See the course outline for more information.
Office: E7-6342 (office hours in E7-6323)
Course times and location
The in-class lecture notes will be posted here before each class.
The four assignments will be posted here.
The final project for the course consists of picking a neurobiological system and building a model for it. There is a list of possible projects, expectations for the project and more info at this link, but is not intended to be comprehensive, so feel free to come up with your own ideas. Please have your projects approved by me by the end of Reading Week. To do so, you will need to submit a short summary of your project by Feb 24th.
It is suggested that the project report is in the format discussed in chapter 1 of the book (see pp. 19-23; i.e., System Description, Design Specification, Implementation), see the project page for details.
The final document should be between, at least ten, and (at the very most) twenty content pages at 12pt, 1.25 line spacing. Have a look at the following project template for more information.
Update: Instead of a presentation, students are expected to provide a short, one-page "intermediate" project report by Apr 2, 2020. While this intermediate report is not marked, not submitting a report by the deadline will result in a -10 mark penalty (25% of the final project). Have a look at this document for more information.
Two lectures per week and homework assignments consisting of computer exercises using Python. For SYDE 750 a larger class project is required, usually a computer simulation developed based on significant neuroscientific research and/or collaboration with a neurophysiologist. For SYDE 556 a class project based on an in class/text example is required. This course examines a general framework for modeling computation by neurobiological systems with an emphasis on quantitative formulations. Particular emphasis will be placed on understanding computation, representation, and dynamics in such systems. Students will learn how the fundamentals of signal processing, control theory and statistical inference, can be applied to modeling sensory, motor, and cognitive systems.
Knowing how to program with matrices using Python or some other language is highly recommended. Familiarity with Fourier Transforms and other signal processing concepts is recommended. Familiarity with calculus and linear algebra is required.