B. M. ’t Hart, T. Achakulvisut, A. Akrami, Bradly Alicea, Ulrik R Beierholm, Gunnar Blohm, Kathryn Bonnen, John S Butler, Brandon Caie, You Cheng, H. Chow, Isaac David, Eric E. J. DeWitt, Jan Drugowitsch, Kshitij Dwivedi, P. Fiquet, Jeremy Forest, Byron Galbraith, Qingling Gu, Pankaj Gupta, Alexandre Hyafil, K. Kording, Arvind Kumar, Patrick Mineault, John D. Murray, Megan A. K. Peters, P. Schrater, C. Stringer, P. Wallisch, B. Wyble
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引用次数: 1
摘要
Neuromatch Academy (https://neuromatch.io/academy)被设计为一个在线暑期学校,在三周内涵盖计算神经科学的基础知识。这些材料涵盖了主流和新兴的计算神经科学工具,它们如何相互补充,并特别关注它们如何帮助我们更好地理解大脑的功能。材料的一个原始组成部分是其对建模选择的关注,即我们如何选择正确的方法,我们如何构建模型,以及我们如何评估模型以确定它们是否提供真正的(有意义的)洞察力。教学材料的元模型组件询问了哪些问题可以通过不同的技术来回答,以及如何有意义地应用它们来深入了解大脑功能。
Neuromatch Academy: a 3-week, online summer school in computational neuroscience
Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.