Using Computation to Achieve Breakthroughs in Neuroscience

利用计算实现神经科学的突破

• 批准号: 10220673
• 负责人: BENJAMIN Y HAYDEN
• 金额: $ 37.33万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10220673
• 关键词: Neurosciences

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.

项目摘要 在多个时间和空间分辨率下，新技术使对神经过程的惊人访问能够访问。 但是，回答科学问题所需的数据通常取决于计算 实验独有的技术，可能必须修改（甚至是重新开发） 具体实验。这些不是可以在单一类中学到的技术，而是 考虑必须将问题纳入每个实验和每个分析的问题。从根本上， 正在收集数据，但该字段没有获得收集到的数据的全部价值；学生需要 其他培训以成功提取实验中存在的完整信息。 学生需要了解实验范式中的复杂性和局限性 可以应用于这些范式的计算分析中的复杂性和局限性。更多的 重要的是，如果学生要进行自己的分析，那么学生就需要深入了解 如何定义和得出适当的控制分析。确保实验设计的严谨性和 对于这些类型的数据，随后的分析特别困难。我们建议建立一个全面的 培训计划的研究生和早期阶段的培训计划，以教他们如何 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​​于促进科学职业的非常强大的位置。此外， 培训计划还将帮助支持，维护和改善强大的跨学科社区 明尼苏达大学内已经存在的计算，实验和临床神经科学。