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A modular attractor associative memory with patchy connectivity and weight pruning
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.ORCID-id: 0000-0002-2358-7815
2013 (engelsk)Rapport (Annet vitenskapelig)
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 46K units with a constant activity level, gradually diluting the connectivity. We show that the storage capacity of the modular network is comparable with the theoretical values estimated for simple associative memories and furthermore we introduce a new technique to reduce the connectivity, which enhances the storage capacity up to the asymptotic value.

sted, utgiver, år, opplag, sider
KTH Royal Institute of Technology, 2013.
Serie
TRITA-CSC-CB 2013:01, ISSN 1653-5707
Emneord [en]
Storage Capacity, BCPNN, Neural Network, Sparse Coding, Associative Memory, Hebbian Learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-124654ISRN: KTH/CSC/CB--13/01--SEOAI: oai:DiVA.org:kth-124654DiVA, id: diva2:638143
Merknad

QC 20130729

Tilgjengelig fra: 2013-07-26 Laget: 2013-07-26 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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