Instructor: Terry Stewart (firstname.lastname@example.org)
Office: PAS 2463
Office Hours: Monday 2-3 and Thursday 3-4, or by appointment
Location: E5 6127
Times: Mon 12:00pm-1:20pm & Thurs. 12:30p-1:50p (plus 2:00p-2:50p Thurs for SYDE 750)
The in-class lecture notes will be posted here before each class.
Week 5, 6: (January 29, Feb 1, 5, 8) Dynamics
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: (due January 22nd) Assignment 1
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 at http://nbviewer.jupyter.org/github/tcstewar/syde556-1/blob/master/Final%20Projects.ipynb, 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. You will need to submit a short (3-paragraph) summary of your project by March 29th.
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.