Using Computation to Achieve Breakthroughs in Neuroscience

项目摘要 新技术使人们能够以多个时间和空间分辨率惊人地接触到神经过程。 然而，回答科学问题所需的数据分析往往依赖于计算 实验所独有的技术，可能需要针对每种技术进行修改(甚至重新开发 具体的实验。这些不是可以在一节课上学到的技术，而是 思考必须纳入每个实验和每个分析中的问题。从根本上说， 正在收集数据，但该字段未获得所收集数据的全部价值；学生需要 为了成功地提取他们实验中存在的完整信息，他们进行了额外的培训。 学生需要理解实验范例的复杂性和局限性，以及 可应用于这些范例的计算分析中的复杂性和局限性。更多 重要的是，如果一个学生要发展他或她自己的分析，这个学生需要深刻的理解 如何定义和得出适当的控制分析。确保实验设计的严格性和 对于这些类型的数据，后续的分析尤其困难。我们建议建立一个全面的 为博士后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，实现神经科学的科学突破。这 培训将使这些学生在进一步发展他们的科学事业方面处于非常有利的地位。此外， 培训计划还将帮助支持、维持和改善在 明尼苏达大学已有的计算、实验和临床神经科学。

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.