15-883 Computational Models of Neural Systems
Monday / Wednesday 4:30 ‐ 5:50 in Gates 4211
Units: 12.0, Section: A
Professor David S. Touretzky (just "Dave" is fine)
- email: email@example.com; office phone 412-268-7561
- Office location: Gates-Hillman Center, room 9013
- Office hours: drop by any evening, or email for an appointment
This course is an in-depth study of information processing in real
neural systems from a computer science perspective. We will examine
several brain areas, such as the hippocampus and cerebellum, where
processing is sufficiently well understood that it can be discussed in
terms of specific representations and algorithms. We will focus
primarily on computer models of these systems after establishing the
necessary anatomical, physiological, and psychophysical context. There
will be some neuroscience tutorial lectures for those with no prior
background in this area.
The prerequisite for this course is prior familiarity with either
computer science or neuroscience. Computer science students should
have a graduate level understanding of at least one of artificial
intelligence, machine learning, or computer vision. Neuroscience
students should have had at least some prior exposure to computation,
such as an undergraduate programming class.
After taking this course, you will be able to:
- Describe major brain areas, including their anatomy and their hypothesized function.
- Identify the major computational algorithms that have been put forth as models of these brain areas.
- Program simple models in MATLAB.
- There is no textbook for the course.
- Readings are linked from the syllabus and are also listed in the Readings Archive
section of the class web site.
- MATLAB is available in the Andrew clusters on campus, and also
at Pitt. The following links may be useful:
Getting Started with MATLAB, MATLAB function list,
Comprehensive MATLAB Documentation.
- Recommended resources on computational neuroscience:
- P.S. Churchland and T.J. Sejnowski (1992) The Computational Brain. MIT Press.
- P. Dayan and L.F. Abbott (2001) Theoretical Neuroscience. MIT Press.
- T. Trappenberg (2002) Fundamentals of Computational Neuroscience.
Oxford University Press.
- P.S. Churchland (2002) Brain-Wise: Studies in Neural
Philosphy. MIT Press.
- Nature Neuroscience
special issue on computational modeling, November 2000.
There are six assignments in this class, plus a modeling project and
mid-term and final exams. The final course grade will be calculated
using the following categories:
|Assignment #1: HHsim ||2 %|
|Assignment #2: CMAC||3 %|
|Assignment #3: Matrix memory||4 %|
|Assignment #4: Codons||4 %|
|Assignment #5: Learning rules||4 %|
|Assignment #6: Rescorla-Wagner||3 %|
|Modeling Project||20 %|
|Midterm Exam||30 %|
|Final Exam||30 %|
|Total|| 100 %|
The following letter grades will be assigned based on calculations coming
from the course assessment section.
- In Assignment 1 you will experiment with the HHsim Hodgkin-Huxley simulator.
This will help you understanding the mechanisms underlying neuronal spiking.
- In Assignment 2 you will experiment with simulations of the Cerebellar Model
Articulation Controller (CMAC), an example of how function interpolation by table
lookup could be implemented in neural circuitry.
- In Assignment 3 you will work experiment with a simple matrix
memory simulation to see how associative recall and pattern completion
behavior can be obtained from linear threshold units, and how
new patterns can be learned using a simple synaptic modification mechanism.
This type of associative recall is the basis of many models of
- In Assignment 4 you will investigate the statistics of the
codon representation used in Marr's model of hippocampus and
many subsequent models. This assignment uses the mathematical
conventions layed out in the O"Reilly and McLelland paper discussed
in one of the lecture on hippocampus; it gives you the opportunity
to apply the ideas in that paper.
- In Assignment 5 you will experiment with a simulator that allows
you to investigate a variety of synaptic learning rules. Several
key learning algorithms studied in this course, such as associative
learning and competitive learning, could be realized at the cellular
level by synaptic learning mechanisms such as these.
- In Assignment 6 you will experiment with a simulator of
classical conditioning experiments that utilizes the
Rescorla-Wagner learning rule. You will see how the choice of
simulation parameters and the arrangement of the training trials
affects learning behavior.
- For the modeling project, which is 20% of your grade, you will
write MATLAB code to reproduce a model of hippocampal working
memory based on spike timing in entorhinal cortex. This will give
you experience with integrate-and-fire neuron models, which are
more complex than the linear threshold or continuous activation
models used earlier in the course. The project will also give you
an opportunity to write a substantial model in MATLAB from
|Grade ||Percentage Interval|
|A ||90% - 100%|
|B ||80% - 89%|
|C ||70% - 79%|
|D ||65 - 69%|
|R (F) ||below 65%|
- Late-work policy: Assignments are due at 11:59 pm on the
date shown in the class schedule. They can be submitted up to two
days late at a cost of 1 point per day. Assignments more than 2
days late will not be accepted.
- Make-up work policy: Students can make up work if they
miss a deadline due to illness (with a doctor's note).
- Re-grade policy: If you believe your assignment was
graded incorrectly, please contact me. I will be happy to take
- Academic Integrity and Collaboration: The work you
submit in this course must be your own, with the exception of the
Pascaline assignment which is done in pairs. You are welcome to
help or receive help from your fellow students on general matters
such as how to fix a MATLAB error, but you may not share your
MATLAB files with other students, collaborate on writing
code, or in any other way submit or take credit for work that is not
purely your own.
- Class Communication: We will use Piazza as our primary
means of online communication. Please ask questions via Piazza
rather than emailing the instructor or TAs directly, so that your
fellow students can benefit from the discussion. Sometimes a
classmate may be able to answer your question more quickly than the
- Accomodations for Students with Disabilities: If you have
a disability and have an accommodations letter from the Disability
Resources office, I encourage you to discuss your accommodations and
needs with me as early in the semester as possible. I will work with
you to ensure that accommodations are provided as appropriate. If you
suspect that you may have a disability and would benefit from
accommodations but are not yet registered with the Office of
Disability Resources, I encourage you to contact them at
- Statement of Support for Students' Health and Well-Being:
Take care of yourself. Do your best to maintain a healthy lifestyle
this semester by eating well, exercising, avoiding drugs and alcohol,
getting enough sleep, and taking some time to relax. This will help
you achieve your goals and cope with stress.
All of us benefit from support during times of struggle. There are
many helpful resources available on campus and an important part of
the college experience is learning how to ask for help. Asking for
support sooner rather than later is almost always helpful.
If you or anyone you know experiences any academic stress, difficult
life events, or feelings of anxiety or depression, we strongly
encourage you to seek support. Counseling and Psychological Services
(CaPS) is here to help: call 412-268-2922 and visit their website at
reaching out to a friend, faculty or family member you trust for help
getting connected to the support that can help.
Please see the course schedule page for a
list of lectures, assignment issue dates, and assignment due dates.
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