Computational Neuroscience: Mathematical and Statistical Perspectives
Document Type
Article
Publication Date
3-7-2018
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.
Identifier
85043465123 (Scopus)
Publication Title
Annual Review of Statistics and Its Application
External Full Text Location
https://doi.org/10.1146/annurev-statistics-041715-033733
e-ISSN
2326831X
ISSN
23268298
First Page
183
Last Page
214
Volume
5
Grant
1555237
Fund Ref
National Science Foundation
Recommended Citation
Kass, Robert E.; Amari, Shun Ichi; Arai, Kensuke; Brown, Emery N.; Diekman, Casey O.; Diesmann, Markus; Doiron, Brent; Eden, Uri T.; Fairhall, Adrienne L.; Fiddyment, Grant M.; Fukai, Tomoki; Grün, Sonja; Harrison, Matthew T.; Helias, Moritz; Nakahara, Hiroyuki; Teramae, Jun Nosuke; Thomas, Peter J.; and Reimers, Mark, "Computational Neuroscience: Mathematical and Statistical Perspectives" (2018). Faculty Publications. 8792.
https://digitalcommons.njit.edu/fac_pubs/8792
