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

ABSTRACT: 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 lar

SUBMITTER: Croteau-Chonka EC 

PROVIDER: S-EPMC9678368 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature