Skip to main content

Research Repository

Advanced Search

Combining spatial and parametric working memory in a dynamic neural field model

Wojtak, Weronika; Coombes, Stephen; Bicho, Estela; Erlhagen, Wolfram

Authors

Weronika Wojtak

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 http://eprints.nottingham.ac.uk/id/eprint/40913
Publisher URL http://link.springer.com/chapter/10.1007/978-3-319-44778-0_48
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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

Files


ICANN2016.pdf (752 Kb)
PDF

Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





You might also like



Downloadable Citations