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Computational Cognitive Neuroscience (Psych 4175/5175), Spring 2019

  • Lab Sections: Wed MUEN E311 12-01:50, Wed MUEN E311 2-3:50
  • TA: Hilarie Nickerson, hnickerson@colorado.edu, Office Hours in Gold A152 by appointment.


How does the brain secrete the mind? This course will introduce you to the ideas and methods in computational cognitive neuroscience that have been applied to answering this question. Specifically, we focus on simulating cognitive and perceptual processes using neural network models. These models provide a bridge between behavioral and biological levels of analysis. We start by understanding the basic computational and biological properties of individual neurons and networks of neurons, which give rise to basic processing mechanisms like spreading activation, inhibition, and multiple constraint satisfaction. We then discuss learning mechanisms (self-organizing and error-driven), which all networks of neurons require to perform any reasonably complex task. We will examine a range of cognitive phenomena within this framework, including attention, memory, language, and higher-level cognition. The overarching goal is to use simulations to help us to understand how our neurons give rise to our thoughts.


Due to the scope of topics covered and diversity of student backgrounds, we do not have formal prerequisites for this course. If you are a Psych major, typically you will have had the relevant PSYC 1001 (intro), 2145 (cognitive), 2012 (bio), and 3101 (stats), which provide basic background in cognitive psychology and neurobiology that will be useful for the course. Neuroscience majors also will have had relevant coursework and should be in fine shape. Computer science / math / etc students may not have had as much Psych and Neuro background, but we make every attempt to keep everyone on the same page, and the textbook does not assume significant background knowledge. Students who have a sincere interest and/or additional background in cognitive psychology, neuroscience, and/or computers (or their relationships) will find this course more engaging. While the models we will be using are mathematically based, only algebra and some simple calculus-level concepts are discussed (and no actual math is required from undergraduates -- grad students are expected to solve a few simple algebra equations). The focus will be on intuitive and practical applications, not on theoretical derivations. Computer programming experience is not required, because the models are accessible via a graphical interface, but it is beneficial, especially for more ambitious final projects.


There is a weekly two-hour lab session that is supervised by the teaching assistant, where students obtain in-depth hands-on experience with the computer simulation explorations. These explorations are the centerpiece of the course, and provide a unique exploratory learning opportunity. You will perform many what-if scenarios to understand what aspects of the brain's biology are important for producing specific cognitive phenomena. You will simulate the effects of brain damage in these models, to understand neuropsychology (the study of brain-damaged patients). The computer models enable complete control and dynamic, colorful visualization of these explorations, providing a unique ability to understand how cognition emerges from the brain. You will document these explorations by answering the simulation exercises questions (to be worked on during the lab sessions). You should be able to do most, hopefully all, of the required homework during these lab sessions.

Lab does NOT meet the first week of class -- starts up the 2nd week when there are actual homeworks to be done.

Tutorials for developing your own network in Emergent:


Your grade will be based on three components in the following proportions:

  • Simulation exercises: 50%
  • Reading reactions: 10%
  • Final project: 30%
  • Class participation: 10%

Simulation Exercises

The simulation exercises are linked in several places: where they fit within the flow of the chapter, at the end of each chapter, and in this master list: http://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Sims/All . You should answer all of the exercise questions for each chapter, and submit to the class Canvas site on or before the date shown in the schedule below. Although you will be working on these exercises in the labs, you must write them up individually. We want to see that each person individually understands the material, so this should be evident in your writeup. It is best to write down results and first drafts of answers as you work through the exercises -- they can take a while to run and you don't want to have to run them repeatedly. Exercises turned in late will be penalized 5% for each day after the due date.

Important: -- in the Neuron exercise, the questions marked advanced are only required for graduate students -- undergrads are welcome to do them if you want, but they are not required. These are questions 2.4 and 2.6.

