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Modeling and Identifying Neural Systems
Instructor: Sean G. Carver, Ph.D., Psychological and Brain Sciences, The Johns Hopkins University.
Semester Offered: Spring 2009.
One Hundred Word Description: Students in this course will be trained to perform and assigned original research in computational neuroscience. The course will cover mathematical modeling of neurons, 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.
Prerequisites: Calculus I and II, and Nervous System I and II, or permission of instructor.
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...
Course Mechanics: This class will be a hands on experience. Pending approval, class will meet twice a week in the Kreiger computer classroom. Each meeting will last 1.5 hours. In addition, there will be three hours per week of supervised computer laboratory time. Attendance for the laboratory will be optional. The purpose of the laboratory will be to allow students, if they choose, to complete computer assignments with the help of the instructor. An effort will be made to design the homework sets to allow most students to complete most of the assignments during the laboratory time. Grading will be determined partly by homework (due at the beginning of most class periods) and partly by final projects. I am not planning exams.
Textbook: Neurons in Action: Tutorials and Simulations Using NEURON, Version 2. By John W. Moore and Ann E. Stuart. Sinauer Associates, Inc, Sunderland Massachusetts.
Tentative Syllabus: (click here)
Research Projects: One of the challenges of neural system identification is the reality that the neural systems generating the experimental data remain inevitably more complicated than the models being fitted. What sort of problems this presents in practice is unknown. For final projects, students will test approximate maximum likelihood parameter estimation under the situation just described (the generating system is more complicated than fitted model). To perform these tests students will choose between a one, two, or three compartment model to generate the data, and will fit one of these three model structures to the simulated data. To generate the data, students will decide what injected current stimulus to provide to each compartment. To fit the data, students will specify the correctly known, incorrectly assumed, and unknown parameters. Students will make all choices by editing a single MATLAB script. It will take 5-15 minutes to do the editing needed to run an experiment and perhaps overnight to run the experiment. In mid-semester students will submit a proposal for their project, and thereafter run one experiment before each class.
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