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Online Estimation and Control of Neuronal Nonlinear Dynamics Based on Data-Driven Statistical Approach

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Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1143))

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Abstract

Due to recent development of measuring and stimulating technology, estimating and controlling neuronal dynamics become more important in neuroscience. A recent numerical study showed that the state of single neuron can not be controlled accurately by feedback control signal based on noisy observable data. Thus, it is essential to estimate hidden states in neuronal dynamics such as membrane potential and channel variable in order to realize accurate control by means of Bayesian statistical approach. For this purpose, we need to estimate the parameters of the dynamical model of neuron online used for the feedback control based on the estimated states. In this study, we propose a method for simultaneously estimating parameters and states online, and determining the control signal based on the estimated membrane potential. We use the particle filter for state estimation in order to deal with non-linear dynamics of neuron. Moreover, parameters of the neuron model are estimated online by using stochastic EM algorithm. We show that feedback control based on estimated membrane potential can be performed with online estimation of state and parameter estimation. Furthermore, we show that the controlling the state of neuron become more accurate when the control signal is determined based on our approach.

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References

  1. Osawa, S., et al.: Optogenetically induced seizure and the longitudinal hippocampal network dynamics. PLoS One 8(4), e60928 (2013)

    Article  Google Scholar 

  2. Otsuka, S., Omori, T.: Estimation of neuronal dynamics based on sparse modeling. Neural Networks 109, 137–146 (2019)

    Article  Google Scholar 

  3. Omori, T., Hukushima, K.: Extracting nonlinear spatiotemporal dynamics in active dendrites using data-driven statistical approach. In: Journal of Physics: Conference Series, vol. 699, p. 012011. IOP Publishing (2016)

    Google Scholar 

  4. Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Phys. Rev. E 79(4), 040901 (2009)

    Article  MathSciNet  Google Scholar 

  5. Inoue, H., Omori, T.: Bayesian estimation of neural systems using particle-Gibbs. In: Proceedings of the 2017 International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, pp. 68–73. ACM (2017)

    Google Scholar 

  6. Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Networks 15(5), 1063–1070 (2004)

    Article  Google Scholar 

  7. Kitagawa, G.: Non-Gaussian state-space modeling of nonstationary time series. J. Am. Stat. Assoc. 82(400), 1032–1041 (1987)

    MathSciNet  MATH  Google Scholar 

  8. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)

    Article  Google Scholar 

  9. Neal, R.M., Hinton, G.E.: A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan, M.I. (ed.) Learning in Graphical Models, pp. 355–368. Springer, Dordrecht (1998). https://doi.org/10.1007/978-94-011-5014-9_12

    Chapter  Google Scholar 

  10. Sato, M.A., Ishii, S.: On-line EM algorithm for the normalized Gaussian network. Neural Comput. 12(2), 407–432 (2000)

    Article  Google Scholar 

  11. Liang, P., Klein, D.: Online EM for unsupervised models. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 611–619 (2009)

    Google Scholar 

  12. Cappé, O., Moulines, E.: On-line expectation-maximization algorithm for latent data models. J. Roy. Stat. Soc. B (Stat. Methodol.) 71(3), 593–613 (2009)

    Article  MathSciNet  Google Scholar 

  13. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1–22 (1977)

    MathSciNet  MATH  Google Scholar 

  14. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

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Acknowledgments

This work is partially supported by Grants-in-Aid for Scientific Research for Innovative Areas “Initiative for High-Dimensional Data driven Science through Deepening of Sparse Modeling” [JSPS KAKENHI Grant No. JP25120010] and for Scientific Research [JSPS KAKENHI Grant No. JP16K00330], and a Fund for the Promotion of Joint International Research (Fostering Joint International Research) [JSPS KAKENHI Grant No. JP15KK0010] from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and Core Research for Evolutional Science and Technology (CREST) [Grant No. JPMJCR1914], Japan Science and Technology Agency, Japan.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501, Japan

    Shuhei Fukami & Toshiaki Omori

Authors
  1. Shuhei Fukami
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  2. Toshiaki Omori
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Correspondence to Toshiaki Omori .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Fukami, S., Omori, T. (2019). Online Estimation and Control of Neuronal Nonlinear Dynamics Based on Data-Driven Statistical Approach. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_64

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  • DOI: https://doi.org/10.1007/978-3-030-36802-9_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36801-2

  • Online ISBN: 978-3-030-36802-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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