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Combining Spatial and Parametric Working Memory in a Dynamic Neural Field Model

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

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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.

W. Wojtak—The work received financial support from the EU-FP7 ITN project NETT: Neural Engineering Transformative Technologies (no. 289146).

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Correspondence to Wolfram Erlhagen .

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Wojtak, W., Coombes, S., Bicho, E., Erlhagen, W. (2016). Combining Spatial and Parametric Working Memory in a Dynamic Neural Field Model. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_48

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_48

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