Using Computation to Achieve Breakthroughs in Neuroscience

项目摘要 新技术使人们能够以多种时间和空间分辨率惊人地访问神经过程。 然而，回答科学问题所需的数据分析往往依赖于计算 技术是独特的实验，可能必须修改（甚至重新开发），为每个 具体实验。这些都不是可以在一个类学习的技术，而是方法， 思考每个实验和分析中必须考虑的问题。从根本上说， 正在收集数据，但该领域没有得到收集的数据的全部价值;学生需要 额外的训练，以便成功地提取实验中存在的完整信息。 学生需要理解实验范式的复杂性和局限性， 复杂性和局限性的计算分析，可以适用于这些范例。更 重要的是，如果一个学生要发展他或她自己的分析，学生需要深入了解 如何定义和导出适当的控制分析。确保实验设计的严谨性， 对这些类型的数据进行后续分析特别困难。我们建议建立一个全面的 为博士前研究生和早期博士后提供培训计划，教他们如何 整合计算分析和技术，以实现神经科学的科学突破。这 培训将使这些学生处于一个非常有利的地位，促进他们的科学事业。此外，委员会认为， 培训计划还将帮助支持、维护和改善强大的跨学科社区， 计算，实验和临床神经科学已经存在于明尼苏达大学。

Project Summary New technologies have enabled amazing access to neural processes at multiple resolutions of time and space. However, the data analyses necessary to answer the scientific questions often depend on computational techniques that are unique to the experiment and may have to be modified (or even developed anew) for each specific experiment. These are not techniques that can be learned in a single class, but rather ways of thinking about problems that must be incorporated into each experiment and each analysis. Fundamentally, the data are being collected, but the field is not getting the full value of the collected data; students need additional training in order to successfully extract the complete information present in their experiments. Students need to understand both the complexities and limitations within experimental paradigms and also the complexities and limitations within computational analyses that can be applied to those paradigms. More importantly, if a student is going to develop his or her own analyses, the student needs a deep understanding of how to define and derive the appropriate control analyses. Ensuring the rigor of the experimental design and the subsequent analyses is particularly difficult for these types of data. We propose to build a comprehensive training program for both predoctoral graduate students and early-stage postdocs, to teach them how to integrate computational analyses and techniques to achieve scientific breakthroughs in neuroscience. This training will place these students in a very strong position for furthering their scientific careers. Furthermore, the training program will also help support, maintain, and improve the strong interdisciplinary community in computational, experimental, and clinical neuroscience that already exists within the University of Minnesota.