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Phase Control of Coupled Neuron Oscillators

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Artificial Neural Networks and Machine Learning – ICANN 2013 (ICANN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8131))

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Abstract

The phase response of an Izhikevich neuron integrator/resonator model based oscillator to a weak short-duration external input pulse is used to determine the Izhikevich model dynamic parameter values needed to attain a specified phase difference between coupled neuron oscillators working at the same natural oscillation frequency. The design of a new type of neuron oscillator-chain based artificial central pattern generator for the coordinated four-legged animal walking movement is proposed as an application.

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Author information

Authors and Affiliations

  1. Computer Systems Department, University of Aizu, Aizu Wakamatsu City, Fukushima Prefecture, Japan

    Mayumi Irifune & Robert H. Fujii

Authors
  1. Mayumi Irifune
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  2. Robert H. Fujii
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Editor information

Editors and Affiliations

  1. Faculty Automation,, Technical University of Sofia, 8 St. Kl. Ohridski Blvd., 1000, Sofia, Bulgaria

    Valeri Mladenov

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  3. Institute of Neural Information Processing, University of Ulm, 89075, Ulm, Germany

    Günther Palm

  4. Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  5. Department of Computer Science, University of Milano, Via Comelico, 39, 20135, Milano, Italy

    Bruno Appollini

  6. Knowledge Engineering, School of Computing and Mathematical Sciences, Auckland University of Technology, 120 Mayoral Drive, 3rd floor, 1010, Auckland, New Zealand

    Nikola Kasabov

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© 2013 Springer-Verlag Berlin Heidelberg

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Irifune, M., Fujii, R.H. (2013). Phase Control of Coupled Neuron Oscillators. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_37

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  • DOI: https://doi.org/10.1007/978-3-642-40728-4_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40727-7

  • Online ISBN: 978-3-642-40728-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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