Mine Your Own View: A New Self-Supervised Learning Approach for Building Stable Representations of Neural Activity

Understanding how populations of neurons work together to represent stimuli, build percepts, and generate complex behaviors, is a fundamental challenge in neuroscience. To establish a link between neural activity and behavior, especially in complex behaviors and tasks, we need approaches that allow for an unbiased look into the latent factors that underlie neural activity. In this talk, I will describe ways in which my lab is tackling this objective through the development of novel unsupervised representation learning methods. With new tools to read out information from the brain in a stable and generalizable manner, we can enable rich comparisons across neural recordings spanning different time points in learning, or across aging and development.

Learning Objectives:

1. Audience members will learn about new tools for visualizing and interpreting neural activity

2. Learn about the application of representation learning methods on various neural recordings from macaque, rat, and mouse