Interdisciplinary Training in Computational Neuroscience

计算神经科学跨学科培训

• 批准号: 7213051
• 负责人: ROBERT E KASS
• 金额: $ 3.88万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-30 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7213051
• 关键词: Interdisciplinary; Training; Computational; Neuroscience

DESCRIPTION (provided by applicant): Neuroscience increasingly requires the integration of sophisticated experimental and quantitative approaches. The development of increasingly sophisticated models and the analysis of massive sources of data routinely push neuroscientists to the limits of their quantitative and analytical abilities. In this way, computational neuroscience has become an element of the mainstream of neuroscience research. However, few training programs have adapted to this increase in the importance of quantitative approaches in neuroscience, leaving many students under prepared to exploit future opportunities in the field. Here we propose training programs involving faculty from more than ten departments at Carnegie Mellon University and the University of Pittsburgh that seek to move computational neuroscience into the mainstream of the field. Specifically, we seek to: 1) expose hundreds of undergraduate students in biomedical fields and hundreds of students in quantitative disciplines to computational neuroscience each year. 2) develop a comprehensive research and education program that provide excellent in depth training in computational neuroscience to 10-15 undergraduates from a variety of disciplines each year 3) develop and extend the training of our graduate students to include substantial additional education in computational neuroscience and 4) expose a group of a dozen talented students primarily from other institutions to training and research in computational neuroscience. This last group of students will consist mostly of students from groups underrepresented in the field of computational neuroscience. The training will be broad and interdisciplinary including biological and psychological approaches on the experimental side and statistical, computational and mathematical on the quantitative side.

描述（由申请人提供）：神经科学越来越需要复杂的实验和定量方法的整合。日益复杂的模型的发展和对大量数据源的分析通常会将神经科学家推向他们定量和分析能力的极限。通过这种方式，计算神经科学已经成为神经科学研究的主流元素。然而，很少有培训计划适应神经科学定量方法重要性的增加，使许多学生准备不足，以利用该领域的未来机会。在这里，我们提出了培训计划，涉及来自卡内基梅隆大学和匹兹堡大学的十多个部门的教师，寻求将计算神经科学纳入该领域的主流。具体而言，我们力求：1）每年让数百名生物医学领域的本科生和数百名定量学科的学生接触计算神经科学。2)制定一个全面的研究和教育计划，每年为来自不同学科的10-15名本科生提供计算神经科学的深入培训; 3）开发和扩展我们研究生的培训，包括计算神经科学的大量额外教育; 4）让一群主要来自其他机构的十几名有才华的学生接受计算神经科学的培训和研究。最后一组学生将主要由计算神经科学领域代表性不足的学生组成。培训将是广泛的和跨学科的，包括实验方面的生物学和心理学方法，以及定量方面的统计，计算和数学。