measured experimentally for networks of size between 500 and 46 K units with a constant activity level, gradually diluting the connectivity. We show that the storage capacity of the modular network with patchy connectivity is comparable with the theoretical values estimated for simple associative memories and furthermore we introduce a new technique to prune the connectivity, which enhances the storage capacity up to the asymptotic value.

Place, publisher, year, edition, pages
2013. Vol. 24, no 4, 129-150 p.
Keyword [en]
Structural plasticity, storage capacity, sparse coding, hebbian learning
National Category
Computer Science
Identifiers
URN: urn:nbn:se:su:diva-98102DOI: 10.3109/0954898X.2013.859323ISI: 000327445400001OAI: oai:DiVA.org:su-98102DiVA: diva2:682300
Note

AuthorCount:2;

Available from: 2013-12-27 Created: 2013-12-27 Last updated: 2017-12-06Bibliographically approved

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