Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva
Summary: 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....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223027177 |
| _version_ | 1797359972538908672 |
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| author | Anna-Maria Jürgensen Panagiotis Sakagiannis Michael Schleyer Bertram Gerber Martin Paul Nawrot |
| author_facet | Anna-Maria Jürgensen Panagiotis Sakagiannis Michael Schleyer Bertram Gerber Martin Paul Nawrot |
| author_sort | Anna-Maria Jürgensen |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-03-08T15:31:40Z |
| format | Article |
| id | doaj.art-7467031233164986b3d2f13d91e84e5a |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-08T15:31:40Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-7467031233164986b3d2f13d91e84e5a2024-01-10T04:39:06ZengElsevieriScience2589-00422024-01-01271108640Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larvaAnna-Maria Jürgensen0Panagiotis Sakagiannis1Michael Schleyer2Bertram Gerber3Martin Paul Nawrot4Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, GermanyComputational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, GermanyLeibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany; Institute for the Advancement of Higher Education, Faculty of Science, Hokkaido University, Sapporo 060-08080, JapanLeibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany; Institute for Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany; Center for Brain and Behavioral Sciences (CBBS), Otto-von-Guericke University, 39118 Magdeburg, GermanyComputational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004223027177BioinformaticsBiological sciencesNatural sciencesNeuroscienceTechniques in neuroscience |
| spellingShingle | Anna-Maria Jürgensen Panagiotis Sakagiannis Michael Schleyer Bertram Gerber Martin Paul Nawrot Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva iScience Bioinformatics Biological sciences Natural sciences Neuroscience Techniques in neuroscience |
| title | Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva |
| title_full | Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva |
| title_fullStr | Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva |
| title_full_unstemmed | Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva |
| title_short | Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva |
| title_sort | prediction error drives associative learning and conditioned behavior in a spiking model of drosophila larva |
| topic | Bioinformatics Biological sciences Natural sciences Neuroscience Techniques in neuroscience |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223027177 |
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