Reading reactions

For each chapter in the online Computational Cognitive Neuroscience Textbook, you write up a few sentences about the topic you found most interesting in the chapter and why, and in particular any interesting questions that occurred to you that might be useful to discuss during class (for shy students, this can also be a great way to ask your questions and can really help with your participation grade if you otherwise don't feel comfortable participating in discussions during class). These reading reactions are designed to ensure that you are keeping up on the reading and to inform us about your interests. Reading reactions should be submitted to the Canvas system for this course, prior to the class meeting when they are due (i.e., by 12pm noon).

Final Project

The final project is an opportunity for you to use simulations to examine some psychological phenomenon of interest to you. This project will require careful preparation and thought, so I strongly recommend that you begin your work early. Do not be overly ambitious -- relatively clear and simple but thoughtful work is preferred to a complicated half-baked mess. Do not be misled by the relative simplicity of running the canned exercises in the book -- simulation projects take a long time to complete! The following timeline is designed to ensure that you make progress on your project (5 of the 30 points for the project will come from simply making each of the 5 deadlines before the final due date) and that you receive feedback on it before turning in the final version. See the schedule section below for the exact dates of each of the events, indicated by the Deadline keyword.

Deadline Assignment
Today! Project topic version 1 -- just a quick idea to get you thinking about what you might want to do
Top 2 Project topic version 2 -- a bit more thought now that you've had more experience with the simulations
Prop Project proposal: 1 page summary of your question of interest and proposed approach to explore this question through simulations
Meet Meeting w/instructor about project -- happens in regular lab meeting times
Student Pres Presentation of project to class -- just 5 minutes and a few slides to convey the key idea, model, and results
Paper Final paper (date pending grades schedule from University)

In your presentation to the class, you are expected to have substantive, if not final, results to discuss. Presentations should clearly motivate the psychological issue or phenomenon and your approach to it, in addition to a summary of the methods and results.

A final paper describing your project is due on the last day of classes. This paper should be around 5 pages single-spaced (precise page count not important -- just a rough guide -- important thing is addressing all the key elements), and should be in a basic APA-like format, with a concise introduction to the psychological / neural / computational issue or phenomenon and a justification of your general approach to modeling it, methods (how you constructed or modified your model, what manipulations you performed and why), results (what you found in terms of the relevant measures of model function, including averages over multiple runs of the model as they can be somewhat random on any given run), and a concluding discussion (about the significance of your results, what you might do to improve your model, etc.). You do not need to include an abstract. Network diagrams and graphs of significant results should be included. However, do not include excessive or redundant figures; the text should provide a clear interpretation and justification of all figures. NOTE: For each day that the final paper is late, 5% will be deducted from your final paper grade.

Project Topic and Approach Guidelines

Here is some more info about selecting an appropriate project topic and guidelines for how to get started on doing the project.

  • The main point of the project is to build a connection between some specific cognitive neuroscience (or other) data, and a computational model thereof -- the goal is for you to learn first-hand how the models help you better understand some specific phenomenon of interest. Thus, you will need to find one or more scientific papers with relevant data to model -- this is a great place to start in thinking about your project topic. Just start typing some terms of interest into http://scholar.google.com and see if you can hit upon a paper with some interesting findings you'd like to model.
  • Undergraduates can often start out by just doing some basic manipulations to an existing simulation model from the textbook projects -- for example if you're interested in amnesia, you can damage the hippocampus model in various ways. Drug effects can often be simulated by manipulating various parameters in an existing model, and observing the effects on overall behavior. Again, the key is to make a connection between some specific data and the model -- it is not critical to demonstrate your computer programming skills. However, if you are interested in a topic that is not well covered by the textbook projects, by all means go big and build a new model from scratch -- you will have plenty of help in the lab to do this.
  • Grad students are typically expected to produce a more sophisticated model, often of the empirical phenomena you are actually studying in your research.
  • For the project proposal and topic emails, please specify a domain as specifically as possible, and do try to use google scholar to find some relevant articles so you can get a clearer idea. If you have any idea about how to actually implement this in a model, please specify. It does not need to be very long -- just include as much specificity as you can.

