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International Conference on Artificial Neural Networks

ICANN 2011: Artificial Neural Networks and Machine Learning – ICANN 2011 pp 159–166Cite as

State Prediction: A Constructive Method to Program Recurrent Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6791))

Abstract

We introduce a novel technique to program desired state sequences into recurrent neural networks in one shot. The basic methodology and its scalability to large and input-driven networks is demonstrated by shaping attractor landscapes, transient dynamics and programming limit cycles. The approach unifies programming of transient and attractor dynamics in a generic framework.

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References

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Author information

Authors and Affiliations

  1. Research Institute for Cognition and Robotics (CoR-Lab) & Faculty of Technology, Bielefeld University, Universitätsstr. 25, 33615, Bielefeld, Germany

    René Felix Reinhart & Jochen Jakob Steil

Authors
  1. René Felix Reinhart
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  2. Jochen Jakob Steil
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Editor information

Editors and Affiliations

  1. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland

    Timo Honkela  & Samuel Kaski  & 

  2. School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK

    Mark Girolami

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© 2011 Springer-Verlag Berlin Heidelberg

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Cite this paper

Reinhart, R.F., Steil, J.J. (2011). State Prediction: A Constructive Method to Program Recurrent Neural Networks. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_20

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  • DOI: https://doi.org/10.1007/978-3-642-21735-7_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21734-0

  • Online ISBN: 978-3-642-21735-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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