- Interdisciplinary Program in Cognitive Science, Seoul National University
- Instructor: Dr. Joon Shik Kim (E-mail: jskim.ozmagi@gmail.com, Tel: 880-1847, Room: 302-314-1)
- TA: Chung-Yeon Lee (E-mail: cylee@bi.snu.ac.kr, Tel: 880-1847, Room: 302-314-1)
- Classroom: 14-303
- Time: Friday 13:00-16:00
- Objectives
- Mathematical and statistical modeling of the brain
- Study of an information processing system based on statistical physics and machine learning
- Analysis of brain signal data with statistical methods
- Text book
- Unsupervised Learning: Foundations of Neural Computation, edited by Geoffrey Hinton and Terrence J. Sejnowski, The MIT Press, 1999
- Evaluation
- Paper presentation (20%)
- Research presentation I (15%)
- Research presentation II (15%)
- Report of a term project (30%)
- Participation in discussion (20%)
- Announcement
- Presentation will be evaluated by other students
- A term project
- The project consists of performing a cognitive science experiment and writing a final report
- Main reference for a project will be presented at the paper presentation
- References
- The free-energy principle: a rough guide to the brain, K Friston, Trends in cognitive sciences, 13(7):293-301, 2009
- An information-maximization approach to blind separation and blind deconvolution, AJ Bell and TJ Sejnowski, Neural Computation, 7(6):1129-1159, 1995
- Natural gradient works efficiently in learning, S Amari, Neural Computation, 10(2):251-276, 1998
- Bayesian self-organization driven by prior probability distributions, AL Yuille, SM Smirnakis, and Lei Xu, Neural Computation, 7(3):580-595, 1995
- The Helmholtz machine, P Dayan, GE Hinton, RM Neal, and RS Zemel, Neural computation, 7(5):889-904, 1995
- A global minimization algorithm based on a geodesic of a Lagrangian formulation of Newtonian dynamics, JS Kim, JC Kim, J O, and BT Zhang, Neural Processing Letters, 26(2):121-131, 2007
- An evolutionary Monte Carlo algorithm for predicting DNA hybridization, JS Kim, JW Lee, YK Noh, JY Park, DY Lee, KA Yang, YG Chai, JC Kim, and BT Zhang, Biosystems, 91(1):69-75, 2008
- Effects of verbal working memory load on corticocortical connectivity Modeled by path analysis of functional magnetic resonance imaging data, GD Honey, CHY Fu, J Kim, MJ Brammer, TJ Croudace, J Suckling, EM Pich, SCR Williams, and ET Bullmore, Neuroimage, 17(2):573-582, 2002
- Functional connectivity in an fMRI working memory task in high-functioning autism, H Koshino, PA Carpenter, NJ Minshew, VL Cherkassky, TA Keller, and MA Just, Neuroimage, 24(3):810-821, 2005
- Schedule
| Date |
Paper |
Speaker |
Slides |
| 3.4 |
Free-energy principle (Ref. [1]) |
Joon Shik Kim |
down |
| 3.11 |
Blind separation (Ref. [2]) |
Joon Shik Kim |
down |
| 3.18 |
Natural gradient (Ref. [3]) |
Joon Shik Kim |
down |
| 3.25 |
Bayesian self-organization (Ref. [4]) |
Joon Shik Kim |
down |
| 4.1 |
No class, Cognitive neuroscience conference (CNS) in San Francisco |
|
|
| 4.8 |
No class, CNS |
|
|
| 4.15 |
Paper presentation, students |
Joon Shik Kim |
|
| 4.22 |
The Helmholtz machine (Ref. [5]) |
Joon Shik Kim |
down |
| 4.29 |
A global minimization algorithm (Ref. [6]) |
Joon Shik Kim |
down |
| 5.6 |
An evolutionary Monte Carlo algorithm (Ref. [7]) |
Joon Shik Kim |
down |
| 5.13 |
Research presentation I |
Students |
|
| 5.20 |
Verbal working memory via corticocortical connectivity (Ref. [8]) |
Joon Shik Kim |
down |
| 5.27 |
Functional connectivity in an fMRI working memory task (Ref. [9]) |
Joon Shik Kim |
down |
| 6.3 |
Research presentation II |
Students |
|
| 6.10 |
Due of the final report of a term project |
|
|
|