I gave a lecture yesterday as part of Chris’ computational neuroscience class on generalized linear and additive models (GLMs and GAMs) and their application to neuroscience. A lot of the people in the class have little to no background in stats so I kept it very basic. I also have exercises involving the estimation of a place field from simulated that borrow heavily from Dr. Paninski’s assignment for his neural data analysis class (he himself inherited them from Uri Eden). Email me if you need the .m files that go with the assignment for a class or for practice.
One response to “Lecture slides on Generalized Linear and Additive models”
[…] During my lecture on Wednesday, a few students asked me where they could learn more about generalized linear and additive models (GLMs and GAMs) and their applications to systems identification in neuroscience. Unfortunately, there are few textbooks in computational neuroscience, and most cover systems identification to some degree, most notably Marmarelis’ latest. To the best of my knowledge, however, neuroscience books are pretty silent on GLMs and GAMs. The statistics literature is of course abundant on this, but it might be a bit inaccessible for people whose background lies in computer science or applied math. […]