Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/43686

TitleCombining spatial and parametric working memory in a dynamic neural field model
Author(s)Wojtak, Weronika
Coombes, Stephen
Bicho, Estela
Erlhagen, Wolfram
KeywordsDynamic neural field
Amari model
Working memory
Neural Integrator
Issue dateNov-2016
PublisherSpringer International Publishing
JournalLecture Notes in Computer Science
Abstract(s)We present a novel dynamic neural field model consisting of two coupled fields of Amari-type which supports the existence of localized activity patterns or “bumps” with a continuum of amplitudes. Bump solutions have been used in the past to model spatial working memory. We apply the model to explain input-specific persistent activity that increases monotonically with the time integral of the input (parametric working memory). In numerical simulations of a multi-item memory task, we show that the model robustly memorizes the strength and/or duration of inputs. Moreover, and important for adaptive behavior in dynamic environments, the memory strength can be changed at any time by new behaviorally relevant information. A direct comparison of model behaviors shows that the 2-field model does not suffer the problems of the classical Amari model when the inputs are presented sequentially as opposed to simultaneously.
TypeConference paper
URIhttp://hdl.handle.net/1822/43686
ISBN9783319447773
DOI10.1007/978-3-319-44778-0_48
ISSN0302-9743
Publisher versionwww.springer.com
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

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