neural coding & computation group
Generalized Linear Model (GLM) for single and multi-neuron spike trains
description: Simulates and computes maximum likelihood estimates for the parameters of a GLM spike train model, as described in Pillow et al 2008.
Model arameters are defined by a bank of stimulus filters ("receptive fields"), spike-history filters, and coupling filters that capture dependencies between neurons. The stimulus filter can be parametrized linearly or bi-linearly, and the nonlinearity can be specified a priori (from a class ensuring concavity of the log-likelihood), or fit using cubic splines. The model generalizes the single-filter "Linear-Nonlinear-Poisson" model to incorporate spike-history effects and dependencies between neurons.
clone: github page
more info: README.html
Pillow JW, Shlens J, Paninski L, Sher A, Litke AM, Chichilnisky EJ, Simoncelli EP. (2008) "Spatio-temporal correlations and visual signaling in a complete neuronal population." Nature 454: 995-999. (abstract | pdf | supplementary inf )
note: if you're interested in code for trial-based neural data, where the primary focus is on dissecting the influence of various external covariates on the response, you might want the neuroGLM code (from Park et al, Nat Neurosci 2008) package instead.