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Autapse Modulated Bursting

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

In this paper we present a model of autapses which are synapses connecting axons and dendrites of the same neuron and feeding back axonal action potentials to the own dendritic tree. The timely physiological self-inhibitory function they may serve, indicated by the spike-timing dependent plasticity rule, provides a potential negative feedback mechanism to control the dynamical properties of neurons. The model autapse is applied to a neuron with conductance-based minimal model to construct a fast-slow buster. Three types of bursting that the burster exhibits are analyzed geometrically through phase portraits to illustrate the modulation competence of the autapse model on bursting.

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Author information

Authors and Affiliations

  1. Department of Information and Control Engineering, Tongji University, Shanghai, 200092, China

    Guang-Hong Wang & Ping Jiang

  2. School of Informatics, University of Bradford, Bradford, BD7 1DP, UK

    Ping Jiang

Authors
  1. Guang-Hong Wang
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  2. Ping Jiang
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Editor information

Editors and Affiliations

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

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, GH., Jiang, P. (2006). Autapse Modulated Bursting. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_52

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  • DOI: https://doi.org/10.1007/11759966_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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