Computational Modeling of Intelligence (Spring 2011)
  • Interdisciplinary Program in Cognitive Science, Seoul National University
  • Instructor: Dr. Joon Shik Kim (E-mail:, Tel: 880-1847, Room: 302-314-1)
  • TA: Chung-Yeon Lee (E-mail:, 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
    1. The free-energy principle: a rough guide to the brain, K Friston, Trends in cognitive sciences, 13(7):293-301, 2009
    2. An information-maximization approach to blind separation and blind deconvolution, AJ Bell and TJ Sejnowski, Neural Computation, 7(6):1129-1159, 1995
    3. Natural gradient works efficiently in learning, S Amari, Neural Computation, 10(2):251-276, 1998
    4. Bayesian self-organization driven by prior probability distributions, AL Yuille, SM Smirnakis, and Lei Xu, Neural Computation, 7(3):580-595, 1995
    5. The Helmholtz machine, P Dayan, GE Hinton, RM Neal, and RS Zemel, Neural computation, 7(5):889-904, 1995
    6. 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
    7. 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
    8. 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
    9. 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

Last updated: June 2011 by Chung-Yeon Lee