Class Participation

Productive participation in class discussion is encouraged to help you get the most out of this course. You are expected to read the text chapters the week they are assigned and to come to class prepared to actively participate in discussion. You can also communicate about any course-related topics as a group by emailing cogsim@grey.colorado.edu.

Grads & Undergrads: This course is designed for advanced undergraduates and graduate students. Undergrads need not feel intimidated by the presence of graduate students in the class. More will be expected of the grads than the undergrads, especially when it comes to the final projects. Also, undergrads will be responsible for fewer of the homework questions.

Grading Policy

Grades are not curved; they are based on percentages (note: Canvas truncates points, so a 92.9 is still an A- for example):

 97-100 A+    87-89 B+   77-79 C+   67-69 D+
 93-96  A     83-86 B    73-76 C    63-66 D
 90-92  A-    80-82 B-   70-72 C-   60-62 D-

Note that the University does not give out A+ grades, but I record these internally, e.g., for use in a recommendation letter.

Simulation Pragmatics

See http://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Sims/All for the full listing of the simulation projects and associated homework questions, and instructions for getting everything running.

IMPORTANT: When you log out of the lab computers, any files you have will be LOST! -- email or USB-Key anything you want to keep!


Wk Date Tuesday Ch Due Date Thursday Ch Due Due Fri
1 15 Jan Introduction 1 Top1 17 Jan CCN Phenomena RR1
2 22 Jan Neurons 2 RR2 24 Jan Neurons 2
3 29 Jan Networks 3 HW2 31 Jan Networks 3 RR3
4 5 Feb Learning 4 HW3 7 Feb Learning 4 RR4
5 12 Feb Learning 4 14 Feb Brain Areas 5 HW4
6 19 Feb Perception 6 RR5 21 Feb Perception 6 RR6
7 26 Feb Perception 6 28 Feb Motor 7 HW6
8 5 Mar Motor 7 RR7 7 Mar Motor 7 Top2
9 12 Mar Memory 8 HW7 14 Mar Memory 8 RR8, Prop
10 19 Mar Memory 8 21 Mar Language 9 HW8
11 26 Mar spring break 28 Mar spring break
12 2 Apr Language 9 RR9, Meet 4 Apr Language 9
13 9 Apr Language 9 11 Apr Executive Function 10 HW9
14 16 Apr Executive Function 10 RR10 18 Apr Executive Function 10 HW10
15 23 Apr Student Presentations 25 Apr Student Presentations
16 30 Apr Student Presentations 2 May Grand Finale Paper*

Ch = Chapter in text to read, Due = Materials due in class, with number representing chapter that is due (can be different than Ch column) (HW# = homework for chapter #, RR# = reading reaction for chapter #), Top = Paper topic, Prop = Final project proposal, Meet = Meet with instructor this week to discuss proposals. Paper = Final papers due, exact date is still TBD (they don't let us know when grades are due until later!)

Lecture Slides for Download

The following slides are available for printing prior to lecture, to make your note-taking more efficient -- just annotate and underline and jot down key points or whatever, instead of slavishly transcribing and thereby likely missing all the key points!

Printout format = 6 pages per printed page (better for printing).

Chapter Printout Format Powerpoint File
1 pdf icon.png oreilly_ccn_intro.pdf (PDF) File:oreilly ccn intro.pptx
2 pdf icon.png oreilly_ccn_neuron.pdf (PDF) File:oreilly ccn neuron.pptx
3 pdf icon.png oreilly_ccn_networks.pdf (PDF) File:oreilly ccn networks.pptx
4 pdf icon.png oreilly_ccn_learning.pdf (PDF) File:oreilly ccn learning.pptx
5 pdf icon.png herd_ccn_brainareas.pdf (PDF) File:herd ccn brainareas.pptx
5 pdf icon.png oreilly_ccn_brainareas.pdf (PDF) File:oreilly ccn brainareas.pptx
6 pdf icon.png oreilly_ccn_percept.pdf (PDF) File:oreilly ccn percept.pptx
7 pdf icon.png oreilly_ccn_motor.pdf (PDF) File:oreilly ccn motor.pptx
8 pdf icon.png oreilly_ccn_memory.pdf (PDF) File:oreilly ccn memory.pptx
8 pdf icon.png Ch8ExSlides.pdf (PDF) Supplementary slides for Ch. 8
9 pdf icon.png oreilly_ccn_language.pdf (PDF) File:oreilly ccn language.pptx
10 pdf icon.png oreilly_ccn_executive_jessica.pdf (PDF) File:oreilly ccn executive jessica.pptx

