Flexible kernel memory.

This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors...

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Detalles Bibliográficos
Autores principales:	Dimitri Nowicki, Hava Siegelmann
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1df67ecceedb4f71842f5a9388e702b1
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id	oai:doaj.org-article:1df67ecceedb4f71842f5a9388e702b1
record_format	dspace
spelling	oai:doaj.org-article:1df67ecceedb4f71842f5a9388e702b12021-12-02T20:21:00ZFlexible kernel memory.1932-620310.1371/journal.pone.0010955https://doaj.org/article/1df67ecceedb4f71842f5a9388e702b12010-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20552013/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces.Dimitri NowickiHava SiegelmannPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 6, p e10955 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dimitri Nowicki Hava Siegelmann Flexible kernel memory.
description	This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces.
format	article
author	Dimitri Nowicki Hava Siegelmann
author_facet	Dimitri Nowicki Hava Siegelmann
author_sort	Dimitri Nowicki
title	Flexible kernel memory.
title_short	Flexible kernel memory.
title_full	Flexible kernel memory.
title_fullStr	Flexible kernel memory.
title_full_unstemmed	Flexible kernel memory.
title_sort	flexible kernel memory.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/1df67ecceedb4f71842f5a9388e702b1
work_keys_str_mv	AT dimitrinowicki flexiblekernelmemory AT havasiegelmann flexiblekernelmemory
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Flexible kernel memory.

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