EthoPy: Reproducible Behavioral Neuroscience Made Accessible

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. Yet, 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 for behavioral studies and their underlying neural activity.由于脑活动与行为紧密耦联，要精准解析神经功能，必须纳入能够捕捉动物所经历的复杂多样情境的行为任务。然而，为行为实验开展的动物训练往往劳动强度大、成本高昂且难以标准化。为解决上述难题，我们研发了EthoPy——一款基于Python的开源行为控制框架，集成了刺激呈现、硬件管理与数据记录三大功能模块。EthoPy可适配多样化的行为范式、刺激模态与实验系统，涵盖居家饲养笼至头部固定装置等多种场景，且可在树莓派（Raspberry Pi）这类低成本硬件上运行。其模块化架构与数据库集成功能，支持实验人员极低参与度下的可扩展、高通量自动化行为训练，并通过全面的元数据追踪确保实验可重复性。通过自动化训练流程，EthoPy使得实现传统手段难以达成的复杂行为范式成为可能。因此，EthoPy为行为研究及其背后的神经活动分析提供了一套易用且可扩展的框架。