University Policies

Therapy, Counseling, etc

There are several important resources on campus -- strongly recommend reaching out if you or someone you know is suffering from a psychological disorder or other such condition:

Academic Assistance, etc

These are resources for those who might be falling behind in their classes and otherwise experiencing academic difficulties:


If you qualify for accommodations because of a disability, please submit to me a letter from Disability Services in a timely manner so that your needs can be addressed. Disability Services determines accommodations based on documented disabilities. Contact: 303-492-8671, Center for Community N200, and http://www.colorado.edu/disabilityservices.

If you have a temporary medical condition or injury, see guidelines at http://www.colorado.edu/disabilityservices/students/temporary-medical-conditions

Disability Services' letters for students with disabilities indicate legally mandated reasonable accommodations. The syllabus statements and answers to Frequently Asked Questions can be found at http://www.colorado.edu/disabilityservices

Religious Observances

Campus policy regarding religious observances requires that faculty make every effort to deal reasonably and fairly with all students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance. Please notify me in advance, preferably at the start of the semester, of any cases where this will affect you in this class, and appropriate accommodations will be made. See full details at http://www.colorado.edu/policies/observance-religious-holidays-and-absences-classes-andor-exams

A comprehensive calendar of the religious holidays most commonly observed by CU-Boulder students is at http://www.interfaithcalendar.org/

Classroom Behavior

Students and faculty each have responsibility for maintaining an appropriate learning environment. Those who fail to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with differences of race, color, culture, religion, creed, politics, veteran's status, sexual orientation, gender, gender identity and gender expression, age, disability, and nationalities. Class rosters are provided to the instructor with the student's legal name. I will gladly honor your request to address you by an alternate name or gender pronoun. Please advise me of this preference early in the semester so that I may make appropriate changes to my records. See policies at http://www.colorado.edu/policies/student-classroom-and-course-related-behavior and at http://www.colorado.edu/osc/

Discrimination and Harassment

The University of Colorado at Boulder Discrimination and Harassment Policy and Procedures, the University of Colorado Sexual Harassment Policy and Procedures, and the University of Colorado Conflict of Interest in Cases of Amorous Relationships policy apply to all students, staff, and faculty. Any student, staff, or faculty member who believes s/he has been the subject of sexual harassment or discrimination or harassment based upon race, color, national origin, sex, age, disability, creed, religion, sexual orientation, or veteran status should contact the Office of Discrimination and Harassment (ODH) at 303-492-2127 or the Office of Student Conduct (OSC) at 303-492-5550. Information about the ODH, the above referenced policies, and the campus resources available to assist individuals regarding discrimination or harassment can be obtained at http://www.colorado.edu/odh

Honor Code

All students of the University of Colorado at Boulder are responsible for knowing and adhering to the academic integrity policy of this institution. Violations of this policy may include: cheating, plagiarism, aid of academic dishonesty, fabrication, lying, bribery, and threatening behavior. All incidents of academic misconduct shall be reported to the Honor Code Council (honor@colorado.edu; 303-735-2273). Students who are found to be in violation of the academic integrity policy will be subject to both academic sanctions from the faculty member and non-academic sanctions (including but not limited to university probation, suspension, or expulsion). Other information on the Honor Code can be found at http://www.colorado.edu/policies/student-honor-code-policy