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Training a Spiking Neural Network to Generate Online Handwriting Movements

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

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Abstract

In this paper we developed a spiking neural network model that learns to generate online handwriting movements. The architecture is a feed forward network with one hidden layer. The input layer uses a set of Beta elliptic parameters. The hidden layer contains both excitatory and inhibitory neurons. Whereas the output layer provides the script coordinates x(t) and y(t). The proposed spiking neural network has been trained according to Sander Bohet model. The trained spiking neural network has been successfully tested on MAYASTROUN data base. Also a comparative stady between the proposed spiking neural network and an artificial neural network proposed in a previous work is established.

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Authors and Affiliations

  1. REGIM-Lab.: Research Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, 3038, Sfax, Tunisia

    Mahmoud Ltaief, Hala Bezine & Adel M. Alimi

Authors
  1. Mahmoud Ltaief
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  2. Hala Bezine
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  3. Adel M. Alimi
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Corresponding author

Correspondence to Mahmoud Ltaief .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Ltaief, M., Bezine, H., Alimi, A.M. (2017). Training a Spiking Neural Network to Generate Online Handwriting Movements. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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