Computational Neuroscience: Mathematical and Statistical Perspectives

Robert E. Kass; Shun Ichi Amari; Kensuke Arai; Emery N. Brown; Casey O. Diekman; Markus Diesmann; Brent Doiron; Uri T. Eden; Adrienne L. Fairhall; Grant M. Fiddyment; Tomoki Fukai; Sonja Grün; Matthew T. Harrison; Moritz Helias; Hiroyuki Nakahara; Jun Nosuke Teramae; Peter J. Thomas; Mark Reimers; Jordan Rodu; Horacio G. Rotstein; Eric Shea-Brown; Hideaki Shimazaki; Shigeru Shinomoto; Byron M. Yu; Mark A. Kramer

doi:10.1146/annurev-statistics-041715-033733

Computational Neuroscience: Mathematical and Statistical Perspectives

Robert E. Kass, Shun Ichi Amari, Kensuke Arai, Emery N. Brown, Casey O. Diekman, Markus Diesmann, Brent Doiron, Uri T. Eden, Adrienne L. Fairhall, Grant M. Fiddyment, Tomoki Fukai, Sonja Grün, Matthew T. Harrison, Moritz Helias, Hiroyuki Nakahara, Jun Nosuke Teramae, Peter J. Thomas, Mark Reimers, Jordan Rodu, Horacio G. RotsteinEric Shea-Brown, Hideaki Shimazaki, Shigeru Shinomoto, Byron M. Yu, Mark A. Kramer

Research output: Contribution to journal › Article › peer-review

43 Scopus citations

Abstract

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

Original language	English (US)
Pages (from-to)	183-214
Number of pages	32
Journal	Annual Review of Statistics and Its Application
Volume	5
DOIs	https://doi.org/10.1146/annurev-statistics-041715-033733
State	Published - Mar 7 2018

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

Keywords

Neural data analysis
Neural modeling
Neural networks
Theoretical neuroscience

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10.1146/annurev-statistics-041715-033733

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Kass, R. E., Amari, S. I., Arai, K., Brown, E. N., Diekman, C. O., Diesmann, M., Doiron, B., Eden, U. T., Fairhall, A. L., Fiddyment, G. M., Fukai, T., Grün, S., Harrison, M. T., Helias, M., Nakahara, H., Teramae, J. N., Thomas, P. J., Reimers, M., Rodu, J., ... Kramer, M. A. (2018). Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application, 5, 183-214. https://doi.org/10.1146/annurev-statistics-041715-033733

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abstract = "Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.",

keywords = "Neural data analysis, Neural modeling, Neural networks, Theoretical neuroscience",

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doi = "10.1146/annurev-statistics-041715-033733",

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Kass, RE, Amari, SI, Arai, K, Brown, EN, Diekman, CO, Diesmann, M, Doiron, B, Eden, UT, Fairhall, AL, Fiddyment, GM, Fukai, T, Grün, S, Harrison, MT, Helias, M, Nakahara, H, Teramae, JN, Thomas, PJ, Reimers, M, Rodu, J, Rotstein, HG, Shea-Brown, E, Shimazaki, H, Shinomoto, S, Yu, BM & Kramer, MA 2018, 'Computational Neuroscience: Mathematical and Statistical Perspectives', Annual Review of Statistics and Its Application, vol. 5, pp. 183-214. https://doi.org/10.1146/annurev-statistics-041715-033733

TY - JOUR

T1 - Computational Neuroscience

T2 - Mathematical and Statistical Perspectives

AU - Kass, Robert E.

AU - Amari, Shun Ichi

AU - Arai, Kensuke

AU - Brown, Emery N.

AU - Diekman, Casey O.

AU - Diesmann, Markus

AU - Doiron, Brent

AU - Eden, Uri T.

AU - Fairhall, Adrienne L.

AU - Fiddyment, Grant M.

AU - Fukai, Tomoki

AU - Grün, Sonja

AU - Harrison, Matthew T.

AU - Helias, Moritz

AU - Nakahara, Hiroyuki

AU - Teramae, Jun Nosuke

AU - Thomas, Peter J.

AU - Reimers, Mark

AU - Rodu, Jordan

AU - Rotstein, Horacio G.

AU - Shea-Brown, Eric

AU - Shimazaki, Hideaki

AU - Shinomoto, Shigeru

AU - Yu, Byron M.

AU - Kramer, Mark A.

PY - 2018/3/7

Y1 - 2018/3/7

N2 - Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

AB - Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

KW - Neural data analysis

KW - Neural modeling

KW - Neural networks

KW - Theoretical neuroscience

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EP - 214

JO - Annual Review of Statistics and Its Application

JF - Annual Review of Statistics and Its Application

ER -

Computational Neuroscience: Mathematical and Statistical Perspectives

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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