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August 12, 2018 - September 1, 2018
Computational Neuroscience is a rapidly evolving field whose methods and techniques are critical for understanding and modelling the brain, and also for designing and interpreting experiments. Mathematical modeling is an essential tool to cut through the vast complexity of neurobiological systems and their many interacting elements.
The course teaches the central ideas, methods, and practices of modern computational neuroscience through a combination of lectures and hands-on project work. During the course’s mornings, distinguished international faculty deliver lectures on topics across the entire breadth of experimental and computational neuroscience. For the remainder of the time, students work on research projects in teams of 2 to 3 people under close supervision of expert tutors and faculty. Research projects are proposed by faculty before the course, and include the modeling of neurons, neural systems, and behaviour, the analysis of state-of-the-art neural data (behavioural data, multi-electrode recordings, calcium imaging data, connectomics data, etc.), and the development of theories to explain experimental observations.
This course is designed for graduate students and postdoctoral fellows from a variety of disciplines, including neuroscience, physics, electrical engineering, computer science, mathematics and psychology. Students are expected to have a keen interest and basic background in neurobiology, a solid foundation in mathematics, as well as some computing experience. A four-day pre-school in mathematics and programming is offered for students that want to catch up on their math and programming skills.