Tutorial 1: Generalized Linear Models (GLMs)
- Source: International Neuroinformatics Coordinating Facility
This tutorial covers Generalized Linear Models (GLMs), which are a fundamental framework for supervised learning. In this tutorial, the objective is to model a retinal ganglion cell spike train by fitting a temporal receptive field: first with a Linear-Gaussian GLM (also known as ordinary least-squares regression model) and then with a Poisson GLM (aka "Linear-Nonlinear-Poisson" model). This tutorial also covers a special case of GLMs, logistic regression, and learn how to ensure good model performance. This tutorial is designed to run with retinal ganglion cell spike train data from Uzzell & Chichilnisky 2004.
Topics covered in this lesson:
- Modeling retinal ganglion spike train by fitting a temporal receptive field
- Linear-Gaussian GLM
- Poisson GLM
- Logic regression