We develop code for analyzing neurophysiology datasets in various contexts, with a focus on capturing nonlinear, physiologically relevant, computations performed in neural systems. Code is in both Matlab (historical) and Python.
Published code
-
The Nonlinear Input Model (NIM), published in McFarland et al. (PLoS CB, 2013). [More information]
A statistical model for describing nonlinear computation in sensory neurons. It is in form of an LNLN (linear-nonlinear x2) cascade, with its predicted firing rate given as a sum over nonlinear inputs followed by a spiking nonlinearity. -
The Separable NIM (sNIM), published in Shi et al. (Sci Rep, 2019). [Github]
The sNIM is a version of the NIM (see above) that uses spatiotemporal filters comprised of space-time-separable elements. This allows for detailed spatiotemporal characterization of spatial and temporal sensitivity for neurons with receptive fields where this approximation is valid, such as in the retina. -
The Rectified Latent Variable Model (RVLM) published in Whiteway and Butts (J Neurophys, 2017). [Github]
The Rectified Latent Variable Model (RLVM) is a probabilistic model for describing the activity of a large population of neurons based on a much smaller set of inputs (i.e., latent variables). Key elements of the model that distinguish it from other approaches are the constraint that the latent variables be non-negative (e.g., like neural activity), and a lack of other constraints (i.e., need not be uncorrelated, independent, Gaussian-distributed, etc.) -
Precise eye-tracking using V1 neural activity published in McFarland et al. (Nat Comm, 2014). [More info]
An algorithm that uses probabilistic models (see NIM) of the stimulus processing of visual cortical neurons to infer an animal's eye position from the spiking activity of a recorded neural population. - GLM implementation of stimulus- and choice-driven activity in V2 and V3, supporting Quinn et al. (Nat Comm, 2021) [Github]