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.

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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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