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 OrbánMihály 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.

List of exam topics, homework scores

Exam dates:

  • 18 May, 10 AM, BME building R, room 501
  • 6 June. 9 AM, ELTE Southern Block, room 0-220 Kárteszi Ferenc
  • 19 June. 10 AM, ELTE Southern Block, room 0-220 Kárteszi Ferenc

 

Homeworks: If you missed homework deadlines or would like to make second attempts, three extra homeworks may be sent in by the end of semester (deadline is the day before the first exam, 18 May).

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

 

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

 

Decision making and reinforcement learning – MB, 7 May

 

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