Graduate Course (PhD program):Introduction to Computational Neuroscience |
(Winter term; Block course during 1 week; 15 hours total )
This course is intendend for graduate students in
the Neurosciences with an interest in Computational Neuroscience.
The contents are similar to that of
the course `Neural Networks and Biological Modeling'.
However, since the course is aimed
for experimentalists in neuroscience,
some mathematically more difficult chapters have
been removed. Nevertheless an elementary knowledge of
differential equations is required.
See also the page of the Doctoral School in
Neuroscience.
Objectifs:
Be introduced to the basic questions
and models in Computational Neurosciences:
single-cell modeling, Hebbian unsupervised learning,
reward-based learning, models of associative memory,
population dynamics, and
spike-timing dependent synaptic
plasticity.
Contents
Exam: Oral exam.
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