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
The course is
run primarily for new Gatsby students for whom it is mandatory.
Students, postdocs and faculty from outside the unit are welcome to
attend, but should note that the course carries no formal course unit
value. It is, however, suitable for Skills Development credit as
required by the Research Councils; see here
more for details.
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.