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118398 |
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|a dc
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|a Goldman, Mark S
|e author
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|a Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
|e contributor
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|a McGovern Institute for Brain Research at MIT
|e contributor
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|a Fee, Michael Sean
|e contributor
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|a Goldman, Mark S
|e contributor
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|a Fee, Michale Sean
|e contributor
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|a Fee, Michale Sean
|e author
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|a Computational training for the next generation of neuroscientists
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| 246 |
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|a Computational training for the next generation of neuroscientists
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| 260 |
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|b Elsevier,
|c 2018-10-09T18:44:40Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/118398
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|a 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.
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|a en_US
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|a Article
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|t Current Opinion in Neurobiology
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