MA666: Advanced Modeling and Data Analysis in Neuroscience

Course Webpage (Fall 2012)

Course Instructor: Mark Kramer (email, 3-4591)
Course Hours: 12:30-2 PM / TTh / MCS B23
Office Hours: 2:00-3:30 PM / TTh, or by appointment
Textbook: MATLAB for Neuroscientists
Handouts . . .

Course goals

In this course we will focus on the quantification and modeling of voltage data generated by the brain at a variety of spatial scales --- from single neurons to the EEG. The main goals of this course are: (1) To introduce more sophisticated mathematical concepts encountered in neuroscience research and more advanced neuroscience graduate courses. (2) To practice implementing programs in MATLAB. (3) To think about neuroscience in a quantitative way.

Course Flyer



The plan - it's still flexible . . .

Labs, Reading, Homework

Week 1 (8)
Lecture: Introduction + Measures of coupling.

Lab: Cross correlation and coherence code and my_cc_circ_shift.m.
Sample data - Case_Study_1.mat and Case_Study_2.mat.
Optional Reading:

Week 2 (9)
Lecture: Cross frequency coupling and bicoherence.
Lab: Application of cross frequency coupling and bicoherence code and bicoherence.m.
Sample data - data_1.mat and data_2.mat.
Optional Reading:

Week 3 (10)
Lecture: Relating spikes and fields through coherence.
Lab: The spike train spectrum and spike-field coherence code and my_cc_circ_shift.m.
Sample data - case_study_1.mat and case_study_2.mat.
Optional Reading:

Week 4 (11)
Lecture: The FitzHugh-Nagumo model.
Lab: Analysis of the FitzHugh-Nagumo model.
Motivation - MATLAB code to first explore the Hodgkin-Huxley model.
Download and install XPPAUT binary from here.
Assignment for this week. The FitzHugh_Nagumo.ode file for XPPAUT. The FitzHugh_Nagumo.nb file for Mathematica.
Optional Reading:

Week 5 (12)
Lecture: Bifurcations and zero eigenvalues.
Lab: More practice with XPPAUT.
If you haven't already done so, download and install an XPPAUT binary from here.
Assignment for this week. The FitzHugh_Nagumo.ode file for XPPAUT (same as last week).
Optional Reading:

Week 6 (13)
Lecture: Three models of gamma.
Lab: ING, PING, and sparse PING. Lab code. Other code we'll need: HH0.m, ing.m, ping.m, and sparse_ping.m.
Optional Reading: