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

  • Weronika Wojtak
  • Stephen Coombes
  • Estela Bicho
  • Wolfram Erlhagen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9886)

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.

Keywords

Field Model Coupling Function Delay Response Task Input Strength Neural Field Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Weronika Wojtak
    • 1
    • 3
  • Stephen Coombes
    • 2
  • Estela Bicho
    • 1
  • Wolfram Erlhagen
    • 3
  1. 1.Research Centre AlgoritmiUniversity of MinhoGuimarãesPortugal
  2. 2.School of Mathematical Sciences, Centre for Mathematical Medicine and BiologyUniversity of NottinghamNottinghamUK
  3. 3.Research Centre for MathematicsUniversity of MinhoGuimarãesPortugal

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