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Residual Activity in the Neurons Allows SOMs to Learn Temporal Order

  • Conference paper
Artificial Neural Networks: Biological Inspirations – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3696))

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Abstract

A novel activity associated to the neurons of a SOM, called Residual Activity (RA), is defined in order to enlarge into the temporal domain the capabilities of a Self-Organizing Map for clustering and classifying the input data when it offers a temporal relationship. This novel activity is based on the biological plausible idea of partially retaining the activity of the neurons for future stages, that increases their probability to become the winning neuron for future stimuli. The proposed paper also proposes two quantifiable parameters for evaluating the performances of algorithms that aim to exploit temporal relationship of the input data for classification. Special designed benchmarks with spatio-temporal relationship are presented in which the proposed new algorithm, called TESOM (acronym for Time Enhanced SOM), has demonstrated to improve the temporal index without decreasing the quantization error.

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Authors and Affiliations

  1. DISAM – Universidad Politécnica Madrid,  

    Pascual Campoy & Carlos J. Vicente

Authors
  1. Pascual Campoy
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  2. Carlos J. Vicente
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Campoy, P., Vicente, C.J. (2005). Residual Activity in the Neurons Allows SOMs to Learn Temporal Order. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_59

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  • DOI: https://doi.org/10.1007/11550822_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28752-0

  • Online ISBN: 978-3-540-28754-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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