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Biologically Inspired Cognitive Architectures (BICA) for Young Scientists pp 47–52Cite as

Simulation of Learning in Neuronal Culture

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 449))

Abstract

The neuronal cultures in vitro plated on the multielectrode arrays is an important object of research in modern neurosciences. The protocol of culture stimulation which allows to receive a required response of culture on a selected electrode in response to stimulation is known. Such stimulation protocol can be considered as the elementary form of learning. In this study we create model of neuronal culture in vitro and obtained primary data on ability of such model to learning through stimulation.

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References

  1. Shahaf, G., Marom, S.: Learning in networks of cortical neurons. J. Neurosci. 21(22), 8782–8788 (2001)

    Google Scholar 

  2. Le Feber, J., Stegenga, J., Rutten, W.L.: The effect of slow electrical stimuli to achieve learning in cultured networks of rat cortical neurons. PLoS ONE 5(1), e8871 (2010)

    Article  Google Scholar 

  3. Pimashkin, A., Gladkov, A., Mukhina, I., Kazantsev, V.: Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays. Front. Neural Circ. 7 (2013)

    Google Scholar 

  4. Nava, I., Tessadori, J., Chiappalone, M.: Change of network dynamics in a neuro-robotic system. In: Biomimetic and Biohybrid Systems (pp. 225–237). Springer, Berlin (2014)

    Google Scholar 

  5. Gritsun, T.A., Le Feber, J., Stegenga, J., Rutten, W.L.: Network bursts in cortical cultures are best simulated using pacemaker neurons and adaptive synapses. Biol. Cybern. 102(4), 293–310 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  6. Gritsun, T., le Feber, J., Stegenga, J., Rutten, W.L.: Experimental analysis and computational modeling of interburst intervals in spontaneous activity of cortical neuronal culture. Biol. Cybern. 105(3–4), 197–210 (2011)

    Article  Google Scholar 

  7. Baltz, T., Herzog, A., Voigt, T.: Slow oscillating population activity in developing cortical networks: models and experimental results. J. Neurophysiol. 106(3), 1500–1514 (2011)

    Article  Google Scholar 

  8. Massobrio, P., Pasquale, V., Martinoia, S.: Self-organized criticality in cortical assemblies occurs in concurrent scale-free and small-world networks. Sci. Rep. 5

    Google Scholar 

  9. Yu, H., Guo, X., Wang, J., Deng, B., Wei, X.: Spike coherence and synchronization on Newman-Watts small-world neuronal networks modulated by spike-timing-dependent plasticity. Physica A 419, 307–317 (2015)

    Article  MathSciNet  Google Scholar 

  10. Yu, H., Guo, X., Wang, J., Deng, B., Wei, X.: Vibrational resonance in adaptive small-world neuronal networks with spike-timing-dependent plasticity. Physica A 436, 170–179 (2015)

    Article  MathSciNet  Google Scholar 

  11. Gewaltig, M.O., Diesmann, M.: NEST (neural simulation tool). Scholarpedia 2(4), 1430 (2007)

    Article  Google Scholar 

  12. Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Networks 14(6), 1569–1572 (2003)

    Article  MathSciNet  Google Scholar 

  13. Chao, Z.C., Bakkum, D.J., Wagenaar, D.A., Potter, S.M.: Effects of random external background stimulation on network synaptic stability after tetanization. Neuroinformatics 3(3), 263–280 (2005)

    Article  Google Scholar 

  14. Yger, P., El Boustani, S., Destexhe, A., Frégnac, Y.: Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons. J. Comput. Neurosci. 31(2), 229–245 (2011)

    Article  Google Scholar 

  15. Miles, R., Traub, R.D., Wong, R.K.: Spread of synchronous firing in longitudinal slices from the CA3 region of the hippocampus. J. Neurophysiol. 60(4), 1481–1496 (1988)

    Google Scholar 

  16. Gütig, R., Aharonov, R., Rotter, S., Sompolinsky, H.: Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J. Neurosci. 23(9), 3697–3714 (2003)

    Google Scholar 

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Acknowledgements

This work was supported by the Russian Science Foundation, Grant No 15-11-30014.

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Authors and Affiliations

  1. NRC Kurchatov Institute, Moscow, Russia

    Alexey Degterev & Mikhail Burtsev

  2. NRNU MEPhI, Moscow, Russia

    Alexey Degterev & Mikhail Burtsev

Authors
  1. Alexey Degterev
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  2. Mikhail Burtsev
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Corresponding author

Correspondence to Alexey Degterev .

Editor information

Editors and Affiliations

  1. Department 22 “Cybernetics”, National Research Nuclear University, Moscow, Russia

    Alexei V. Samsonovich

  2. “MEPhI”, National Research Nuclear University, Moscow, Russia

    Valentin V. Klimov

  3. “MEPhI”, National Research Nuclear University, Moscow, Russia

    Galina V. Rybina

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© 2016 Springer International Publishing Switzerland

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Degterev, A., Burtsev, M. (2016). Simulation of Learning in Neuronal Culture. In: Samsonovich, A., Klimov, V., Rybina, G. (eds) Biologically Inspired Cognitive Architectures (BICA) for Young Scientists . Advances in Intelligent Systems and Computing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-319-32554-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-32554-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32553-8

  • Online ISBN: 978-3-319-32554-5

  • eBook Packages: EngineeringEngineering (R0)

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