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

Computational Neuroscience Mathematical and Statistical Perspectives

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.
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