Identification of Neural Network Structure from Multiple Spike Sequences

  • Kaori Kuroda
  • Kantaro Fujiwara
  • Tohru Ikeguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)


In this paper, we propose a new estimation method of direction of the connectivity between neurons in neural network only from multiple spike sequences. The proposed method is based on the spike time metric, or a statistical measure to quantify a degree of dissimilarity between two spike sequences, and the partialization analysis. To resolve this issue, we modify the definition of the conventional cost in the spike time metric. Then, the proposed method can effectively estimate direction of connectivity between neurons. To check the validity, we applied the proposed method to multiple spike sequences that are produced by a mathematical neural network model. As a result, our method can estimate the neural network structure and the direction of couplings with high accuracy.


Spike time metric Multiple spike sequences Direction of connectivity Partialization analysis Point processes 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kaori Kuroda
    • 1
  • Kantaro Fujiwara
    • 1
  • Tohru Ikeguchi
    • 1
    • 2
  1. 1.Graduate School of Science and EngineeringSaitama UniversityJapan
  2. 2.Saitama University Brain Science InstituteSakura-kuJapan

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