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Modeling and Identifying Neurosystems (AS.080.323)

Instructor: Sean G. Carver, Ph.D., Psychological and Brain Sciences Department, The Johns Hopkins University.

Semester Offered: Spring 2009.

Class: Tuesday & Thursday 4:30-6:00 pm, Krieger 309.

Lab: Tuesday & Thursday 6:00-7:30 pm, Krieger 309 (optional, but see below).

Spring Break: March 17 & 19; no class or lab.

Class website: http://www.seancarver.org

One Hundred Word Description: Students in this course will be trained to perform original research in computational neuroscience. The course will cover mathematical modeling of neurons, which is useful for understanding the computations of single cells. The student's research will test software, adapted by the instructor from methods of other disciplines, for systematically creating models of neurons using experimental data. For the tests, data will come from another known model, rather than from a biological neuron. To perform the research, students will be given a thorough understanding of the biophysical mechanisms of neurons and of the basic paradigms of neural modeling and system identification.

Background: Neural modeling is often pursued in an ad hoc way. Researchers add the mechanisms they know about, but need to wave their hands about the ones they don't. They necessarily make many simplifying assumptions but often include many details that are not needed to parsimoniously capture the phenomena. More...

Tentative Syllabus: (click here)

Class Materials: (click here)

Prerequisites: ( AS.110.106 AND AS.110.107 ) AND ( AS.080.306 OR AS.080.304) or permission of instructor.

Office hours: During lab, or by appointment. To arrange an appointment, please see me during lab or send an email to seancarverphd@gmail.com. Fridays are the best days for appointments. Once work on projects begins, I expect to talk to everyone about their progress at least once a week. These meetings can be during the lab or in my office.

Expectations: Class attendance; homework for each class; consistent progress on final project; weekly updates on project progress; effective communication of results.

Final Grade: Based 50% on homework, 50% on final project; no exams planned.

Lab attendance: Lab allows students to complete homework assignments under my supervision. I expect that most students will find that they can complete their assignments more quickly and easily in lab. Thus, though attendance is optional it is highly recommended.

Classroom and lab rules: Students are expected to read, understand, and abide by the computer lab rules posted at http://www.jhu.edu/classrooms/policies.html. Most notably, no food or drink is allowed in the lab. Please treat the time between 4:30-6:00 the way you would any other class: only leave in an emergency, keep phones on vibrate and do not take calls, etc., even during the brief times when we are working individually on short exercises. During lab (6:00-7:30) come and go as you wish, but please respect the fact that the lab is for hard work on our class (homework and project) and for nothing else. I encourage students to talk with and work with other students on assignments and projects during lab (but see my academic integrity policy, below).

Lecture etiquette: If I say something you do not understand, stop me and ask me to explain it again. Some of the lectures will be challenging and I insist on taking the time necessary to make sure everyone understands the material.

Homework: Exercises will be given each day of class, and designed to be completed during the lab period. Final projects require time outside of lab. Most homework will be completed on the computer, and quickly assembled into a PowerPoint presentation that should convey to classmates the work you have done. Homework is turned in by email (seancarverphd@gmail.com). Homework is due at midnight before the next class (Monday night or Wednesday night). The penalty for a late assignment is 30%, with a grace period extending until I check my email early the next morning. Before class I will assemble the most interesting slides into a presentation for discussion at the beginning of class.

Homework grading: 10 points is full credit. One additional bonus point will be awarded for doing something creative but not part of the assignment. Bonus work can be handed in separately, but before the homework deadline. Students are encouraged to work on bonus work outside of lab or inside of lab, as time permits. Bonus work can be, but need not be, time consuming and can relate to the student’s final project. To earn the bonus point, you must successfully communicate what you have done through the material you turn in.

Textbook: Neurons in Action: Tutorials and Simulations Using NEURON, Version 2. By John W. Moore and Ann E. Stuart. Sinauer Associates, Inc, Sunderland Massachusetts.

Academic integrity: You are encouraged to work together on projects, but you must disclose who did what. You can give hints and help to and receive hints and help from other students on homework, but after getting help, do the assignments yourself and hand in your own work.

Projects: Three ideas for projects are listed below, together with guidelines for posing your own ideas. I anticipate that most or all students will choose one of the three ideas that I have posed. Inevitably, more than one student will chose the same project, in which case it makes sense for the group to work together. However, all three projects can be divided into sub-projects for students who wish to work alone. When subdividing projects, considerable coordination and communication within groups will remain necessary to avoid duplication of effort and allow each student’s work to build upon the work of the others. As explained above, once project assignments are settled, I expect to talk to each person or team weekly about their progress, either during lab, or in my office, by appointment. Teams are encouraged to talk to me together. Weekly progress will be rewarded by participation points (described below) and also, if turned in with your homework, by bonus points (described above).

Project Grades: Your projects will be evaluated at three milestones throughout the semester. At each milestone, each person (coordinated with their group) will give a 10 minute (or less) presentation to the class about how their work is coming. The first milestone will be a project proposal, fleshing out research plans, early in the semester. The second milestone will be a progress report, mid-semester. The last milestone will be the final presentation on the last day of class. At each milestone each person will be given a grade between 0 and 100. For computing the final project grade (worth half of the course grade), the grades for the first two milestones will each be weighted at 10%; the last will be worth 80%. This arrangement will give students the chance to see how I grade before the grading really counts. Of the 100 points for each milestone, 50 will be for participation (consistent effort and progress every week), 30 will be for communication (a good presentation), and 20 for effectiveness (good research). Additionally up 10 bonus points will be awarded for creativity. One creativity point will be easy to get; I’ll be miserly with the other points.

Project idea guidelines: We will be testing a statistical method for developing models of single neurons. A good project is one which tests this method on making an inference about a single neuron which might be valuable to an experimentalist. You are not required to solve the problem for full credit, only make progress.

Project 1: (a new look at a classical problem): Could Hodgkin and Huxley have understood the action potential without the voltage-clamp? That is, could Hodgkin and Huxley have succeeded with the methods I will introduce in class and just a current clamp, or are our methods so limited that to do the most basic research in neuroscience you still need the tool that makes our methods superfluous?

Project 2: (most practical): When can you infer the presence of specific ionic currents, and understand their properties, using these methods?

Project 3: (most mathematical): Can you detect backpropagation in a model of weakly electric fish sensory cells from a somatic voltage measurements? This project could be interesting because the model to be studied exhibits “chaos.” I am adding a new thought about project 3.

Beyond the classroom: I developed “Modeling and Identifying Neurosystems” with the intention of introducing students to research in computational neuroscience, and with the hope of increasing student’s internal motivation and desire for creative work. If you find that passion for research compels you to continue, opportunities are there. With modestly more effort, your project could be turned into a poster that could be presented at a conference. Some conferences award travel stipends to undergraduates. With considerably more effort, your project could be turned into a paper that would stand a decent chance of acceptance in a good journal. I do plan to pursue this endeavor. If you are interested, I will invite you to continue to collaborate with me and we will coauthor a paper together. If you do not have the time or interest for a continued collaboration, you can still be my coauthor provided you have made a significant contribution to the project during class (but aware that this threshold may be somewhat higher than the minimum requirements for an A in the course).

Note: My homepage has moved to the LIMBS wiki.