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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
Editors	Tingwen Huang, Qingshan Liu, Weng Kin Lai, Sabri Arik
Publisher	Springer Verlag
Pages	583-591
Number of pages	9
ISBN (Print)	9783319265544
DOIs	https://doi.org/10.1007/978-3-319-26555-1_66
Publication status	Published - 2015
Externally published	Yes
Event	22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey Duration: 09-11-2015 → 12-11-2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9491
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Neural Information Processing, ICONIP 2015
Country/Territory	Turkey
City	Istanbul
Period	09-11-15 → 12-11-15

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

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 et al. / 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",

language = "English",

isbn = "9783319265544",

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

publisher = "Springer Verlag",

pages = "583--591",

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, Turkey, 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 et al.
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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

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SN - 9783319265544

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

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

A2 - Huang, Tingwen

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

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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

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