Abstract
Identification of functional connectivity between neurons is an important issue in computational neuroscience. Recently, the number of simultaneously recorded neurons is increasing, and computational complexity to estimate functional connectivity is exploding. In this study, we propose a two-stage algorithm to estimate spike response functions between neurons in a large scale network. We applied the proposed algorithm to various scales of neural networks and showed that the computational complexity is reduced without sacrificing estimation accuracy.
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Baek, J., Oba, S., Yoshimoto, J., Doya, K., Ishii, S. (2015). Computational Complexity Reduction for Functional Connectivity Estimation in Large Scale Neural Network. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_66
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DOI: https://doi.org/10.1007/978-3-319-26555-1_66
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