15-883 Computational Models of Neural Systems

Monday / Wednesday 4:30 ‐ 5:50 in Gates 4211

Fall 2017

Units: 12.0, Section: A


Professor David S. Touretzky (just "Dave" is fine)
  • email: dst@cs.cmu.edu; office phone 412-268-7561
  • Office location: Gates-Hillman Center, room 9013
  • Office hours: drop by any evening, or email for an appointment

Course Description

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.

Learning Objectives

After taking this course, you will be able to:
  1. Describe major brain areas, including their anatomy and their hypothesized function.

  2. Identify the major computational algorithms that have been put forth as models of these brain areas.

  3. Program simple models in MATLAB.

Learning Resources

  • 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: CMAC3 %
Assignment #3: Matrix memory4 %
Assignment #4: Codons4 %
Assignment #5: Learning rules4 %
Assignment #6: Rescorla-Wagner3 %
Modeling Project20 %
Midterm Exam30 %
Final Exam30 %
Total  100 %

  • 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 human memory.

  • 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 scratch.

The following letter grades will be assigned based on calculations coming from the course assessment section.
Grade   Percentage Interval
A 90% - 100%
B 80% - 89%
C 70% - 79%
D 65 - 69%
R (F) below 65%

Grading Policies

  • 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 another look.

Course Policies

  • 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 instructor.

  • 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 access@andrew.cmu.edu.

  • 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 http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

Course Schedule

Please see the course schedule page for a list of lectures, assignment issue dates, and assignment due dates.

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