  Experimental neuroscience increasingly requires tractable models for
analyzing and predicting the behavior of neurons and networks. The generalized
linear model (GLM) is an increasingly popular statistical framework for
analyzing neural data that is flexible, exhibits rich dynamic behavior and is
computationally tractable (Paninski, 2004; Pillow et al., 2008; Truccolo et
al., 2005). What follows is a brief summary of the primary equations governing
the application of GLM's to spike trains with a few sentences linking this work
to the larger statistical literature. Latter sections include extensions of a
basic GLM to model spatio-temporal receptive fields as well as network activity
in an arbitrary numbers of neurons.
