TRAINING IN METHODS OF COMPUTATIONAL NEUROSCIENCE

海洋生物实验室计算神经科学方法课程的目标是每年培训22名高级学生和博士后研究员，研究计算神经科学中的基本知识问题，并为他们提供分析和数值工具，以协助他们的研究。 该课程具有独特的特点，如果不是不可能的话，也很难在大学环境中复制。 首先，它继续吸引和教育来自广泛的既定学科的学生，这些学科对神经科学产生影响，包括生物学，认知科学，计算机科学，工程，数学和物理学。 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，学生接触到一个大的访问教师除了接受指令和指导，从董事，住院医师和课程助理。 在大学环境中，不太可能召集这样的教师组合来学习类似的课程。最后，该课程为学生提供了从详细的细胞到更抽象的系统水平模拟神经系统的最先进的计算技术和计算机硬件基础。本课程是一个为期四周的密集讲座和实验系列。 讲座沿着计算复杂性的增加规模进行。 他们最初专注于单个神经元和突触的电生理学和化学动力学。 接下来是关于局部神经元回路和小神经系统的动力学和可塑性的讲座，这些主题说明了单细胞特性如何通过小网络塑造计算。 最后一系列的讲座地址涉及大型神经元系统，特别是皮层的感觉和感知问题的计算，以及抽象模型的新兴应用，以提供一个框架来理解这些系统。该实验室强调计算神经科学成功研究所需的工具谱。 一方面，该课程提供通用模拟软件以及特定于单细胞动力学或单细胞和大型网络的模拟软件的培训。 另一方面，学生接受应用数学领域的培训，如概率论和矩阵代数，这对制定和解决该领域的问题至关重要。

The goal of the Methods in Computational Neuroscience Course at the Marine Biological Laboratory is to train 22 advanced students and post-doctoral fellows a year in the fundamental intellectual issues in computational neuroscience and to avail them with analytical and numerical tools to assist in their studies. The course has unique features that would be hard if not impossible to replicate in a university setting. First, it continues to attract and educate students from a broad range of established disciplines that impact upon neuroscience, including biology, cognitive science, computer science, engineering, mathematics and physics. Secondly, as a means to provide a coherent and relatively complete perspective on the growing field of computational neuroscience, students are exposed to a large visiting faculty in addition to receiving instruction and guidance from directors, scholars-in-residence and course assistants. It is unlikely that such a combination of faculty could be convened for an analogous course in a university setting. Lastly, the course provides students with access to state-of-the-art computational techniques and computer hardware fundamental to simulating neural systems from the detailed cellular through more abstracts systems level. The course is organized as an intensive four week lecture and laboratory series. The lectures progress along an increasing scale of computational complexity. They initially focus on the electrophysiology and chemical dynamics of individual neurons and synapses. This is followed by lectures on the dynamics and plasticity of local neuronal circuits and small nervous systems, topics that illustrate how single-cell properties shape computations by small networks. A final series of lectures addresses computations that involve sensory and perceptual issues in large neuronal systems, particularly cortex, and the emerging application of abstract models to provide a framework for understand these systems. The laboratory emphasizes the spectrum of tools necessary for successful research in computational neuroscience. On the one hand, the course provides training with general purpose simulation software as well as with simulation software that is specific to single cell dynamics, or to both single cells and large networks. On the other hand, students are trained in areas of applied mathematics, such as probability theory and matrix algebra, that are essential to formulating and solving problems in the field.