Predictive olfactory learning in Drosophila

Abstract Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs)...

Description complète

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chang Zhao, Yves F. Widmer, Sören Diegelmann, Mihai A. Petrovici, Simon G. Sprecher, Walter Senn
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Medicine R Science Q
Accès en ligne:	https://doaj.org/article/c2f90342db924b4eaf22516f0a87b60f
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

id	oai:doaj.org-article:c2f90342db924b4eaf22516f0a87b60f
record_format	dspace
spelling	oai:doaj.org-article:c2f90342db924b4eaf22516f0a87b60f2021-12-02T16:35:57ZPredictive olfactory learning in Drosophila10.1038/s41598-021-85841-y2045-2322https://doaj.org/article/c2f90342db924b4eaf22516f0a87b60f2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85841-yhttps://doaj.org/toc/2045-2322Abstract Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.Chang ZhaoYves F. WidmerSören DiegelmannMihai A. PetroviciSimon G. SprecherWalter SennNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chang Zhao Yves F. Widmer Sören Diegelmann Mihai A. Petrovici Simon G. Sprecher Walter Senn Predictive olfactory learning in Drosophila
description	Abstract Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.
format	article
author	Chang Zhao Yves F. Widmer Sören Diegelmann Mihai A. Petrovici Simon G. Sprecher Walter Senn
author_facet	Chang Zhao Yves F. Widmer Sören Diegelmann Mihai A. Petrovici Simon G. Sprecher Walter Senn
author_sort	Chang Zhao
title	Predictive olfactory learning in Drosophila
title_short	Predictive olfactory learning in Drosophila
title_full	Predictive olfactory learning in Drosophila
title_fullStr	Predictive olfactory learning in Drosophila
title_full_unstemmed	Predictive olfactory learning in Drosophila
title_sort	predictive olfactory learning in drosophila
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c2f90342db924b4eaf22516f0a87b60f
work_keys_str_mv	AT changzhao predictiveolfactorylearningindrosophila AT yvesfwidmer predictiveolfactorylearningindrosophila AT sorendiegelmann predictiveolfactorylearningindrosophila AT mihaiapetrovici predictiveolfactorylearningindrosophila AT simongsprecher predictiveolfactorylearningindrosophila AT waltersenn predictiveolfactorylearningindrosophila
_version_	1718383656231239680

Predictive olfactory learning in Drosophila

Documents similaires