Training in Methods of Computational Neuroscience (MCN)

摘要 伍兹海洋生物实验室计算神经科学(MCN)课程的教学方法 霍尔，MA提供了一个独特的培训机会，让学生掌握尖端技能和概念 了解脑的生物功能和神经机制的计算方法基础 细胞、电路和行为。MCN是一门密集的讲座和计算机实验室课程，目标是一名有才华的 在生物和数学科学方面具有互补专业知识的学生混合在一起。MCN学生互动 与国际知名科学家进行一对一的培训，并与广泛的 各种计算技术和方法。MCN课程的目标是培训学生1) 在了解大脑功能的计算方法的概念和方法中2)在 神经数据分析的数学基础和技术3)使用计算工具 解决神经生物学中的基本问题，4)使用计算方法诊断和 治疗人类大脑疾病和5)促进跨学科合作，从而导致长期 互动和指导。本课程提供关于如何使用计算的深入指导 使用实验技术解决神经生物学中的基本问题的方法 从分子到行为的各种生物尺度。这是通过实践培训来完成的 基于模型的神经、统计和理论分析的数学基础和技术 在神经元、网络和大脑系统的机械性描述的背景下的数据。MCN课程 不断更新与新的实验进展相关的计算主题，如解剖学 微结构和全脑范围的连接性，使用电学和光学技术进行高密度测量 利用光遗传学对神经回路进行记录和因果扰动。这些新主题包括状态空间 轨迹分析、深度网络、压缩感知、高级统计推理、流形学习、 降维、大数据集分析，包括脑部广泛成像和大规模细胞类型 特定的连接性。另一项创新是更加重视计算方法，以 了解和解决大脑疾病，包括计算精神病学的新兴领域。 这门课程的核心是与一群敬业的教员进行激烈互动的环境，其中许多人 他们花了几周甚至整个月的时间和学生一起工作。在课程的后半部分，学生们 在教师和教学团队的指导下，专注于新颖的尖端研究项目 助理。这门课程培养了一个大学的、国际化的环境，通过以下方式促进学生的职业生涯 为学生提供网络机会，促进长期互动、指导和 合作。

ABSTRACT The Methods in Computational Neuroscience (MCN) course at the Marine Biological Laboratory in Woods Hole, MA provides a unique training opportunity to equip students with cutting edge skills and conceptual foundations of computational methods to understanding the biological function and neural mechanisms of brain cells, circuits, and behavior. MCN is an intense lecture and computer laboratory course targeted to a talented mix of students with complementary expertise in biological and mathematical sciences. MCN students interact one-on-one with internationally renowned scientists and carry out hands-on, project-based training with a wide variety of computational techniques and approaches. The goals of the MCN course are to train students 1) in the concepts and methods of computational approaches to understanding brain function 2) in the mathematical foundations and techniques for the analysis of neural data 3) to use computational tools to solve fundamental questions in neurobiology, 4) to use computational methods to diagnose and treat human brain disorders and 5) to foster interdisciplinary collaborations that lead to long term interactions and mentorship. The course provides in-depth instruction on how to use computational approaches to solve fundamental problems in neurobiology using experimental techniques applied across a wide range of biological scales from molecules to behavior. This is accomplished through hands-on training in the mathematical foundations and techniques for the model-based, statistical and theoretical analysis of neural data in the context of mechanistic descriptions of neurons, networks, and brain systems. The MCN course continually updates its computational topics that are relevant to new experimental advances, such anatomical connectivity at microstructural and brain-wide scales, high density measurements using electrical and optical recording, and causal perturbations of neural circuits using optogenetics. These new topics include state-space trajectory analysis, deep networks, compressed sensing, advanced statistical inference, manifold learning, dimensionality reduction, analysis of large data sets including brain wide imaging and large scale cell-type specific connectivity. An additional innovation is an increased emphasis on computational approaches to understanding and addressing brain disorders, including the emerging field of computational psychiatry. Central to the course is an environment of intense interactions with a group of dedicated faculty, many of whom devote weeks or even the entire month to work with students. In the last half of the course students devote concentrated effort to novel cutting edge research projects mentored by a team of faculty and teaching assistants. The course fosters a collegial, international environment that advances student careers by providing students with networking opportunities that lead to long-term interactions, mentorship, and collaborations.