ETHOPY: REPRODUCIBLE BEHAVIORAL NEUROSCIENCE

Progress in neuroscience increasingly depends on sophisticated behavioral paradigms that capture complex and adaptive animal behavior. However, implementing such experiments at scale remains challenging, as behavioral training is often labor-intensive, costly, and difficult to standardize across laboratories and hardware platforms. To address these challenges, we present EthoPy, an open-source, Python-based framework for designing, running, and monitoring behavioral experiments, with a particular emphasis on automated, high-throughput home-cage training. EthoPy employs a modular software architecture that separates task logic, stimulus control, hardware interfaces, and data management into independent components. This design enables reuse and extension of experimental code across behavioral paradigms, sensory modalities, and experimental systems, ranging from freely-moving to head-fixed configurations, while operating on affordable hardware such as Raspberry Pi. Database integration and comprehensive metadata tracking support scalable automation with minimal experimenter involvement, while ensuring reproducibility and standardized data handling. EthoPy is actively developed, released under an MIT license, distributed via PyPI, and integrates with widely used behavioral and experimental tools. We demonstrate EthoPy across complex behavioral tasks, including visual object recognition and rule switching, across diverse experimental systems ranging from automated home-cage training to open-field and head-fixed setups, such as the Air-Track, and spanning visual, auditory, and olfactory stimulus modalities, highlighting its capacity for adaptive, high-level behavioral training. Together, EthoPy provides an accessible and extensible platform for scalable, reproducible behavioral neuroscience, enabling laboratories to share, standardize, and build upon complex behavioral paradigms.​​​​​