- Professor Sue Becker, becker (at mcmaster dot ca)
- Office hours: by appointment.
TA and co-instructor
- Kiret Dhindsa, dhindsj (at mcmaster dot ca)
- Thursdays in PC-311, Sept 15 - Dec 15 (no class Sept 29, Nov 3)
This course will cover some of the most influential computational models
of learning and memory, and the application of these models to
understanding how the brain learns and encodes information, the analysis
of neuroscientific data and brain-computer interfaces. In the first 2
hours of each class, lectures will draw on classic papers in the
literature, while in hour 3 students will present and discuss papers
describing applications of the models.
Students must have some computer programming experience (in any
programming language), and be comfortable writing programs that include
loops, variables and procedures/functions.
If you've never programmed in Matlab, it would be useful to
read the Matlab mini-tutorial in section 1.5 of Jay McClelland's book
"Explorations in Parallel Distributed
Processing: A Handbook of Models, Programs, and Exercises"
(a draft of the
2nd edition is available online at this link).
|3 programming assignments (10% each) ||30%
|One 20-30 minute oral presentation ||20%
|One final project ||40%
- The 3 assignments will be programmed in Matlab, and will be
due approximately every 2nd week in the earlier part of the course. Each
assignment will involve simulating one of the models discussed in class and
writing up your results in the format of a brief scientific report (intro,
methods, results, discussion).
Assignments must be turned in by 3:30pm on
due dates posted (to be announced). 20% of the total possible mark per
day will be deducted for late assignments.
- The presention of a research article will involve summarizing the key
points of the article, suggesting some questions for discussion and leading
the class discussion of the paper.
- Participation marks will be earned for contributing to the class
discussions of the 11 research articles. One mark for each paper in which a
non-trivial contribution is made to the discussion, up to a maximum of 10
- Final Project
- The project will involve the application of one of the learning models
discussed in the
course to the classification of EEG data for a brain-controlled
- Due date: December 22, 4pm in hard-copy to Sue Becker's mailbox