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Study on Decision Algorithm of Neurons’ Synchronization Based on Neurodynamics

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Advances in Neural Networks – ISNN 2012 (ISNN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7367))

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Abstract

In the paper, based on the neurodynamics theory and the existing neuron synchronization’s research findings, the dynamic phase function which can describe the neuron’s discharge characteristics is defined and revised, and the neuron synchronization decision algorithm and procedure based on the dynamic phase function is put forward. The synchronization characteristics and rule of the two uncoupled HR neurons are discussed by the synchronization decision algorithm. Compared with other decision indexes and algorithms, the neuron synchronization decision algorithm based on the dynamic phase function can not only judge the three common synchronization types: asynchronization, the generalized synchronization, and the phase synchronization, but also quantitatively solve the critical value of the neurons’ realizing synchronization such as the stimulation current amplitude.

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Author information

Authors and Affiliations

  1. School of Management and Economics, Xi’an Technological University, 710032, Xi’an, China

    Xiaoying He

  2. Information Engineering Department, Engineering University of Chinese Armed Police Force, 710086, Xi’an, China

    Yueping Peng

Authors
  1. Xiaoying He
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  2. Yueping Peng
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Editor information

Editors and Affiliations

  1. Department of Mechanical & Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Jun Wang

  2. School of Electrical and Computer Engineering, Oklahoma State University, 74078, Stillwater, OK, USA

    Gary G. Yen

  3. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios M. Polycarpou

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© 2012 Springer-Verlag Berlin Heidelberg

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He, X., Peng, Y. (2012). Study on Decision Algorithm of Neurons’ Synchronization Based on Neurodynamics. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_26

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  • DOI: https://doi.org/10.1007/978-3-642-31346-2_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31345-5

  • Online ISBN: 978-3-642-31346-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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