Statistical Learning in the Nervous System

(in Hungarian, Statisztikai tanulás az idegrendszerben)

Spring semester, 2022/23

Kurzus információk

Kurzus időpont: keddenként 16:15

First lecture: 28 February 2023

Venue: BME Q épület QBF10

Course language: English

Assignments: please send your homeworks to the following e-mail address:

The course is listed at the following universities:

  • Eötvös University (ELTE), Neptun code: mv2n9044, CCNM17-214
  • Technical University (BME), in masters programmes with code BMETE47MC39; in PhD programmes with code BMETE47D119
  • Pázmány University (PPKE), students can take credits to the class offered by ELTE by accrediting the course after completion
  • we welcome everyone else with personalized administrative procedures if needed

Lecturers: Gergő Orbán, Dávid G. Nagy, Anna Székely
Time: Tuesdays, 4:15 pm – 5:45 pm

The course aims to cover a few topics in the functional description of the nervous system with special focus on statistical methods. Efficient methods for learning about visual data are described and the ways the nervous system implements these computations are also discussed. Materials of the course from previous years can be accessed here. Background reading for all lectures is listed here.

List of exam topics (updated on 23 May 2023)


Introduction. Computational approach, perception as inference, representation, coding, why probabilities? 
  • Slides  (updated on 18 April 2023)
  • Illusion of the year website
Knowledge representation. Formal systems, logic, probability theory

Probabilistic models. Probability calculus, graphical models, Bayesian inference, approximate inference 
Bayesian behaviour 
Approximate inference, Sampling. MCMC 
Sampling in cognition 


Measuring priors 
  • Slides (updated on 18 April 2023)
Bayesian modelling of vision I. PCA, the Olshausen & Field model,  Modelling correlations of filters, GSM
  • Slides (updated on 19 April 2021)


Bayesian modelling of vision II. Complex models of natural images, hierarchical models 


Neural representation of probabilities. PPC, sampling hypothesis 
  • Slides (updated on 12 May 2021)


Learning. Information theory and learning theory. Maximum likelihood learning, minimum description length principle 
  • Slides (updated on 12 May 2021)
Structure learning. Automatic Occam’s razor, visual chunk learning 
  • Slides (updated on 12 May 2021)
Computer lab, implementation of Bayesian inference problems — ? we’ll see if this will happen

Kódolási gyakorlat

A kódolás python notebookban végezhető el. A kódok részletes instrukciókat tartalmaznak, nem a kódolási készségek fejlesztése (se nem ezek felmérése) a célja, hanem kódolás segítségével kíván bepillantást adni az órán tárgyalt eszközök használatába.
A python notebook a saját gépen is futtatható amennyiben python rendelkezésre áll (open source szoftver), de még egyszerűbb a google colab szolgáltatását használni

A két gyakorlat:


Computational Neuroscience

(in Hungarian, Idegrendszeri modellezés)

Neptun: kv2n9o46
Fall semester, yearly.
Lecturers: Gergő Orbán, Balázs Ujfalussy and Zoltán Somogyvári.
Course material can be found at

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.