EthoPy: Reproducible Behavioral Neuroscience Made Simple

Abstract

As brain activity is tightly coupled to behavior, an accurate understanding of neural function necessitates consideration of behavioral tasks that capture the complexity and variety animals encounter. Nevertheless, animal training for behavioral experiments is often labor-intensive, costly, and difficult to standardize. To overcome these challenges, we developed EthoPy, an open-source, Python-based behavioral control framework that integrates stimulus presentation, hardware management, and data logging. EthoPy supports diverse behavioral paradigms, stimulus modalities, and experimental systems, from homecage to head-fixed configurations, while operating on affordable hardware, such as Raspberry Pi. Its modular architecture and database integration enable scalable, high-throughput automatic behavioral training with minimal experimenter involvement while ensuring reproducibility through comprehensive metadata tracking. By automating training workflows, EthoPy makes it feasible to implement sophisticated behavioral paradigms that are traditionally difficult to achieve. EthoPy thus provides an accessible, extensible framework to study behavior and the underlying neural activity.