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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.
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.
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