Computational Cognitive Neuroscience
Bruce MacLennan [he/his/him]
Office: Min Kao 550
Hours: 2:30–3:30 WF, or make an
Meg Stuart [she/hers/her]
Office: Min Kao 204
Hours: 2:30–4:30 MW, or make an
Classes: 12:20–1:10 MWF in Min Kao 405
This page: http://web.eecs.utk.edu/~mclennan/Classes/494-594-CCN
This course is a survey of computational cognitive neuroscience.
Therefore, on the one hand, it is focused on the neuroscience of
cognitive processes, including perception, categorization, memory,
language, action, and executive control. On the other hand, it makes
use of computer simulations of neural networks to model cognitive
processes and to test hypotheses about their neural implementations.
The course is valuable to computer science students,
especially those interested in artificial intelligence, neural
networks, and neuromorphic computing. There are many things that
brains do better than our AI systems, and this course will help you
to understand how they do it. You will be able to take the concepts
and theories of neural information processing and use them to
develop better AI systems.
The course is valuable for neuroscience students because it
will teach you how computers can be applied to modeling the neural
processes underlying cognition. It will give you hands-on experience
using these tools, which will reinforce your neuroscience knowledge
and give you a deeper understanding of neural information processing
in the brain. It will also show you how these processes can be
implemented on computers in order to achieve artificial
This course is designed primarily for students in either Computer
Science or the Interdisciplinary Program for
Neuroscience. The course is intended to be interdisciplinary
and self-contained, and so there are no specific prerequisites. The
models explored will not require mathematics beyond elementary
calculus. However, the course will be taught at a level appropriate
for seniors and graduate students. If you have any questions about
whether you should take this course, please send me
This is the first time this course has been taught in this form, so
both the assignments and grading are subject to change.
At this time, we expect weekly homework including (1) results of
your simulation experiments, (2) a short paragraph reflecting on the
week’s reading assignment. For graduate (594) students there will
also be a term paper or project; graduate students also will be
expected to give an in-class presentation on a research topic. There
will be occasional pop quizzes.
See the Schedule of Due
Dates. It might be a good idea to bookmark this! We deduct 20%
for each day late, up to two days late, after which there is no
We will be using O’Reilly, R. C., Munakata, Y., Frank, M. J.,
Hazy, T. E., et al., Computational Cognitive Neuroscience
(2014, 2nd ed.). It is an online free text available at
this link (opens in new window).
Students will be expected to download the emergent
software system and install it on their personal computer (Mac,
Windows, or linux) so that they can complete assignments. We will
help you do this during the first week of class. If you are unable
to get emergent installed, please see this page for information on
logging into the EECS remote Windows desktops.
- For Students with Disabilities
Students who have a disability that requires accommodation(s)
should make an appointment with the Office of Disability Services
(974-6087) to discuss their specific needs as well as schedule an
appointment with me during my office hours.
- Name and Pronoun Accommodations
If you use a name and/or pronouns other than what is in the course
roll, please email me
with the name and/or pronouns that you would like me to use and I
will be glad to accommodate this request.
List of Topics
We will spend a week or two on each of these topics, which
correspond to chapters in the textbook.
- Introduction to cognitive modeling [slides: 6/page or 3/page]
- The individual neuron [6/page
- Networks of neurons [6/page
- Learning and adaptive mechanisms [6/page or 3/page]
- Large-scale organization of the brain [6/page or 3/page]
- Perception and attention [6/page
- Motor control and reinforcement learning [6/page or 3/page]
- Learning and memory [6/page
- Language [6/page or 3/page]
- Executive function [6/page
- Student presentations:
Chakma: “Neural Computational Models for One-Shot Learning” [pdf]
Reynolds: “Spaun and the Nengo Neural Simulator” [pdf]
Stuart: “Neural Network Models of Schizophrenia” [pdf]
Weiss: “Mental Illness: Computational models to improve
Return to MacLennan’s
Send mail to Bruce MacLennan / MacLennan@eecs.utk.edu
Last updated: 2017-05-12.