Training in Methods of Computational Neuroscience

 描述(由申请人提供)：马萨诸塞州伍兹霍尔海洋生物实验室的计算神经科学(MCN)方法课程提供了一个独特的培训机会，使学生掌握用于理解脑细胞、电路和行为的动力学和功能的计算方法的尖端技能和概念基础。MCN是一门密集的讲座和实验室课程-面向有才华和多样化的学生池。MCN学生与国际知名科学家进行一对一的互动，并使用各种计算技术和方法进行基于项目的实践培训。MCN课程的一个基本目标是为学生开展研究做好准备，以1)新的理论和数据分析工具，以及2)理解心理过程的生物学基础的概念基础。这些目标与大脑倡议的科学愿景直接一致。本课程提供关于如何使用计算方法解决神经生物学中的基本问题的深入指导，以及通过不同的实验技术探索的广泛范围的桥梁。这是通过在神经、网络和大脑系统的机械描述的背景下，对神经数据进行基于模型的分析的数学基础和技术方面的动手培训来实现的。MCN课程通过不断更新与新的实验进展相关的计算主题来保持其创新优势，例如识别具有不同细胞属性的细胞类型、提供有关神经元连通性和结构属性信息的高分辨率解剖学、使用高密度电和光学技术的大规模神经元记录以及光遗传学等因果干预。这门课程的新主题包括新兴的计算方法，如压缩传感、高级统计推理、最优实验设计、降维和大数据集分析。另一项创新是更加重视用计算方法来理解和解决大脑疾病，包括精神分裂症、癫痫、成瘾、抑郁症和帕金森氏症。这门课程的独特之处在于，它为学生提供了与一群敬业教师的密集互动，他们中的许多人花了几周甚至整个月的时间与学生一起工作。本课程通过为学生提供网络机会，促进长期互动、指导和合作，从而营造一个促进学生职业生涯发展的大学、国际环境。

 DESCRIPTION (provided by applicant): The Methods in Computational Neuroscience (MCN) course at the Marine Biological Laboratory in Woods Hole, MA provides a singular training opportunity to equip students with cutting edge skills and conceptual foundations of computational methods applied to understanding the dynamics and function of brain cells, circuits, and behavior. MCN is an intense lecture and laboratory course-targeted to a talented and diverse pool of students. 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. A foundational goal of the MCN course is to prepare students to carry out research leading to 1) new theoretical and data analysis tools, and 2) conceptual foundations for understanding of the biological basis of mental processes. These goals directly align with the scientific vision of the BRAIN Initiative. The course provides in-depth instruction on how to use computational approaches to solve fundamental problems in neurobiology, and bridge the wide range of scales probed by different experimental techniques. This is accomplished through hands-on training in the mathematical foundations and techniques for the model- based analysis of neural data, in the context of mechanistic descriptions of neurons, networks, and brain systems. The MCN course maintains its innovative edge by continually updating computational topics that are relevant to new experimental advances, such as the identification of cell types with distinct cellular properties, high-resolution anatomy that provids information about connectivity and structural properties of neurons, large-scale neuronal recordings with high-density electrical and optical techniques, and causal interventions such as optogenetics. New topics in the course include emerging computational approaches such as compressed sensing, advanced statistical inference, optimal experimental design, dimensionality reduction and analysis of large data sets. An additional innovation is an increased emphasis on computational approaches to understanding and addressing brain disorders, including schizophrenia, epilepsy, addiction, depression, and Parkinson's disease. The course is unique in providing students intensive interactions with a group of dedicated faculty, many of whom devote weeks or even the entire month to work with students. 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.