This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience".

This lecture provides an overview of generalized linear models (GLM) and contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.

Topics covered in this lesson:

• Overview of generalized linear models for different types of output and different likelihoods
• Classifiers and regularizers
• Review of maximum likelihood estimation for the parameters and issues around overfitting and regularization

External Links

• Link to Lecture Slides
• Suggested Reading
• Link to Neuromatch Academy
• Link to discussion forum on Neurostars.org

Prerequisites

• Experience with Python Programming Language.

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

External Links:

• Link to Tutorial Exercises
• Link to Tutorial Slides
• Link to Neuromatch Academy

This tutorial covers the implementation of logistic regression, a special case of GLMs used to model binary outcomes. Oftentimes the variable you would like to predict takes only one of two possible values. Left or right? Awake or asleep? Car or bus? In this tutorial, we will decode a mouse's left/right decisions from spike train data.

Objectives of this tutorial:

• Learn about logistic regression, how it is derived within the GLM theory, and how it is implemented in scikit-learn
• Apply logistic regression to decode choices from neural responses
• Learn about regularization, including the different approaches and the influence of hyperparameters

Topics covered in this lesson:

• Logistic regression, how it is derived within the GLM theory, and how it is implemented in scikit-learn
• Apply logistic regression to decode choies from neural responses
• Regularization, including the different approaches and the influence of hyperparameters

External Links

• Link to Tutorial Exercises
• Link to Tutorial Slides
• Link to Neuromatch Academy

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience".

This lecture further develops the concepts introduced in Machine Learning I.

Topics covered in this lesson

• When to use generalized linear models (GLMs): applications to receptive field estimation, single neuron dynamics, population dynamics, disentangling event contributions from firing rates, and decision-making
• How to interpretation GLMs

External Links

• Link to Lecture Slides
• Link to discussion forum on Neurostars.org

Prerequisites

• Completion of Machine Learning I.