Training in Methods of Computational Neuroscience

描述（由申请人提供）：在此提案中，我们要求继续支持自1988年以来每年在海洋生物实验室开设的计算神经科学方法课程。本课程的目标是每年在计算神经科学的基础问题上培养24名高级学生和博士后，为他们提供分析工具来协助他们的研究。这门课程有几个独特的特点，即使不是不可能，也很难在大学环境中复制。首先，它继续吸引和教育来自影响神经科学的广泛学科的学生，包括生物学、认知科学、计算机科学、数学、物理学和工程学。其次，为了在不断发展的计算神经科学领域提供连贯和相对完整的视角，学生们可以接触到来自该领域领导者的大量访问教师，并接受课程主任、常驻教师和课程助理的指导和指导。在大学环境中，这样的教师组合不太可能被召集起来进行类似的课程。最后，该课程为学生提供了从详细的细胞到更抽象的系统级别的神经系统模拟中必不可少的最先进的计算技术和计算机硬件。本课程是一个为期四周的强化讲座/实验系列。前三周包括每天的正式讲座，以及辅导课和实验练习。每周都有一个明确的主题，从基础知识（第1周）到网络（第2周）再到学习和认知（第3周）。第四周专门用于学生项目。我们的目标是向学生展示实验和理论相结合的方法如何带来突破，并使他们熟悉成功所需的分析方法和计算工具。

DESCRIPTION (provided by applicant): In this proposal we request continuation of support for the Methods in Computational Neuroscience Course offered annually at the Marine Biological Laboratory since 1988. The goal of this course is to train 24 advanced students and post-doctoral fellows per year in the fundamental issues in computational neuroscience, providing them with analytical tools to assist in their studies. The course has several unique features, which would be hard if not impossible to duplicate in a university setting. First, it continues to attract and educate students from a broad range of established disciplines that impact on neuroscience, including biology, cognitive science, computer science, mathematics, physics and engineering. Secondly, as a means to providing a coherent and relatively complete perspective on the growing field of computational neuroscience, students are exposed to a large visiting faculty, drawn from leaders in the field, as well as, receiving instruction and guidance from the course directors, resident faculty and course assistants. It is unlikely that such a combination of faculty could be convened for a similar course in a university setting. Finally, the course provides students with access to state-of-art computational techniques and computer hardware essential in the simulation of neural systems from a detailed cellular through more abstract systems level. The course is an intensive 4-week lecture/laboratory series. The first three weeks consist of daily formal lectures, along with tutorials and lab exercises. Each week is devoted to a clear theme, ranging from the basics (Week 1) to networks (Week 2) to learning and cognition (Week 3). Week Four is devoted exclusively to student projects. We aim to show students how combined experimental and theoretical approaches can lead to breakthroughs and make them familiar with the analytic methods and computational tools that they will need for success.