Weronika Wojtak
Combining spatial and parametric working memory in a dynamic neural field model
Wojtak, Weronika; Coombes, Stephen; Bicho, Estela; Erlhagen, Wolfram
Authors
Professor STEPHEN COOMBES STEPHEN.COOMBES@NOTTINGHAM.AC.UK
Professor of Applied Mathematics
Estela Bicho
Wolfram Erlhagen
Abstract
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.
Citation
Wojtak, W., Coombes, S., Bicho, E., & Erlhagen, W. (in press). Combining spatial and parametric working memory in a dynamic neural field model. Lecture Notes in Artificial Intelligence, 9886, https://doi.org/10.1007/978-3-319-44778-0_48
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 13, 2016 |
Online Publication Date | Aug 13, 2016 |
Deposit Date | Feb 28, 2017 |
Publicly Available Date | Feb 28, 2017 |
Journal | Lecture Notes in Computer Science |
Electronic ISSN | 0302-9743 |
Publisher | Springer Verlag |
Peer Reviewed | Peer Reviewed |
Volume | 9886 |
Book Title | Artificial Neural Networks and Machine Learning – ICANN 2016 |
DOI | https://doi.org/10.1007/978-3-319-44778-0_48 |
Public URL | https://nottingham-repository.worktribe.com/output/805704 |
Publisher URL | http://link.springer.com/chapter/10.1007/978-3-319-44778-0_48 |
Additional Information | The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-44778-0_48. Volume title: Artificial Neural Networks and Machine Learning – ICANN 2016 |
Contract Date | Feb 28, 2017 |
Files
ICANN2016.pdf
(752 Kb)
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