Fitting and analysis of trial-based neural spike responses with Generalized Linear Model (GLM).
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+basisFactory Ignore spikes outside of the given duration Jan 28, 2016
+buildGLM Time offset range bug fix Apr 25, 2017
docs
matRegress Nonlinear functions imported from ncclabcode Jun 19, 2017
test Stimulus representations for timing/duration Jan 14, 2015
.gitignore
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README.md
loadHukExampleCell.m
tutorial.m
tutorial_GLM_history_only.m Minor typo fix in the comment! Oct 11, 2016
tutorial_exampleData.m Bugfix: multi-dim continuous with basis, now works Jan 19, 2015

README.md

neuroGLM

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.

Downloading the repository

  • From command line:

    git clone git@github.com:pillowlab/neuroGLM.git

  • In browser: click to Download ZIP and then unzip archive

Example Script

Open tutorial.m to see it in action using a simulated dataset

Simple Overview

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.

Extra documentation

Reference