### Statistical Learning in the Nervous System

### (in Hungarian, Statisztikai tanulás az idegrendszerben)

**Spring semester, 2017/18**

**The course is listed at the following universities:
**

**Eötvös University (ELTE)**, Neptun code:**mv2n9044****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, Mihály Bányai, Balázs Török and Dávid Nagy

**Time**: Mondays, 4:15 pm – 5:45 pm

**Location**: ELTE Lágymányosi Campus, Northern Block (Északi Tömb) 0.89 Jedlik Ányos room

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.

**Introduction.** Computational approach, perception as inference, representation, coding, why probabilities? – MB, 12 Feb

**Knowledge representation.** Formal systems, logic, probability theory – DN, 19 Feb

**Probabilistic models.** Probability calculus, graphical models, Bayesian inference, approximate inference – DN, 26 Feb

**Bayesian behaviour** – BT, 5 Mar

**Computer lab**, implementation of Bayesian inference problems – MB, 12 Mar

- Slides
- Jupyter notebooks
- C.M. Bishop: Pattern Recognition and Machine Learning, Chapter 9

**Approximate inference II: Sampling.** MCMC – BT, 19 Mar

**Bayesian modelling of vision I.** PCA, the Olshausen & Field model, Modelling correlations of filters, GSM – GO, 26 Mar

**Measuring priors** – BT, 9 Apr

**Neural representation of probabilities.** PPC, sampling hypothesis – GO, 16 Apr

**Structure learning.** Learning theory, automatic Occam’s razor, visual chunk learning – DN, 23 Apr

**Bayesian modelling of vision II.** Higher-level vision – GO, 7 May

**Decision making and reinforcement learning** – MB, 14 May

### Computational Neuroscience

### (in Hungarian, Idegrendszeri modellezés)

Neptun: kv2n9o46

Fall semester, 2013/14.

Lecturers: Gergő Orbán, Balázs Ujfalussy and Zoltán Somogyvári.

Course material can be found at http://cneuro.rmki.kfki.hu/education/neuromodel

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.