Computational training for the next generation of neuroscientists
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GoldmanFee_CompNeuroTraining_Submitted_all[1].pdf
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772.07 KB
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Author(s)
Goldman, Mark S
Fee, Michale Sean
Alternative Title
Computational training for the next generation of neuroscientists
Date Issued
July 2017
Journal
Current Opinion in Neurobiology
Publisher
Elsevier
Citation
Goldman, Mark S, and Michale S Fee. “Computational Training for the Next Generation of Neuroscientists.” Current Opinion in Neurobiology 46 (October 2017): 25–30 © 2017 Elsevier Ltd
Version
Author's final manuscript
Abstract
Neuroscience research has become increasingly reliant upon quantitative and computational data analysis and modeling techniques. However, the vast majority of neuroscientists are still trained within the traditional biology curriculum, in which computational and quantitative approaches beyond elementary statistics may be given little emphasis. Here we provide the results of an informal poll of computational and other neuroscientists that sought to identify critical needs, areas for improvement, and educational resources for computational neuroscience training. Motivated by this survey, we suggest steps to facilitate quantitative and computational training for future neuroscientists.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.conb.2017.06.007
