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International Conference on Artificial Neural Networks

ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions pp 137–148Cite as

Echo State Property of Neuronal Cell Cultures

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11731))

Abstract

Physical reservoir computing (PRC) utilizes the nonlinear dynamics of physical systems, which is called a reservoir, as a computational resource. The prerequisite for physical dynamics to be a successful reservoir is to have the echo state property (ESP), asymptotic properties of transient trajectory to driving signals, with some memory held in the system. In this study, the prerequisites in dissociate cultures of cortical neuronal cells are estimated. With a state-of-the-art measuring system of high-dense CMOS array, our experiments demonstrated that each neuron exhibited reproducible spike trains in response to identical driving stimulus. Additionally, the memory function was estimated, which found that input information in the dynamics of neuronal activities in the culture up to at least 20 ms was retrieved. These results supported the notion that the cultures had ESP and could thereby serve as PRC.

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References

  1. Appeltant, L., et al.: Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011). https://doi.org/10.1038/ncomms1476

    Article  Google Scholar 

  2. Bakkum, D.J., et al.: The axon initial segment is the dominant contributor to the neuron’s extracellular electrical potential landscape. Adv. Biosyst. 3(2), 1800308 (2019). https://doi.org/10.1002/adbi.201800308

    Article  Google Scholar 

  3. Brewer, G.J., Torricelli, J., Evege, E., Price, P.: Optimized survival of hippocampal neurons in B27-supplemented neurobasal\(^{\rm TM}\), a newserum-free medium combination. J. Neurosci. Res. 35(5), 567–576 (1993). https://doi.org/10.1002/jnr.490350513

    Article  Google Scholar 

  4. Brunner, D., Soriano, M.C., Mirasso, C.R., Fischer, I.: Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2013). https://doi.org/10.1038/ncomms2368

    Article  Google Scholar 

  5. Buonomano, D.V., Maass, W.: State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10(2), 113 (2009). https://doi.org/10.1038/nrn2558

    Article  Google Scholar 

  6. Dranias, M.R., Ju, H., Rajaram, E., VanDongen, A.M.: Short-term memory in networks of dissociated cortical neurons. J. Neurosci. 33(5), 1940–1953 (2013). https://doi.org/10.1523/JNEUROSCI.2718-12.2013

    Article  Google Scholar 

  7. Durstewitz, D., Deco, G.: Computational significance of transient dynamics in cortical networks. Eur. J. Neurosci. 27(1), 217–227 (2008). https://doi.org/10.1111/j.1460-9568.2007.05976.x

    Article  Google Scholar 

  8. Fernando, C., Sojakka, S.: Pattern recognition in a bucket. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS, vol. 2801, pp. 588–597. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39432-7_63

    Chapter  Google Scholar 

  9. Goel, A., Buonomano, D.V.: Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments. Philos. Trans. R. Soc. B: Biol. Sci. 369(1637), 20120460 (2014). https://doi.org/10.1098/rstb.2012.0460

    Article  Google Scholar 

  10. Hales, C.M., Rolston, J.D., Potter, S.M.: How to culture, record and stimulate neuronal networks on micro-electrode arrays (meas). JoVE (J. Vis. Exp.) (39), e2056 (2010). https://doi.org/10.3791/2056

  11. Jaeger, H.: Identification of behaviors in an agent’s phase space. Citeseer (1995)

    Google Scholar 

  12. Jaeger, H.: Short term memory in echo state networks, vol. 5. GMD-Forschungszentrum Informationstechnik (2001)

    Google Scholar 

  13. Jaeger, H.: Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, vol. 5. GMD-Forschungszentrum Informationstechnik Bonn (2002)

    Google Scholar 

  14. Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004). https://doi.org/10.1126/science.1091277

    Article  Google Scholar 

  15. Jimbo, Y., Kawana, A., Parodi, P., Torre, V.: The dynamics of a neuronal culture of dissociated cortical neurons of neonatal rats. Biol. Cybern. 83(1), 1–20 (2000). https://doi.org/10.1007/PL00007970

    Article  Google Scholar 

  16. Johnson, H.A., Goel, A., Buonomano, D.V.: Neural dynamics of in vitro cortical networks reflects experienced temporal patterns. Nat. Neurosci. 13(8), 917 (2010). https://doi.org/10.1038/nn.2579

    Article  Google Scholar 

  17. Laje, R., Buonomano, D.V.: Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16(7), 925 (2013). https://doi.org/10.1038/nn.3405

    Article  Google Scholar 

  18. Lu, Z., Hunt, B.R., Ott, E.: Attractor reconstruction by machine learning. Chaos: Interdisc. J. Nonlinear Sci. 28(6), 061104 (2018). https://doi.org/10.1063/1.5039508

