Statistical Learning in the Nervous System

(in Hungarian, Statisztikai tanulás az idegrendszerben)

Tavaszi félév, 2021/22

Kurzus információk

Kurzus időpont: hétfőnként 16:15

Első alkalom: 2021. február 15.

Kurzus platform: google meet

Link: meet.google.com/uws-etha-tzf 

Házi feladatok: a feladatokat kérjük erre a címre elküldeni: assignmentscsnl@gmail.com

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, Dávid G. Nagy
Time: Mondays, 4:15 pm – 5:45 pm
Location: online, see above

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

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:

A korábbi házi feladatok egy PDF-be összeszedve (Május 4-i frissítés)

Introduction. Computational approach, perception as inference, representation, coding, why probabilities?  -15 Feb
  • Slides  (updated on 17 Feb 2021 with up-to-date material)
  • Illusion of the year website
Knowledge representation. Formal systems, logic, probability theory – 22 Feb
Probabilistic models. Probability calculus – 1 Mar
Probabilistic models 2. Graphical models, Bayesian inference, approximate inference – 8 Mar
  • Slides (last year’s slides)
Bayesian behaviour – 22 Mar
Approximate inference, Sampling. MCMC – 29 Mar
Computer lab, implementation of Bayesian inference problems — ? we’ll see if this will happen
Sampling in cognition – 12 Apr
  • Slides (last year’s slides)
Representing and measuring mental priors – 19 Apr
  • Slides (last year’s slides)

Readling:

Bayesian modelling of vision I. PCA, the Olshausen & Field model,  Modelling correlations of filters, GSM – 26 Apr
  • Slides (last year’s slides)

Reading:

Bayesian modelling of vision II. Complex models of natural images, hierarchical models – 3 May

Reading:

Neural representation of probabilities. PPC, sampling hypothesis – 10 May
  • Slides (last year’s slides)

Reading:

Structure learning. Learning theory, automatic Occam’s razor, visual chunk learning – 17 May – is it beyond the term time?
  • Slides (last year’s slides)
Decision making and reinforcement learning – (skipped this year)

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.