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.
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Kuroda, K., Fujiwara, K., Ikeguchi, T. (2012). Identification of Neural Network Structure from Multiple Spike Sequences. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_23
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DOI: https://doi.org/10.1007/978-3-642-34481-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34480-0
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