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Advanced Graduate Training Program in Theoretical Neuroscience

Advanced Graduate Training Program in Theoretical Neuroscience
理论神经科学高级研究生培训计划
批准号:
9296209
负责人:
Laurence F. Abbott
金额:
$18.73万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-07-01 至 2019-06-30

项目摘要

项目成果

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中文摘要
翻译
描述(由申请人提供):我们要求继续一项理论神经科学培训计划,并提供资金支持4名博士预科生。培训将在哥伦比亚大学理论神经科学中心进行,由该中心的5名(很快将达到6名)教师、另外一名客座理论研究员和哥伦比亚大学神经科学系的17名实验人员提供支持。理论中心提供了一个特殊的环境,在这里博士预科,博士后和教师研究人员进行广泛的互动和合作。大多数受训者是哥伦比亚大学神经生物学和行为学研究生项目的成员(少数来自其他研究生项目),他们将学习课程并满足该项目的要求。这将在理论神经科学的必修和选修课程的大量选择中得到加强。培训项目的一个显著特点是与哥伦比亚大学和其他地方的优秀实验实验室广泛合作,使我们的学员能够获得他们需要的数据,更重要的是,使他们能够第一手地学习实验神经科学的技术和方法。我们的目标是培养优秀的神经科学家,能够应用复杂的数据分析方法和有见地的建模方法来处理实验数据。
英文摘要
DESCRIPTION (provided by applicant): We request continuation of a training program in Theoretical Neuroscience with funds to support 4 predoctoral trainees. Training will occur at the Center for Theoretical Neuroscience at Columbia University, supported by the 5 (soon to be 6) faculty at the center, by an additional extended visiting faculty researcher in theory, and by 17 experimentalists from the Columbia Department of Neuroscience. The Theory Center provides an exceptional environment in which pre-doctoral, post-doctoral and faculty researchers interact and collaborate extensively. Most trainees with be members of the Columbia graduate program in Neurobiology and Behavior (with a small number drawn from other graduate programs) and will take the courses and satisfy the requirements of that program. This will be augmented with both required and a large selection of elective courses in theoretical neuroscience. A distinctive feature of the training program is extensive collaborations with outstanding experimental laboratories both at Columbia and elsewhere that allows our trainees access to the data they need and also, importantly, permits them to learn, first-hand, the techniques and approaches of experimental neuroscience. Our aim is to produce outstanding neuroscientists capable of applying sophisticated methods of data analysis and insightful modeling approaches to experimental data.
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