Interdisciplinary Training in Computational Neuroscience

申请者描述(由申请人提供)：本申请请求续签对卡内基梅隆大学(CMU)和匹兹堡大学(PITT)计算神经科学(TPCN)本科生和研究生培训计划的支持，以及为本科生举办的计算神经科学暑期班的支持，该课程将向来自美国各地的学院和大学的学生开放。TPCN将由认知神经基础中心(CNBC)管理，CNBC是由CMU和PIT联合运营的伞形组织，成立于1994年，旨在促进对大脑功能神经机制的跨学科研究，目前由107名教职员工组成，在20个系任职。神经科学的研究对于解决神经和心理健康障碍的原因至关重要。如果神经科学领域要继续快速发展，神经科学家必须使用、理解和开发新技术，获取和分析越来越大的数据集，并更直接地与神经生物系统的复杂性作斗争。在这一努力中，广泛开发和采用新的计算方法已成为取得进展的关键。TPCN项目的主要目标是帮助培养具有强大量化技能的新一代跨学科神经科学家。第二个目标是通过加强本科生和研究生的培训，将计算和数据分析原理纳入神经科学领域。学员将在垂直整合、跨学科的研究团队中工作。研究生将学习认知神经科学、神经生理学和系统神经科学的课程；他们将满足他们选择的定量方法的深度要求(涉及计算机科学、工程、数学和/或统计学)；他们将至少在一个实验实验室拥有丰富的经验；他们将参加匹兹堡计算神经科学界的期刊俱乐部和研讨会。为期一年的本科生将选修数学、计算机编程、统计学和神经科学；他们将额外选修一门神经科学或心理学课程和一门计算神经科学课程；他们将完成一项为期一年的研究项目。此外，他们还将完成暑期项目。参加暑期课程的本科生将完成一系列关于计算神经科学主题的讲座，包括MatLab教程、统计学方法、微分方程基础和神经编码思想，并将完成一个研究项目。所有受训人员都将接受负责任地进行研究的培训。在5年的资助期间，TPCN将支持20名NRSA研究生、10名非NRSA研究生、30名本科生一年制研究员和60名本科生暑期研究员。 公共卫生相关性：神经科学研究对于解决神经和精神健康疾病的原因至关重要。如果神经科学领域要继续快速发展，神经科学家必须使用、理解和开发新技术，获取和分析越来越大的数据集，并更直接地与神经生物系统的复杂性作斗争。这些培训计划的主要目标将是帮助培养具有强大量化技能的新一代跨学科神经科学家。

DESCRIPTION (provided by applicant): This application requests renewal of support for undergraduate and graduate training programs in computational neuroscience (TPCN) at both Carnegie Mellon University (CMU) and the University of Pittsburgh (Pitt), and for a summer school in computational neuroscience for undergraduates, which will be available to students coming from colleges and universities throughout the United States. The TPCN will administered by the Center for the Neural Basis of Cognition (CNBC), an umbrella organization operated jointly by CMU and Pitt that was established in 1994 to foster interdisciplinary research on the neural mechanisms of brain function, which now comprises 107 faculty having appointments in 20 departments. Research in neuroscience is crucial for attacking the causes of neurological and mental health disorders. If the field of neuroscience is to continue its rapid advance, neuroscientists must use, understand, and develop new technologies, acquire and analyze ever larger data sets, and grapple more directly with the complexity of neurobiological systems. In this effort, widespread development and adoption of new computational methods has become essential to progress. The primary goal of TPCN programs is to help train a new generation of interdisciplinary neuroscientists with strong quantitative skills. A second goal is the incorporation of computational and data analytic principles into the field of neuroscience through enhanced training at the undergraduate and graduate level. Trainees will work in vertically integrated, cross-disciplinary research teams. Graduate students will take courses in cognitive neuroscience, neurophysiology, and systems neuroscience; they will satisfy a depth requirement in quantitative methodology of their choice (involving computer science, engineering, mathematics, and/or statistics); they will have extended experience in at least one experimental laboratory; and they will take part in journal clubs and seminars within the large Pittsburgh computational neuroscience community. Year-long undergraduates will take courses in mathematics, computer programming, statistics, and neuroscience; they will take an additional course in neuroscience or psychology and a course in computational neuroscience; and they will complete a year-long research project. In addition, they will complete the summer program. Undergraduate trainees in the summer program will sit through a series of lectures on topics in computational neuroscience, including tutorials in Matlab, statistical methods, fundamentals of differential equations, and ideas of neural coding, and will complete a research project. All trainees will receive training in responsible conduct of research. Across 5 years of funding, TPCN will support 20 NRSA graduate students, 10 non-NRSA graduate students, 30 undergraduate year-long fellows, and 60 undergraduate summer fellows. PUBLIC HEALTH RELEVANCE: Research in neuroscience is crucial for attacking the causes of neurological and mental health disorders. If the field of neuroscience is to continue its rapid advance, neuroscientists must use, understand, and develop new technologies, acquire and analyze ever larger data sets, and grapple more directly with the complexity of neurobiological systems. The primary goal of these training programs will be to help train a new generation of interdisciplinary neuroscientists with strong quantitative skills.