Introduction to Computational Neuroscience
A mathematical introduction to how neurons encode and process information, taught at MIT by the Seung Lab. The course covers convolution, correlation, linear systems theory, probability and information theory, signal detection theory, game theory, and reinforcement learning, then applies these tools to neural coding, with emphasis on the visual system. It also covers the Hodgkin-Huxley model and related descriptions of neural excitability, stochastic models of ion channel behavior, cable theory, and models of synaptic transmission. Materials include MIT OpenCourseWare lecture notes, problem sets, and readings that combine mathematics with experimental neuroscience. No certificate is offered since this is an OpenCourseWare release, but all course materials are free to use. Suited to students with some background in calculus and probability who want a rigorous quantitative foundation in how the brain processes information.