Advertisement

On the Approach of Ensemble of Interacting Imperfect Models

  • Miroslav Mirchev
  • Ljupco Kocarev
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

Several approaches of ensemble of interacting imperfect models combined based on observed data either by adaptive synchronization, optimized couplings or weighted combining have been recently proposed. In this study we further examine the weighted combining method using the Hindmarsh-Rose (HR) neuron model and the different outcomes that we can expect. We generate data with an HR model usually referred as ‘truth’ and use the data to train an ensemble of HR models with perturbed parameter values, so that together they mimic the truth. The results show that the weights of the ensemble can be learned using data from a truth HR model exhibiting bursting, in order to represent the same bursting behavior as well as other behaviors such as spiking and random bursting.

Keywords

Convex Combination Coupling Coefficient Neuron Model Truth Behavior Imperfect Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was partially supported by project EC Grant #266722.

References

  1. 1.
    C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer, Secaucus, 2006)Google Scholar
  2. 2.
    G. Duane, J. Tribbia, B. Kirtman, Consensus on long-range prediction by adaptive synchronization of models, in EGU General Assembly Conference Abstracts, ed. by D.N. Arabelos, C.C. Tscherning, pp. 13324, April 2009Google Scholar
  3. 3.
    G. Duane, Synchronicity from synchronized chaos. Arxiv.org/abs/1101.2213. (Submitted 2011)Google Scholar
  4. 4.
    L.A. van der Berge, F.M. Selten, W. Wiegerinck, G.S. Duane, A multi-model ensemble method that combines imperfect models through learning. Earth Syst. Dyn. 2(1), 161–177 (2011)CrossRefGoogle Scholar
  5. 5.
    M. Mirchev, G.S. Duane, W.K.S. Tang, L. Kocarev, Improved modeling by coupling imperfect models. Commun. Nonlinear Sci. Numer. Simul. 17(7), 2741–2751 (2012)Google Scholar
  6. 6.
    W. Wiegerinck, F.M. Selten, Supermodeling: combining imperfect models through learning, in NIPS Workshop on Machine Learning for Sustainability (MLSUST), 2011Google Scholar
  7. 7.
    J.L. Hindmarsh, R.M. Rose, A model of neuronal bursting using three coupled first order differential equations. Proc. Roy. Soc. Lond. Ser. B. Biol. Sci. 221(1222), 87–102 (1984)CrossRefGoogle Scholar
  8. 8.
    M.R. Cohen, A. Kohn, Measuring and interpreting neuronal correlations. Nat. Neurosci. 14, 811–819 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dipartimento di Elettronica e TelecomunicazioniPolitecnico di TorinoTurinItaly
  2. 2.Macedonian Academy of Sciences and Arts, Skopje, Macedonia and BioCircuits InstituteUniversity of California, San DiegoLa JollaUSA

Personalised recommendations