Statistical Learning in the Nervous System

(in Hungarian, Statisztikai tanulás az idegrendszerben)

 

Spring semester, 2016/17

The course is listed at the following universities:

  • Eötvös University (ELTE), Neptun code: mv2n9044
  • Technical University (BME), PhD programmes, code BMETE47D088 (for technical reasons the couse is listed with the title ‘Magasabb szintű agyműködés modellezése’)
  • Technical University (BME), masters programmes, code BMETE47MC39
  • Pázmány University (PPKE), students can enroll to the class offered by ELTE in their own Neptun system
  • we welcome everyone else with personalized administrative procedures if needed

 

Lecturers: Gergő OrbánMihály Bányai, Balázs Török and Dávid Nagy
Time: Mondays, 4:10 pm – 5:40 pm
Location: ELTE Lágymányosi Campus, Northern Block (Északi Tömb) 0.87 Marx 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.

 

Exam dates:

  • 19 May, 9:00, BME T603 (with BMETE47D088 code only)
  • 30 May, 10:00, ELTE ÉT 2.105
  • 7 June, 10:00, ELTE ÉT 7.59
  • 12 June, 10:00, ELTE ÉT 7.59

 

List of exam topics

Homework results

 

 

Introduction. Computational approach, perception as inference, representation, coding, why probabilities?  – MB, 13 Feb

 

Knowledge representation. Formal systems, logic, probability theory – DN, 20 Feb

 

Probabilistic models. Probability calculus, graphical models, Bayesian inference, approximate inference – DN, 27 Feb

 

Approximate inference I. Iterative estimation, mixture distributions, EM – MB, 6 Mar

 

Bayesian behaviour – BT, 13 Mar

 

Approximate inference II: Sampling. MCMC – BT, 20 Mar

 

Neural representation of probabilities. PPC, sampling hypothesis – GO, 27 Mar

 

Measuring priors – BT, 3 Apr

 

Bayesian modelling of vision I. PCA, the Olshausen & Field model,  Modelling correlations of filters, GSM – GO, 10 Apr

 

Bayesian modelling of vision II. Higher-level vision – GO, 24 Apr

 

Structure learning. Learning theory, automatic Occam’s razor, visual chunk learning – DN, 8 May

 

Decision making and reinforcement learning – MB, 15 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.