This course provides an introduction to neuroscience from a computational perspective. The emphasis is on mathematical and information processing models of the brain at a range of levels (synapses to behavior) and timescales (milliseconds to days). Topics include biophysics of single neurons, synapses, dendrites and axons; neural coding at the single cell and population level; dynamics of large networks, including computing with population codes; and learning at the systems and behavioral levels.
There are no formal prerequisites for the course. However, we will be making heavy use of mathematical, statistical and computational methods. Students should feel comfortable with linear algebra, ordinary differential equations, and probability theory at the level found in Boas (Mathematical Methods in the Physical Sciences) or Arfken (Mathematical Methods for Physicists).
*Most of the course material will be drawn from the textbook "Theoretical Neuroscience" by Peter Dayan & Larry Abbott (MIT Press, ISBN 0-262-04199-5) unless specified otherwise.
Theoretical Neuroscience is run primarily for new Gatsby students for whom it is mandatory. However, students, postdocs and faculty from outside the unit are welcome to attend the course although should note that it is has no formal course unit value. The course is suitable for Skills Development credit as required by the Research Councils. See: http://www.grad.ucl.ac.uk/courses/course-details.pht?course_ID=527