NEU 437/MOL 437 Computational Neuroscience
Faculty: Carlos D. Brody
Introduction to a mathematical description of how networks of neurons can represent information and compute with it. Course will survey computational modeling and data analysis methods for neuroscience. Topics will include representation of visual information, spatial navigation, short-term memory, and decision-making. Two 90 minute lectures, one laboratory. Lectures in common with NEU 537.
Prerequisites and Restrictions:
Basic linear algebra, probability, ordinary differential equations, and some programming experience, or permission of the instructor..