Computational complexity reduction for functional connectivity estimation in large scale neural network

Jeonghun Baek; Shigeyuki Oba; Junichiro Yoshimoto; Kenji Doya; Shin Ishii

doi:10.1007/978-3-319-26555-1_66

Computational complexity reduction for functional connectivity estimation in large scale neural network

Jeonghun Baek
, Shigeyuki Oba
, Junichiro Yoshimoto
, Kenji Doya
, Shin Ishii

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

抄録

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.

本文言語	英語
ホスト出版物のタイトル	Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
編集者	Tingwen Huang, Qingshan Liu, Weng Kin Lai, Sabri Arik
出版社	Springer Verlag
ページ	583-591
ページ数	9
ISBN（印刷版）	9783319265544
DOI	https://doi.org/10.1007/978-3-319-26555-1_66
出版ステータス	出版済み - 2015
外部発表	はい
イベント	22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, トルコ継続期間: 09-11-2015 → 12-11-2015

出版物シリーズ

名前	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
巻	9491
ISSN（印刷版）	0302-9743
ISSN（電子版）	1611-3349

会議

会議	22nd International Conference on Neural Information Processing, ICONIP 2015
国/地域	トルコ
City	Istanbul
Period	09-11-15 → 12-11-15

All Science Journal Classification (ASJC) codes

理論的コンピュータサイエンス
コンピュータサイエンス一般

文献へのアクセス

10.1007/978-3-319-26555-1_66

他のファイルとリンク

引用スタイル

Baek, J., Oba, S., Yoshimoto, J., Doya, K., & Ishii, S. (2015). Computational complexity reduction for functional connectivity estimation in large scale neural network. In T. Huang, Q. Liu, W. K. Lai, & S. Arik (Eds.), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings (pp. 583-591). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9491). Springer Verlag. https://doi.org/10.1007/978-3-319-26555-1_66

Baek, Jeonghun ; Oba, Shigeyuki ; Yoshimoto, Junichiro その他. / Computational complexity reduction for functional connectivity estimation in large scale neural network. Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. editor / Tingwen Huang ; Qingshan Liu ; Weng Kin Lai ; Sabri Arik. Springer Verlag, 2015. pp. 583-591 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Computational complexity reduction for functional connectivity estimation in large scale neural network",

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.",

keywords = "Computational complexity reduction, Functional connectivity, Large scale networks, Two-stage algorithm",

author = "Jeonghun Baek and Shigeyuki Oba and Junichiro Yoshimoto and Kenji Doya and Shin Ishii",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

year = "2015",

doi = "10.1007/978-3-319-26555-1\_66",

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isbn = "9783319265544",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "Tingwen Huang and Qingshan Liu and Lai, \{Weng Kin\} and Sabri Arik",

booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",

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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 T Huang, Q Liu, WK Lai & S Arik (eds), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9491, Springer Verlag, pp. 583-591, 22nd International Conference on Neural Information Processing, ICONIP 2015, Istanbul, トルコ, 09-11-15. https://doi.org/10.1007/978-3-319-26555-1_66

Computational complexity reduction for functional connectivity estimation in large scale neural network. / Baek, Jeonghun; Oba, Shigeyuki; Yoshimoto, Junichiro その他.
Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. ed. / Tingwen Huang; Qingshan Liu; Weng Kin Lai; Sabri Arik. Springer Verlag, 2015. p. 583-591 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9491).

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

TY - GEN

T1 - Computational complexity reduction for functional connectivity estimation in large scale neural network

AU - Baek, Jeonghun

AU - Oba, Shigeyuki

AU - Yoshimoto, Junichiro

AU - Doya, Kenji

AU - Ishii, Shin

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Computational complexity reduction

KW - Functional connectivity

KW - Large scale networks

KW - Two-stage algorithm

UR - https://www.scopus.com/pages/publications/84958542220

UR - https://www.scopus.com/pages/publications/84958542220#tab=citedBy

U2 - 10.1007/978-3-319-26555-1_66

DO - 10.1007/978-3-319-26555-1_66

M3 - Conference contribution

AN - SCOPUS:84958542220

SN - 9783319265544

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 583

EP - 591

BT - Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings

A2 - Huang, Tingwen

A2 - Liu, Qingshan

A2 - Lai, Weng Kin

A2 - Arik, Sabri

PB - Springer Verlag

T2 - 22nd International Conference on Neural Information Processing, ICONIP 2015

Y2 - 9 November 2015 through 12 November 2015

ER -

Baek J, Oba S, Yoshimoto J, Doya K, Ishii S. Computational complexity reduction for functional connectivity estimation in large scale neural network. In Huang T, Liu Q, Lai WK, Arik S, editors, Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Springer Verlag. 2015. p. 583-591. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26555-1_66

Computational complexity reduction for functional connectivity estimation in large scale neural network

抄録

出版物シリーズ

会議

All Science Journal Classification (ASJC) codes

文献へのアクセス

他のファイルとリンク

フィンガープリント

引用スタイル