Instructor: Chris Eliasmith (email)
Office: HH 331
Office Hours: Monday 2-3 and Thursday 3-4, or by appointment
Location: E5 6127
Times: Mon & Thurs. 12:30p-1:50p (plus 2:00p-2:50p Thurs for 750)
If you would like more personalized help for the assignments in this course, two of previous PhD students in the lab (Xuan Choo and Travis DeWolf) offer tutoring services. They can be contacted at email@example.com and they charge \$20 per half hour (or \$15 per person per half hour for groups).
The in-class lecture notes will be posted here before each class.
The four assignments will be posted here.
<lastname>.Assignment<number>.pdf. This document will have all the graphs and any written answers to questions. (From a jupyter notebook, you can do
jupyter nbconvert --to pdf my_name.assignment1.ipynb)
Assignments are due at Midnight. The late penalty is one mark per day it is late.
Assignment 1: HTML
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 and more info here, 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 beginning of Reading Week. You will need to present your project idea in a couple of overhead slides to the class at the end of March. These presentations are pass/fail. If you do not present you will lose 10 marks off your project report.
The project report should be in the format discussed in chapter 1 of the book (see pp. 19-23; i.e., System Description, Design Specification, Implementation). Students will also be expected to do a short (5-10min) presentation on your topic in the last week or so. That presentation should consist of a few slides (max 4), that sets up the problem and describes your expected approach. As with assignments, all code for projects must be submitted as well.
Two lectures per week and homework assignments consisting of computer exercises using Python (or Matlab). 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, Matlab 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.