Computational Neuroscience: Mathematical and Statistical Perspectives
Posted: 5 Apr 2018
Date Written: March 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.
Suggested Citation: Suggested Citation
Kass, Robert E. and Amari, Shun-Ichi and Arai, Kensuke and Brown, Emery and Diekman, Casey O. and Diesmann, Markus and Doiron, Brent and Eden, Uri and Fairhall, Adrienne and Fiddyment, Grant M. and Fukai, Tomoki and Grün, Sonja and Harrison, Matthew T. and Helias, Moritz and Nakahara, Hiroyuki and Teramae, Jun-nosuke and Thomas, Peter J. and Reimers, Mark and Rodu, Jordan and Rotstein, Horacio G. and Shea-Brown, Eric and Shimazaki, Hideaki and Shinomoto, Shigeru and Yu, Byron M. and Kramer, Mark, Computational Neuroscience: Mathematical and Statistical Perspectives (March 2018). Annual Review of Statistics and Its Application, Vol. 5, Issue 1, pp. 183-214, 2018, Available at SSRN: https://ssrn.com/abstract=3157010 or http://dx.doi.org/10.1146/annurev-statistics-041715-033733
Jordan Rodu
University of Pennsylvania - The Wharton School ( email )
3641 Locust Walk
Philadelphia, PA 19104-6365
United States