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.
This MATLAB code is a reference implementation for the analyses found in Park et al. 2014.
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From command line:
git clone [email protected]:pillowlab/neuroGLM.git -
In browser: click to Download ZIP and then unzip archive
Open tutorial.m to see it in action using a simulated dataset
Suppose we record spike responses from a single neuron during a complex behavioral experiment, and would like to know what aspects of the stimulus or behavior are encoded in the neural response. This code package allows us to discover such dependencies using Poisson GLM regression.
Consider a simple example in which a neuron encodes two experimental variables: the time at which a visual target appears, and the motion strength of a moving-dots stimulus. The regressors are the time at which the targets appear, and the time, duration, and strength ("coherence") of the moving dots on each trial.
- I. M. Park, M. L. R. Meister, A. C. Huk, & J. W. Pillow (2014). Encoding and decoding in parietal cortex during sensorimotor decision-making Nature Neuroscience 17, 1395-1403.