SYDE 556/750 - Simulating Neurobiological Systems

SYDE 556/750 - Simulating Neurobiological Systems

Course information for SYDE 556/750, taught Fall 2026.

SYDE 556: Simulating Neurobiological Systems

  • Instructor
    Chris Eliasmith
    Office: E7-6324
    Email: celiasmith@uwaterloo.ca
    Website: http://compneuro.uwaterloo.ca

  • Teaching Assistant
    Graeme Damberger
    Office: EC4-2011 Email: graeme.damberger@uwaterloo.ca

  • Course times and location
    Tuesday: 12:00-1:50 in E5-2004 (SYDE 556/750)
    Thursday: 12:00-12:50 in E5-2004 (SYDE 556/750)
    Thursday: 1:00-1:50 in E5-2004 (SYDE 750 optional 556)

  • Office hours
    Please make an appointment by email.


Syllabus

The course syllabus is here.

Lecture Notes and Assignments

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.

Readings

  • Main resource: “Neural Engineering: Representation, Computation and Dynamics in Neurobiological Systems”, Chris Eliasmith and Charles Anderson, 2003. MIT Press. Free.
  • Optional: “How to Build a Brain”, Chris Eliasmith, 2012. Oxford University Press.

Grading

Grading will be based on writing six tests, keeping the top 5 scores. This comprises 100% of the grade for 556, and 80% for 750. For 750, the remaining 20% is determined by the final project. Late projects lose 1 mark per day and may be at most seven days late. Practice notebooks are provided on which the tests will be based. You are expected to do these notebooks individually to prepare for the tests.


Project Ideas

The final project for the course consists of picking a neurobiological system and building a model for it. Projects are mandatory for 750. 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 29th. To do so, you will need to submit a short summary of your project earlier. Have a look at this document for more information

Project Format

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 near the end of term. Marks are not assigned for the presentation, although a skipped or very poor presentation will result in the loss of up to 4 marks on the project report. The schedule will be set later in the term. Contents can follow the recommendations in the project summary document.


Course Format

Two lectures per week and practice notebooks 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.


Course Prerequisites

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.


Useful Links

Programming

  • NumPy for MATLAB users (a good quick reference for common matrix operations, comparing both Python and Matlab)

Computational Neuroscience

Neuroscience

More information