Training in Methods of Computational Neuroscience

 描述（由申请人提供）：在计算神经科学（MCN）课程的方法在伍兹霍尔，MA海洋生物实验室提供了一个独特的培训机会，使学生掌握尖端的技能和计算方法的概念基础，适用于了解脑细胞，电路和行为的动力学和功能。MCN是一个密集的讲座和实验室课程，针对有才华和多样化的学生。MCN学生与国际知名科学家进行一对一的互动，并通过各种计算技术和方法进行实践，基于项目的培训。MCN课程的一个基本目标是为学生进行研究做好准备，这些研究导致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BRAIN计划的科学愿景直接一致。该课程提供了关于如何使用计算方法来解决神经生物学中的基本问题的深入指导，并通过不同的实验技术来桥接广泛的尺度。这是通过在神经元、网络和大脑系统的机械描述的背景下，对神经数据的基于模型的分析的数学基础和技术进行实践培训来实现的。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特性的细胞类型，提供有关神经元连接性和结构特性信息的高分辨率解剖学，大规模神经元记录高密度电气和光学技术，以及因果干预，如光遗传学。课程中的新主题包括新兴的计算方法，如压缩传感，高级统计推断，最佳实验设计，降维和大型数据集分析。另一个创新是越来越重视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，抑郁症和帕金森病。该课程的独特之处在于为学生提供与一群专门教师的密集互动，其中许多人花费数周甚至整个月的时间与学生合作。该课程通过为学生提供网络机会，从而促进学生的职业生涯，从而促进长期的互动，指导和合作。

 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.