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Multi-item Working Memory Capacity: What Is the Role of the Stimulation Protocol?

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

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9886))

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Abstract

The dynamics of the stimulation protocol become relevant when investigating multi-item working memory (WM). In this work, we explore what is the effect of the stimulation protocol in the encoding and maintenance of multiple items in WM. To this end, we consider a biophysically-realistic attractor model of visual working memory endowed with synaptic facilitation. We show that such a mechanism plays a key role when sequential stimulation protocols are considered. On one hand, synaptic facilitation boosts WM capacity. On the other hand, it allows us to account for the experimentally reported recency effect (i.e. in sequential stimulation protocols, those items presented in the final positions of a sequence are more likely to be retained in WM). In this context, the time constant of the synaptic facilitation process has been found to play an important role in modulating such effects with large values leading to larger capacity limits. However, too large values lead to neuronal dynamics which are not compatible with the recency effect, thus constraining the range of values that the time constant may take.

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Authors and Affiliations

  1. Universitat Pompeu Fabra, Barcelona, Spain

    Marta Balagué

  2. University of Vic-Central University of Catalonia, Vic, Spain

    Laura Dempere-Marco

Authors
  1. Marta Balagué
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  2. Laura Dempere-Marco
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Correspondence to Marta Balagué .

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Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Balagué, M., Dempere-Marco, L. (2016). Multi-item Working Memory Capacity: What Is the Role of the Stimulation Protocol?. 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_31

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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