See the course outline for more information.
Office: E7-6457 Email: email@example.com
Office: E7-6339 Email: firstname.lastname@example.org
Course times and location
Monday: 10:30-12:20 in E7-4433 (SYDE 556/750)
Wednesday: 10:30-11:20 in E7-4433 (SYDE 556/750)
Wednesday: 11:30-12:20 in E7-4433 (SYDE 750, optional for 556)
Please make an appointment by email.
All lecture notes and assignments are available on github. The lecture notes will be posted there before each class. While everything is available earlier on github, all material is subject to change until it is explicitly linked from the github README.
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 Oct 28th. To do so, you will need to submit a short summary of your project by Oct 23rd. Have a look at this document for more information
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.
Students are expected to provide a short, 5-10 min project presentation on the last two days of class. The schedule will be set later in the term. Contents can follow the recommendations in the project summary document.
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. 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 is highly recommended. Familiarity with Fourier Transforms and other signal processing concepts is recommended. Familiarity with calculus and linear algebra is required.