Abstract
Operant conditioning is a crucial tool in neuroscience research for probing brain function. While molecular, anatomical and even physiological techniques have seen radical increases in throughput, efficiency, and reproducibility in recent years, behavioural tools have somewhat lagged behind. Here we present a fully automated, high-throughput system for self-initiated conditioning of up to 25 group-housed, radio-frequency identification (RFID) tagged mice over periods of several months and >106 trials. We validate this "AutonoMouse" system in a series of olfactory behavioural tasks and show that acquired data is comparable to previous semi-manual approaches. Furthermore, we use AutonoMouse to systematically probe the impact of graded olfactory bulb lesions on olfactory behaviour, demonstrating that while odour discrimination in general is robust to even most extensive disruptions, small olfactory bulb lesions already impair odour detection. Discrimination learning of similar mixtures as well as learning speed are in turn reliably impacted by medium lesion sizes. The modular nature and open-source design of AutonoMouse should allow for similar robust and systematic assessments across neuroscience research areas.