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© Research
Publication : eLife

High-throughput automated methods for classical and operant conditioning of Drosophila larvae.

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in eLife - 28 Oct 2022

Croteau-Chonka EC, Clayton MS, Venkatasubramanian L, Harris SN, Jones BMW, Narayan L, Winding M, Masson JB, Zlatic M, Klein KT,

Link to Pubmed [PMID] – 36305588

Link to DOI – 10.7554/eLife.70015e70015

Elife 2022 Oct; 11():

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i. e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i. e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.

https://pubmed.ncbi.nlm.nih.gov/36305588