We are currently teaching in two regular courses:

Statistical Learning in the Nervous System

Lecturer: Gergő Orbán
(spring semester)

The course is a Computational Cognitive Science course which introduces the audience to computational principles for understanding cognitive processes. The course builds up a general mathematical framework in which cognitive phenomena can be interpreted. The course builds on math. It is beneficial to have an understanding of linear algebra, probability theory, and calculus, but it is NOT a prerequisite as mathematical notions are introduced during the course. The course alternates between mathematical underpinning of theory and their applications to cognitive phenomena.

Computational Neuroscience

Lecturers: Gergő Orbán, Balázs Ujfalussy and Zoltán Somogyvári.
(fall semester)

The course focuses on basic principles of computational neuroscience: the biophysics of neurons; action potential generation, transduction, and transmission; simple networks of neurons, and their modifications by learning; and the ways the nervous system encodes and decodes information about the environment and about the body.

 

Further sources for learning about related topics

Balázs Ujfalussy has kindly collected the courses in computational neuroscience and computational cognitive science taught in Budapest in a single web page.