Statistics for Neuroscience

This is a graduate-level lecture course covering statistical reasoning and techniques for neuroscience. The focus is on, 1. the foundations of statistical inference (probability theory, linear algebra, and statistical models); 2. hierarchical (mixed effect) general linear models as a framework for both classic techniques and modern extensions; 3. other contemporary methods relevant to neuroscience (including nonparametric and Monte Carlo techniques, Bayesian approaches, and estimating models by maximizing likelihood). There is emphasis on practical exercises with computation using R, and on example applications to neuroscientific data.