From eye to AI: studying rodent social behavior in the era of machine Learning

TL;DR

This paper reviews how machine learning and AI are transforming rodent social behavior studies by offering more nuanced, less biased insights, while also discussing challenges and practical solutions for researchers.

Contribution

It provides a comprehensive overview of AI tools in rodent social behavior research, highlighting benefits, limitations, and practical guidance for adoption.

Findings

AI enhances analysis of complex social interactions

Modern methods reduce observer bias

Challenges include technical and interpretative hurdles

Abstract

The study of rodent social behavior has shifted in the last years from relying on direct human observation to more nuanced approaches integrating computational methods in artificial intelligence (AI) and machine learning. While conventional approaches introduce bias and can fail to capture the complexity of rodent social interactions, modern approaches bridging computer vision, ethology and neuroscience provide more multifaceted insights into behavior which are particularly relevant to social neuroscience. Despite these benefits, the integration of AI into social behavior research also poses several challenges. Here we discuss the main steps involved and the tools available for analyzing rodent social behavior, examining their advantages and limitations. Additionally, we suggest practical solutions to address common hurdles, aiming to guide young researchers in adopting these methods and to stimulate further discussion among experts regarding the evolving requirements of these tools in scientific applications.