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The pyret package contains tools for analyzing neural electrophysiology data. It focuses on applications in sensory neuroscience, broadly construed as any experiment in which one would like to characterize neural responses to a sensory stimulus. Pyret contains methods for manipulating spike trains (e.g. binning and smoothing), pre-processing experimental stimuli (e.g. resampling), computing spike-triggered averages and ensembles (Schwartz et al. 2006), estimating linear-nonlinear cascade models to predict neural responses to different stimuli (Chichilnisky 2001), part of which follows the scikit-learn API (Pedregosa et al. 2011), as well as a suite of visualization tools for all the above. We designed pyret to be simple, robust, and efficient with broad applicability across a range of sensory neuroscience analyses.

Pyret: A Python package for analysis of neurophysiology data