Research Article: AutonoMouse: High throughput operant conditioning reveals progressive impairment with graded olfactory bulb lesions

Date Published: March 6, 2019

Publisher: Public Library of Science

Author(s): Andrew Erskine, Thorsten Bus, Jan T. Herb, Andreas T. Schaefer, Johannes Reisert.


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.

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The ultimate function of the brain is to orchestrate an organism’s behaviour appropriately according to its current environment. Behavioural techniques are therefore an important tool in neuroscience research [1–12]. Over the last decades, a number of technical advances have allowed for revolutionary improvements in efficiency and throughput in molecular biology [13], physiology [14], anatomy [15,16] and corresponding analysis techniques [14,17–20]. By contrast, with some notable exceptions [21,22] in particular in the analysis of movement patterns [12,23–28], behavioural techniques have not seen similarly radical advances in levels of standardisation and throughput, despite their importance to the field.




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