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Neuronal Synfire Chain via Moment Neuronal Network Approach

  • Xiangnan He
  • Wenlian Lu
  • Jianfeng Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

Abstract

In this letter, we use a novel method to analyse the stability synchronisation propagation in neuronal networks via the moment neuronal network approach developed recently. Here, the stability of synfire chain is twofold, including the stability of both the synchronisation in the cluster and the asynchronisation out of the cluster. Under the framework of the moment neuronal network, we model the dynamics of the Pearson correlation coefficients via evolution field equations. Thus, we study the stability of synfire chain via looking into the attractors of the model. Based on analytic and numerical approaches, In particular, we point out that the variance of the neuronal spike rate should be updated with the synfire propagation. Also, we find out that the balance between the excitation and inhibition PSPs and a suitable size of the cluster can enhance the stability of synfire chain.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiangnan He
    • 1
  • Wenlian Lu
    • 1
  • Jianfeng Feng
    • 2
  1. 1.Center for Computational Systems BiologyFudan UniversityShanghaiP.R. China
  2. 2.Center for Scientific ComputingWarwick UniversityCoventryUK

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