    Article  MathSciNet  Google Scholar 

  19. Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002). https://doi.org/10.1162/089976602760407955

    Article  MATH  Google Scholar 

  20. Manjunath, G., Jaeger, H.: Echo state property linked to an input: exploring a fundamental characteristic of recurrent neural networks. Neural Comput. 25(3), 671–696 (2013). https://doi.org/10.1162/NECO_a_00411

    Article  MathSciNet  MATH  Google Scholar 

  21. Nakajima, K.: Muscular-hydrostat computers: physical reservoir computing for octopus-inspired soft robots. In: Shigeno, S., Murakami, Y., Nomura, T. (eds.) Brain Evolution by Design. DCA, pp. 403–414. Springer, Tokyo (2017). https://doi.org/10.1007/978-4-431-56469-0_18

    Chapter  Google Scholar 

  22. Nakajima, K., Hauser, H., Kang, R., Guglielmino, E., Caldwell, D.G., Pfeifer, R.: Computing with a muscular-hydrostat system. In: 2013 IEEE International Conference on Robotics and Automation, pp. 1504–1511. IEEE (2013). https://doi.org/10.1109/ICRA.2013.6630770

  23. Nakajima, K., Hauser, H., Kang, R., Guglielmino, E., Caldwell, D.G., Pfeifer, R.: A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm. Front. Comput. Neurosci. 7, 91 (2013). https://doi.org/10.3389/fncom.2013.00091

    Article  Google Scholar 

  24. Nakajima, K., Hauser, H., Li, T., Pfeifer, R.: Information processing via physical soft body. Sci. Rep. 5, 10487 (2015). https://doi.org/10.1038/srep10487

    Article  Google Scholar 

  25. Nakajima, K., Hauser, H., Li, T., Pfeifer, R.: Exploiting the dynamics of soft materials for machine learning. Soft Robot. 5(3), 339–347 (2018). https://doi.org/10.1089/soro.2017.0075

    Article  Google Scholar 

  26. Nakajima, K., Li, T., Hauser, H., Pfeifer, R.: Exploiting short-term memory in soft body dynamics as a computational resource. J. R. Soc. Interface 11(100), 20140437 (2014). https://doi.org/10.1098/rsif.2014.0437

    Article  Google Scholar 

  27. Nettleton, J.S., Spain, W.J.: Linear to supralinear summation of AMPA-mediated EPSPs in neocortical pyramidal neurons. J. Neurophysiol. 83(6), 3310–3322 (2000)

    Article  Google Scholar 

  28. Potter, S.M., DeMarse, T.B.: A new approach to neural cell culture for long-term studies. J. Neurosci. Methods 110(1–2), 17–24 (2001). https://doi.org/10.1016/S0165-0270(01)00412-5

    Article  Google Scholar 

  29. Quiroga, R.Q., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16(8), 1661–1687 (2004). https://doi.org/10.1162/089976604774201631

    Article  MATH  Google Scholar 

  30. Rabinovich, M., Huerta, R., Laurent, G.: Transient dynamics for neural processing. Science 321(5885), 48–50 (2008). https://doi.org/10.1126/science.1155564

    Article  Google Scholar 

  31. Verstraeten, D., Schrauwen, B., d’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391–403 (2007). https://doi.org/10.1016/j.neunet.2007.04.003

    Article  MATH  Google Scholar 

  32. Victor, J.D., Purpura, K.P.: Nature and precision of temporal coding in visual cortex: a metric-space analysis. J. Neurophysiol. 76(2), 1310–1326 (1996). https://doi.org/10.1152/jn.1996.76.2.1310

    Article  Google Scholar 

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Acknowledgments

This paper is based on results obtained from a project (Exploration of Neuromorphic Dynamics towards Future Symbiotic Society) commissioned by NEDO, KAKENHI grant (17K20090), AMED (JP18dm0307009) and Asahi Glass Foundation. We thank Hitachi UTokyo Laboratory, Hitachi, Ltd. for fruitful discussions. K. N. was supported by JST PRESTO Grant Number JPMJPR15E7, Japan and KAKENHI No. JP18H05472, No. 16KT0019, and No. JP15K16076.

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Authors and Affiliations

  1. Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan

    Tomoyuki Kubota

  2. Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan

    Kohei Nakajima & Hirokazu Takahashi

Authors
  1. Tomoyuki Kubota
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  2. Kohei Nakajima
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  3. Hirokazu Takahashi
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Correspondence to Tomoyuki Kubota .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Kubota, T., Nakajima, K., Takahashi, H. (2019). Echo State Property of Neuronal Cell Cultures. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_13

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  • DOI: https://doi.org/10.1007/978-3-030-30493-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30492-8

  • Online ISBN: 978-3-030-30493-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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