pillow lab @ princeton

neural coding & computation group



Princeton Neuroscience Institute
Dept. of Psychology
Ctr. for Statistics & Machine Learning

Single-neuron GLM for trial-based data - matlab code


description: Supports flexible regression analyses of trial-based spike train data using a Generalized Linear Model (GLM). This modeling framework aims to discover how neural responses encode both external (e.g., sensory, motor, reward variables) and internal (e.g., spike history, LFP signals) covariates of the response, as described in Park et al 2014.

download: neuroGLM-master.zip
clone: github page
more info: README.html


relevant publication:

Park IM, Meister MLR, Huk AC, & Pillow JW. (2014) "Deciphering the code for sensorimotor decision-making in parietal cortex", Nat Neurosci 17, 1395-1403.   [abs | pdf | SI | link ]


note: if you're interested in modeling the fine timescale spike-history dependencies in single or multi-neuron spike trains from white-noise type "reverse correlation" experiments, you might want the GLMspiketools code (from Pillow et al, Nature 2008) instead.



Please report any bugs to pillow@princeton.edu