Training in Methods of Computational Neuroscience (MCN)

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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.