Statistical Learning in the Nervous System

(in Hungarian, Statisztikai tanulás az idegrendszerben)

Spring semester, 2019/20

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
Time: Mondays, 4:15 pm – 5:45 pm
Location: ELTE TTK, Északi Tömb, 0.89 (földszint), Jedlik terem

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, homework scores

 Date of first class / első óra: 17 February

Teaser talk: Battle Royale: Deep Learning vs Az Agy MAFIHE téli iskola (http://tisk.mafihe.hu/2020-biofizika-teli-iskola), Február 7, péntek, 11:10-12:40

Exam dates: TBA

Introduction. Computational approach, perception as inference, representation, coding, why probabilities?  -17 Feb
  • Slides  (updated on 20 Feb 2020 with up-to-date material)
  • Illusion of the year website
Knowledge representation. Formal systems, logic, probability theory – 24 Feb
(2 March — NO LECTURE, SORRY)
Probabilistic models. Probability calculus, graphical models, Bayesian inference, approximate inference – 9 Mar
Bayesian behaviour – 16 Mar
Approximate inference, Sampling. MCMC – 23 Mar
Computer lab, implementation of Bayesian inference problems — omitted this year
Bayesian modelling of vision I. PCA, the Olshausen & Field model,  Modelling correlations of filters, GSM – 30 Mar

Reading:

Bayesian modelling of vision II. Complex models of natural images, hierarchical models – 6 Apr

Reading:

Neural representation of probabilities. PPC, sampling hypothesis – 20 Apr

Reading:

Representing and measuring mental priors – 27 Apr

Readling:

Structure learning. Learning theory, automatic Occam’s razor, visual chunk learning – 4 May
Decision making and reinforcement learning – 11 May

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 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.