**Language:** This course is given in English.

**Goal:** (i) The students should get to know a number of models and methods in
computational neuroscience. (ii) They should understand the mathematics of these
methods.

**Content:** This lecture covers models of selforganization in neural systems, in
particular addressing vision (receptive fields, neural maps, invariances) and
associative memory (Hopfield network).

**Format:** There is a lecture, which provides the content, and a tutorial,
where you solve exercises and can deepen your understanding of the content. The
exercises are solved in the tutorial in a group effort, not at home. The students
therefore have the opportunity to meet already at 9:00 to work on the exercises on
their own.

**Requirements:** The mathematical level of the course is mixed but generally high. The
tutorial is almost entirely mathematical. Mathematics required include calculus (functions,
derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner
product, orthogonal vectors, basis systems, ...), and a bit of probability theory
(probabilities, probability densities, Bayes' theorem, ...).

**Exam:** This course will be concluded with an oral exam.

- Kursleiter/in: Laurenz Wiskott