Computational training for the next generation of neuroscientists

• Published in: Current opinion in neurobiology Vol. 46; pp. 25 - 30
• Main Authors: Goldman, Mark S, Fee, Michale S
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.10.2017
• Subjects: Computational Biology - education; Humans; Neurosciences - education; Surveys and Questionnaires
• ISSN: 0959-4388; 1873-6882
• DOI: 10.1016/j.conb.2017.06.007

•Reports results of a survey on training needs in computational neuroscience.•More quantitative training is needed for students from life science backgrounds.•Experience with real biological data is needed for non-life science students.•A well-organized, centralized repository is needed to host training resources.•Cultural barriers are holding back widespread computational neuroscience training. 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.