CSHL projects: variance estimation and N-AFC psychophysics

CSHL computational neuroscience: vision is over, and we presented projects using some of the new methods and ideas we explored in class. Earlier in the class, Geoff Boynton did a presentation on psychophysics, and illustrated how to estimate a threshold through Maximum Likelihood in a N-AFC task, where N >= 2. I was curious to … More CSHL projects: variance estimation and N-AFC psychophysics

Learning about GLMs and GAMs in neuroscience

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 … More Learning about GLMs and GAMs in neuroscience

Bounds on accountable deviance in Generalized Linear Models

The quality of fit in Generalized Linear Models (GLMs) is usually quantified by the deviance, or twice the negative log-likelihood. When there’s a high level of noise in the data, it’s difficult to interpret the deviance directly; the lower bound for the deviance doesn’t take into account noise, and is much too low. I had … More Bounds on accountable deviance in Generalized Linear Models