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2015 | OriginalPaper | Chapter

Computational Complexity Reduction for Functional Connectivity Estimation in Large Scale Neural Network

Authors : JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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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Metadata
Title
Computational Complexity Reduction for Functional Connectivity Estimation in Large Scale Neural Network
Authors
JeongHun Baek
Shigeyuki Oba
Junichiro Yoshimoto
Kenji Doya
Shin Ishii
Copyright Year
2015
DOI
https://doi.org/10.1007/978-3-319-26555-1_66

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