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To the Question of Learnability of a Spiking Neuron with Spike-Timing-Dependent Plasticity in Case of Complex Input Signals

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Biologically Inspired Cognitive Architectures (BICA) for Young Scientists

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

Abstract

Results of investigations of learnability of a spiking neuron in case of complex input signals which encode binary vectors are presented. The disadvantages of the supervised learning protocol with stimulating the neuron by current impulses in desired moments of time are analyzed.

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References

  1. Gütig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)

    Article  Google Scholar 

  2. Mitra, S., Fusi, S., Indiveri, G.: Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI. IEEE Trans. Biomed. Circuits Syst. 3(1)

    Google Scholar 

  3. Kukin, K., Sboev, A.: Comparison of learning methods for spiking neural networks. Opt. Mem. Neural Netw. Inf. Opt. 24(2), 123–129 (2015)

    Article  Google Scholar 

  4. Morrison, A., Diesmann, M., Gerstner, W.: Phenomenological models of synaptic plasticity based on spike timing. Biol. Cybern. 98, 459–478 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Legenstein, R., Naeger, C., Maass, W.: What can a neuron learn with spike-timing-dependent plasticity. Neural Comput. 17, 2337–2382 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  6. Maass, W., Markram, H.: Synapses as dynamic memory buffers. Neural Netw. 15, 155–161 (2002)

    Article  Google Scholar 

  7. Bi, G.-Q., Poo, M.-M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24(1), 139–166 (2001)

    Article  Google Scholar 

  8. Gewaltig, M.-O., Diesmann, M.: Nest (neural simulation tool). Scholarpedia 2(4), 1430 (2007)

    Article  Google Scholar 

  9. Sboev, A., Vlasov, D., Serenko, A., Rybka, R., Moloshnikov, I.: A comparison of learning abilities of spiking networks with different spike timing-dependent plasticity forms. J. Phys: Conf. Ser. 681(1), 012013 (2016)

    Google Scholar 

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Acknowledgements

This work was partially supported by RFBR grant 16-37-00214/16.

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Authors and Affiliations

  1. MEPhI National Research Nuclear University, Moscow, Russia

    Alexander Sboev & Danila Vlasov

  2. NRC “Kurchatov Institute”, Moscow, Russia

    Alexander Sboev, Alexey Serenko, Roman Rybka & Ivan Moloshnikov

Authors
  1. Alexander Sboev
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  2. Danila Vlasov
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  3. Alexey Serenko
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  4. Roman Rybka
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  5. Ivan Moloshnikov
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Corresponding author

Correspondence to Alexander Sboev .

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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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Sboev, A., Vlasov, D., Serenko, A., Rybka, R., Moloshnikov, I. (2016). To the Question of Learnability of a Spiking Neuron with Spike-Timing-Dependent Plasticity in Case of Complex Input Signals. 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_26

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  • DOI: https://doi.org/10.1007/978-3-319-32554-5_26

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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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