Undergraduate and Graduate Training in Computational Neuroscience

计算神经科学本科生和研究生培训

• 批准号: 10001483
• 负责人: PAUL MILLER
• 金额: $ 13.3万
• 依托单位: BRANDEIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001483
• 关键词: Big Data; Biology; Brain; Cognition; Computing Methodologies; Data; Data Analyses; Disease; Funding; Goals; Health; Human; Individual; Institution; Laboratory Research; Learning; Methods; Modeling; Molecular; National Research Service Awards; Nervous system structure; Neurons; Neurosciences; Prospective cohort; Psychology; Research; Research Project Grants; Science; Scientist; Senior Scientist; Students; Synapses; Teacher Professional Development; Techniques; Training; Training Programs; Training and Education; Underrepresented Populations; Universities; Work; Writing; career; cohort; computational neuroscience; design; experience; graduate student; informal learning; interdisciplinary collaboration; model building; next generation; programs; skills; statistics; undergraduate student

Project Summary/Abstract This proposal is for a five-year renewal of a T90/R90 training program in Computational Neuroscience at Brandeis University. The proposed program requests funding to support 6 undergraduates, 4 NRSA eligible graduate students, and 2 non- NRSA eligible graduate students each year. Our goal is to identify early career students and provide them the formal and informal education and training they will need to develop into the next generation of computational/quantitative neuroscientists. This is particularly important in the upcoming era of “Big Data” as the techniques needed to collect and analyze neuroscience data are becoming increasingly rigorous and quantitative. The long-term goals and objectives are to increase the number of scientists with strong quantitative and modeling skills in neuroscience, as this cohort will be needed to understand brain function in health and disease. The 21 training faculty have research expertise from human cognition to cellular and molecular neuroscience, so a wide range of research problems, at numerous levels of analysis are available to trainees. The training faculty were chosen because they have demonstrated commitment to the use of theoretical and computational methods and interdisciplinary collaboration to understand the nervous system in health and disease. Students will take courses in statistics, data analysis, computational neuroscience, obtain skills in building models of neurons, synapses, and networks, and employ these in a variety of independent research projects. Two cohorts of prospective trainees will be targeted: 1) Individuals with strong prior experience with quantitative methods who wish to work in neuroscience. 2) Individuals in coming from psychology, neuroscience and biology who wish to learn to employ quantitative and computational methods as part of their ability to tackle important problems in neuroscience. In addition to course work and laboratory research, students and trainees will be engaged in a large number of other activities designed to enhance their speaking skills, writing skills, and ability to collaborate with other scientists. All students and trainees will receive training in the responsible conduct of science, and will interact with senior scientists at other institutions. Trainees from underrepresented populations will be included.

项目概要/摘要 该提案旨在将 T90/R90 培训计划延长五年 布兰代斯大学的计算神经科学。 资助 6 名本科生、4 名符合 NRSA 资格的研究生和 2 名非 每年都有符合 NRSA 资格的研究生，我们的目标是识别早期职业学生。 并为他们提供所需的正式和非正式教育和培训 发展成为下一代计算/定量神经科学家。 在即将到来的“大数据”时代尤为重要，因为所需的技术 收集和分析神经科学数据变得越来越严格 长期目标是增加科学家的数量。 在神经科学方面具有强大的定量和建模技能，因为该群体将 需要了解健康和疾病中的大脑功能 21 名培训教师拥有 从人类认知到细胞和分子神经科学的研究专业知识，所以 受训者可以获得多种分析层次的研究问题。 选择培训教师是因为他们表现出了对培训的承诺 使用理论和计算方法以及跨学科合作 了解健康和疾病中的神经系统 学生将学习以下课程。 统计学、数据分析、计算神经科学，获得建立模型的技能 神经元、突触和网络，并将其应用于各种独立研究 项目将针对两批潜在的受训者：1）具有较强能力的个人。 希望从事神经科学工作的具有定量方法经验的人 2) 来自心理学、神经科学和生物学领域、希望学习 采用定量计算方法作为他们解决问题的能力的一部分 除了课程作业和实验室之外，神经科学中的重要问题。 研究、学生和实习生将从事大量其他活动 旨在提高他们的口语能力、写作能力以及与他人合作的能力 其他科学家将接受负责的培训。 科学行为，并将与其他机构的高级科学家互动。 来自代表性不足的人群将被包括在内。