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

Wojtak, Weronika, Coombes, Stephen, Bicho, Estela and Erlhagen, Wolfram (2016) Combining spatial and parametric working memory in a dynamic neural field model. Lecture Notes in Computer Science, 9886 . pp. 411-418. ISSN 0302-9743

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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.

Item Type: Article
Additional Information: The final publication is available at Springer via Volume title: Artificial Neural Networks and Machine Learning – ICANN 2016
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
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Depositing User: Eprints, Support
Date Deposited: 28 Feb 2017 13:47
Last Modified: 04 May 2020 18:07

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