Tutorial 2: Classifiers and Regularizers

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