New tool RTFED enhances real-time home-cage monitoring of feeding and operant behaviours in mice, suggesting improved experimental flexibility and animal welfare.
Background Conventional approaches for studying feeding and reward-driven behaviours require frequent animal handling or relocation of animals to specialized chambers, inducing stress, confounding behavioural outcomes, and limiting continuous (24/7) data collection. In recent years, the Feeding Experimentation Device (FED3) has emerged as a major advance, offering programmable modes of operation, affordable costs, and flexibility for investigating a range of feeding and operant behaviours. However, certain limitations prevent researchers from fully harnessing the FED3’s capabilities in a user-friendly manner. New method Here, we present the Realtime and Remote FED3 (RTFED) developed for continuous and online home-cage monitoring of mice. Operating on both Raspberry Pi and Windows, RTFED integrates with FED3 to log and transmit feeding events and operant behaviours in real-time. It also incorporates event-triggered video capture through USB cameras, providing additional observational depth. Moreover, RTFED can send TTL signals to external devices (e.g., fibre photometry systems) for precise behaviour-neural synchronization. A key strength of RTFED is its customizable open-source architecture, enabling researchers to tailor both software and hardware configurations to meet specific experimental objectives. Comparison with existing methods This flexibility, together with features such as remote data logging and email notifications that allow timely adjustments and animal welfare monitoring based on behavioural observations, substantially reduces animal disturbance and researcher intervention and labour. Conclusion By offering a cost-effective and modifiable alternative to proprietary commercial solutions, RTFED broadens accessibility, heightens reproducibility, and deepens investigations into feeding and reward-driven behaviours in home-cage settings, ultimately improving the quality and translational relevance of behavioural research.
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Taghipourbibalan et al. (2025) studied this question.
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