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 journalArticlepeer-review

14 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 languageEnglish (US)
Pages (from-to)183-214
Number of pages32
JournalAnnual Review of Statistics and Its Application
Volume5
DOIs
StatePublished - 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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