Universität zu Köln

Jürgensen, Anna-Maria ORCID: 0000-0002-7871-1887, Sakagiannis, Panagiotis ORCID: 0000-0002-1033-5387, Schleyer, Michael, Gerber, Bertram and Nawrot, Martin Paul ORCID: 0000-0003-4133-6419 (2024). Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva. iScience, 27 (1). pp. 1-21. Elsevier. ISSN 2589-0042

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Abstract

[Artikel-Nr. 108640] Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.

Item Type:	Article
Creators:	Creators Email ORCID ORCID Put Code Jürgensen, Anna-Maria a.juergensen@uni-koeln.de https://orcid.org/0000-0002-7871-1887 UNSPECIFIED Sakagiannis, Panagiotis UNSPECIFIED https://orcid.org/0000-0002-1033-5387 UNSPECIFIED Schleyer, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Gerber, Bertram UNSPECIFIED UNSPECIFIED UNSPECIFIED Nawrot, Martin Paul UNSPECIFIED https://orcid.org/0000-0003-4133-6419 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-784521
Identification Number:	10.1016/j.isci.2023.108640
Journal or Publication Title:	iScience
Volume:	27
Number:	1
Page Range:	pp. 1-21
Number of Pages:	21
Date:	2024
Publisher:	Elsevier
ISSN:	2589-0042
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Biology > Zoologisches Institut
Subjects:	Life sciences
['eprint_fieldname_oa_funders' not defined]:	OAPK DFG 2022-2024
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/78452