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A Bayesian attractor network with incremental learning
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-2358-7815
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-2792-1622
2002 (English)In: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 13, no 2, 179-194 p.Article in journal (Refereed) Published
Abstract [en]

A realtime online learning system with capacity limits needs to gradually forget old information in order to avoid catastrophic forgetting. This can be achieved by allowing new information to overwrite old, as in a so-called palimpsest memory. This paper describes an incremental learning rule based on the Bayesian confidence propagation neural network that has palimpsest properties when employed in an attractor neural network. The network does not suffer from catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits faster convergence for newer patterns.

Place, publisher, year, edition, pages
2002. Vol. 13, no 2, 179-194 p.
Keyword [en]
associative memory, neural networks, visual-cortex, hippocampal slices, potentiation, modulation, neurons, storage, specificity, palimpsests
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-21564DOI: 10.1088/0954-898X/13/2/302ISI: 000175773700002OAI: oai:DiVA.org:kth-21564DiVA: diva2:340262
Note
QC 20100525Available from: 2010-08-10 Created: 2010-08-10 Last updated: 2017-12-12Bibliographically approved

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Ekeberg, Örjan

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