• DocumentCode
    1798450
  • Title

    Clustering and synchronous firing of coupled Rulkov maps with STDP for modeling epilepsy

  • Author

    Shibuya, N. ; Unsworth, Charles ; Uwate, Yoko ; Nishio, Yusuke

  • Author_Institution
    Dept. of Electr. & Electr. Eng., Univ. of Tokushima, Tokushima, Japan
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    4302
  • Lastpage
    4307
  • Abstract
    Epilepsy of the neuropsychiatric disorder is provoked from an imbalance in the long-term potentiation (LTP) versus long-term depression (LTD) of the synapses in the hippocampus. The LTP and LTD are replicated by using the Spike Timing Dependent Plasticity (STDP). Additionally, the spiking activity of the synapses in the hippocampus can be approximated by using the Rulkov maps. In our previous study, we considered some easy simulation models which are constructed by using Rulkov maps with STDP. Moreover, these simulation models consist of unidirectionally coupled neurons. In this paper, we consider some easy simulation models with bidirectionally coupled neurons. We explore the effect of unidirectional and bidirectional connection on spiking activity, as basic simulation for constructing the approximate simulation model of epilepsy. From these results, the unidirectional models show high accuracy in-phase/anti-phase synchronization, and it shows divergent relatively early. The bidirectional models show the stable waveform (i.e., non-divergent) for a long term compared to unidirectional models.
  • Keywords
    medical disorders; neural nets; neurophysiology; pattern clustering; LTD; LTP; Rulkov maps synchronous firing; STDP; bidirectional connection; bidirectional models; bidirectionally coupled neurons; clustering; epilepsy; hippocampus synapses; in-phase-anti-phase synchronization; long-term depression; long-term potentiation; neuropsychiatric disorder; spike timing dependent plasticity; synapses spiking activity; unidirectional connection; unidirectional models; Conferences; Joints; Neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889962
  • Filename
    6889962