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Improving Recurrent Neural Network Performance Using Transfer Entropy

  • Oliver Obst
  • Joschka Boedecker
  • Minoru Asada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6444)

Abstract

Reservoir computing approaches have been successfully applied to a variety of tasks. An inherent problem of these approaches, is, however, their variation in performance due to fixed random initialisation of the reservoir. Self-organised approaches like intrinsic plasticity have been applied to improve reservoir quality, but do not take the task of the system into account. We present an approach to improve the hidden layer of recurrent neural networks, guided by the learning goal of the system. Our reservoir adaptation optimises the information transfer at each individual unit, dependent on properties of the information transfer between input and output of the system. Using synthetic data, we show that this reservoir adaptation improves the performance of offline echo state learning and Recursive Least Squares Online Learning.

Keywords

Machine learning recurrent neural network information theory reservoir computing guided self-organisation 

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References

  1. 1.
    Lukosevicius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)CrossRefzbMATHGoogle Scholar
  2. 2.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Jaeger, H., Haas, H.: Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science 304(5667), 78–80 (2004)CrossRefGoogle Scholar
  4. 4.
    Boedecker, J., Obst, O., Mayer, N.M., Asada, M.: Initialization and self-organized optimization of recurrent neural network connectivity. HFSP Journal 3(5), 340–349 (2009)CrossRefGoogle Scholar
  5. 5.
    Triesch, J.: A gradient rule for the plasticity of a neuron’s intrinsic excitability. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 65–70. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Steil, J.J.: Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks 20(3), 353–364 (2007)CrossRefzbMATHGoogle Scholar
  7. 7.
    Hebb, D.O.: The organization of behavior: a neuropsychological theory. Lawrence Erlbaum Associates, Mahwah (1949)Google Scholar
  8. 8.
    Prokopenko, M.: Guided self-organization. HFSP Journal 3(5), 287–289 (2009)CrossRefGoogle Scholar
  9. 9.
    Steil, J.J.: Backpropagation-decorrelation: Recurrent learning with O(N) complexity. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), vol. 1, pp. 843–848 (2004)Google Scholar
  10. 10.
    Hayes, M.H.: Chapter 9.4 Recursive Least Squares. In: Statistical Digital Signal Processing and Modeling. Wiley, Chichester (1996)Google Scholar
  11. 11.
    Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical Report 148, GMD – German National Research Institute for Computer Science (2001)Google Scholar
  12. 12.
    Jaeger, H.: Adaptive nonlineaer systems identification with echo state networks. In: Advances in Neural Information Processing Systems, pp. 609–615 (2003)Google Scholar
  13. 13.
    Schreiber, T.: Measuring information transfer. Physical Review Letters 85(2), 461–464 (2000)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hajnal, M., Lőrincz, A.: Critical echo state networks. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 658–667. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: Detecting non-trivial computation in complex dynamics. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) ECAL 2007. LNCS (LNAI), vol. 4648, pp. 895–904. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Oliver Obst
    • 1
    • 2
  • Joschka Boedecker
    • 3
    • 4
  • Minoru Asada
    • 3
    • 4
  1. 1.CSIRO ICT CentreAdaptive SystemsEppingAustralia
  2. 2.School of Information TechnologiesThe University of SydneyAustralia
  3. 3.Department of Adaptive Machine SystemsOsaka UniversitySuitaJapan
  4. 4.JST ERATO Asada Synergistic Intelligence ProjectSuitaJapan

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