9.29J / 8.261J Introduction to Computational Neuroscience, Spring 2002
Neurons and some equations used to model neuronal behavior. (Image courtesy of Seung and Schneider Laboratories, MIT Department of Brain and Cognitive Sciences.)
Highlights of this Course
This course offers a thorough introduction to and grounding in the current state of computational neuroscience, emphasizing critical thinking. A comprehensive
reading list surveys the field of computational neuroscience and establishes a base of information that future researchers will be able to utilize throughout their careers. The
assignments help students focus on the important aspects of the topics covered in the class. An extensive list of links to
related resources is also provided.
Course Description
Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.