Advertisement

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)

Abstract

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kuroda, K., Ashizawa, T., Ikeguchi, T.: Estimation of Network Structures only from Spike Sequences. Physica A 390, 4002–4011 (2011)CrossRefGoogle Scholar
  2. 2.
    Victor, J., Purpura, K.: Metric-space Analysis of Spike Trains: Theory, Algorithms and Application. Network 8, 127–164 (1997)zbMATHCrossRefGoogle Scholar
  3. 3.
    Schelter, B., Winterhalder, M., Dahlhaus, R., Kurths, J., Timmer, J.: Partial Phase Synchronization for Multivariate Synchronizing Systems. Phys. Rev. Lett. 96, 208103 (2006)CrossRefGoogle Scholar
  4. 4.
    Smirnov, D., Schelter, B., Winterhalder, M., Timmer, J.: Revealing Direction of Coupling between Neuronal Oscillators from Time Series: Phase Dynamics Modeling versus Partial Directed Coherence. Chaos 17, 013111 (2007)Google Scholar
  5. 5.
    Frenzel, S., Pompe, B.: Partial Mutual Information for Coupling Analysis of Multivariate Time Series. Phys. Rev. Lett. 99, 204101 (2007)CrossRefGoogle Scholar
  6. 6.
    Eichler, M., Dahlhaus, R., Sandkuhler, J.: Partial Correlation Analysis for the Identification of Synaptic Connections. Biol. Cybern. 89, 289–302 (2003)zbMATHCrossRefGoogle Scholar
  7. 7.
    Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569–1572 (2003)CrossRefGoogle Scholar
  8. 8.
    Watts, D.J., Strogatz, S.H.: Collective Dynamics of ’Small-world’ Networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  9. 9.
    Otsu, N.: A Threshold Selection Method from Gray Level Histograms. IEEE T. Syst. Man. Cy. 9, 62–66 (1979)CrossRefGoogle Scholar

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

Personalised recommendations