Electrical and Computer Engineering 796:
Models of the Neuron
(Winter 2017)


To provide a solid conceptual and quantitative background in the modeling of biological neurons and how they function as computational devices. Practical experience will be gained in modeling neurons from a number of perspectives, including equivalent electrical circuits, nonlinear dynamical systems, and random processes, and an introduction to the mathematics required to understand and implement these different engineering methodologies will be given.


Dr. Ian Bruce,
ITB-A213, ext. 26984.



Neural model simulation software:

Helpful background knowledge and skills:

A basic undergraduate understanding of electrical circuits, linear systems, ordinary and partial differential equations, probability and random processes, and some ability to program (preferably in Matlab).

Course Outline: (subject to change)

Introduction to Biological Neurons and Neural Computation (1 Lecture)

Simple Deterministic Models of Neural Excitation (2 Lectures)

Stochastic Models of Neural Activity (2 Lectures)

Nonlinear Dynamical Models of Neural Excitation (3 Lectures)

Models of Ion Channel Gating (2 Lectures)


Assignments (3 × 20% = 60%); Project (40%).




There will be one 2.5-hour lecture per week at 9:30am–12:00pm on Wednesdays in BSB-117a, starting on January 11.

PDFs of the lecture slides will be posted here before each lecture.

Slides for:

Lecture #1 (Wednesday, January 11); Lecture #2 (Wednesday, January 18) — continuing with slides from previous lecture
Lecture #3 (Wednesday, January 25); Lecture #4 (Wednesday, February 1) — continuing with slides from previous lecture
Lecture #5 (Wednesday, February 8); Wednesday, February 15 — no lecture due to conference attendance
Lecture #6 (Wednesday, February 22) — continuing with slides from previous lecture
Lecture #7 (Wednesday, March 1); Lecture #8 (Wednesday, March 8) — continuing with slides from previous lecture
Wednesday, March 15 and Wednesday, March 22 — no lecture
Lecture #9 (Wednesday, March 29)

Assignment #3:

Rinzel & Ermentrout book chapter: RinzelErmentrout1998.pdf
Useful Matlab file: quiver_scaled.m

Policy Reminders

"The Faculty of Engineering is concerned with ensuring an environment that is free of all adverse discrimination. If there is a problem, that cannot be resolved by discussion among the persons concerned, individuals are reminded they should contact the Departmental Chair, the Sexual Harassment Officer or the Human Rights Consultant, as soon as possible."

"Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: 'Grade of F assigned for academic dishonesty'), and/or suspension or expulsion from the university.

It is your responsibility to understand what constitutes academic dishonesty. For information on the various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3, located at http://www.mcmaster.ca/policy/Students-AcademicStudies/AcademicIntegrity.pdf

The following illustrates only three forms of academic dishonesty:
1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained.
2. Improper collaboration in group work.
3. Copying or using unauthorized aids in tests and examinations."

Last updated Tuesday, March 7, 2017