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.