Training in Methods of Computational Neuroscience

描述（由申请人提供）：在本提案中，我们要求继续支持自1988年以来每年在海洋生物实验室提供的计算神经科学课程方法。本课程的目标是每年培训24名高级学生和博士后研究员，研究计算神经科学的基本问题，为他们提供分析工具，以协助他们的研究。该课程有几个独特的特点，这将是很难，如果不是不可能在大学环境中复制。首先，它继续吸引和教育来自广泛的影响神经科学的既定学科的学生，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，作为一种手段，以提供一个连贯的和相对完整的角度对计算神经科学的不断增长的领域，学生接触到一个大型的访问教师，从该领域的领导者绘制，以及，从课程主任，驻地教师和课程助理接受指令和指导。这是不可能的，这样的教师组合可以召集类似的课程在大学设置。最后，该课程为学生提供了从详细的细胞到更抽象的系统水平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。该课程是一个密集的4周讲座/实验室系列。前三周包括每天的正式讲座，沿着辅导和实验练习。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.