High-throughput automated methods for classical and operant conditioning of
D. melanogaster
Drosophila melanogaster larva
behavior detection
closed-loop tracking
computational biology
computer vision
neuroscience
operant conditioning
stimulation
systems biology
trace conditioning
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
28 10 2022
28 10 2022
Historique:
received:
04
05
2021
accepted:
26
10
2022
pubmed:
29
10
2022
medline:
24
11
2022
entrez:
28
10
2022
Statut:
epublish
Résumé
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
Identifiants
pubmed: 36305588
doi: 10.7554/eLife.70015
pii: 70015
pmc: PMC9678368
doi:
pii:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Wellcome Trust
ID : 205050/Z/16/Z
Pays : United Kingdom
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : Medical Research Council
ID : MC_UP_1201/20
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 205050/B/16/Z
Pays : United Kingdom
Informations de copyright
© 2022, Croteau-Chonka, Clayton et al.
Déclaration de conflit d'intérêts
EC, MC, LV, SH, BJ, LN, MW, JM, MZ, KK No competing interests declared
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