Deep learning tools for the measurement of animal behavior in neuroscience

TL;DR

This paper reviews recent progress in deep learning-based computer vision tools, especially pose estimation, for accurately measuring animal behavior in neuroscience, highlighting rapid advancements and future potential.

Contribution

It provides an overview of how deep learning methods are transforming behavioral measurement in neuroscience, emphasizing pose estimation advancements.

Findings

Deep learning has significantly improved behavior measurement accuracy.

Pose estimation techniques are rapidly advancing in neuroscience applications.

Challenges remain but future developments are promising.

Abstract

Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the measurement of behavior have come to fruition. Here we discuss how capturing the postures of animals - pose estimation - has been rapidly advancing with new deep learning methods. While challenges still remain, we envision that the fast-paced development of new deep learning tools will rapidly change the landscape of realizable real-world neuroscience.