From eye to AI: studying rodent social behavior in the era of machine Learning
This work aims to assist researchers in social neuroscience by facilitating the adoption of computational tools for more accurate rodent behavior analysis, though it is incremental as it reviews existing methods and suggests improvements.
The paper discusses the shift from human observation to AI and machine learning methods for studying rodent social behavior, highlighting their advantages in reducing bias and capturing complexity, while addressing challenges and providing practical guidance for researchers.
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.