Computational training for the next generation of neuroscientists
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 elemen...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Elsevier
2018
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| Online Access: | http://hdl.handle.net/1721.1/118398 https://orcid.org/0000-0001-7539-1745 |
| _version_ | 1826198722231402496 |
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| author | Goldman, Mark S Fee, Michale Sean |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Goldman, Mark S Fee, Michale Sean |
| author_sort | Goldman, Mark S |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:09:07Z |
| format | Article |
| id | mit-1721.1/118398 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:09:07Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1183982022-10-01T01:38:35Z Computational training for the next generation of neuroscientists Computational training for the next generation of neuroscientists Goldman, Mark S Fee, Michale Sean Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research at MIT Fee, Michael Sean Goldman, Mark S Fee, Michale Sean 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. 2018-10-09T18:44:40Z 2018-10-09T18:44:40Z 2017-07 Article http://purl.org/eprint/type/JournalArticle 0959-4388 http://hdl.handle.net/1721.1/118398 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 https://orcid.org/0000-0001-7539-1745 en_US https://doi.org/10.1016/j.conb.2017.06.007 Current Opinion in Neurobiology Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Prof. Fee via Courtney Crummett |
| spellingShingle | Goldman, Mark S Fee, Michale Sean Computational training for the next generation of neuroscientists |
| title | Computational training for the next generation of neuroscientists |
| title_full | Computational training for the next generation of neuroscientists |
| title_fullStr | Computational training for the next generation of neuroscientists |
| title_full_unstemmed | Computational training for the next generation of neuroscientists |
| title_short | Computational training for the next generation of neuroscientists |
| title_sort | computational training for the next generation of neuroscientists |
| url | http://hdl.handle.net/1721.1/118398 https://orcid.org/0000-0001-7539-1745 |
| work_keys_str_mv | AT goldmanmarks computationaltrainingforthenextgenerationofneuroscientists AT feemichalesean computationaltrainingforthenextgenerationofneuroscientists |