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.

项目总结/摘要 该提案是关于T90/R90培训计划的五年延期， 布兰迪斯大学的计算神经科学。方案要求 资助6名本科生，4名符合NRSA资格的研究生和2名非NRSA学生 NRSA合格的研究生每年。我们的目标是识别早期职业学生 并为他们提供所需的正规和非正规教育和培训， 发展成为下一代计算/定量神经科学家。这是 在即将到来的“大数据”时代， 收集和分析神经科学数据变得越来越严格， 定量的长期目标和目的是增加科学家的人数， 在神经科学方面具有强大的定量和建模技能，因为这一群体将是 需要了解大脑在健康和疾病中的功能。21名培训教师拥有 从人类认知到细胞和分子神经科学， 学员可以在许多分析层次上研究各种各样的问题。 选择培训教师是因为他们表现出对 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。学生将参加课程， 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，并在各种独立的研究中使用这些 项目将针对两批潜在的受训者：1）具有较强 有定量方法经验，希望从事神经科学工作者。（二） 来自心理学，神经科学和生物学的个人希望学习 采用定量和计算方法作为他们解决问题的能力的一部分， 神经科学中的重要问题。除了课程和实验室 研究，学生和受训人员将从事大量的其他活动 旨在提高他们的口语技能，写作技能，以及与 其他科学家。所有学生和学员将接受培训， 他还将与其他机构的资深科学家进行互动。学员 将包括代表性不足的人口。