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A mechanistic model for reward prediction and extinction learning in the fruit fly

Magdalena Springer, View ORCID ProfileMartin Paul Nawrot
doi: https://doi.org/10.1101/2020.12.03.409490
Magdalena Springer
Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Biocenter, Zülpicher Str. 47B, 50674 Cologne, Germany
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Martin Paul Nawrot
Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Biocenter, Zülpicher Str. 47B, 50674 Cologne, Germany
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  • ORCID record for Martin Paul Nawrot
  • For correspondence: martin.nawrot@uni-koeln.de
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Abstract

Extinction learning, the ability to update previously learned information by integrating novel contradictory information, is a key mechanism for adapting our behavior and of high clinical relevance for therapeutic approaches to the modulation of maladaptive memories. Insect models have been instrumental in uncovering fundamental processes of memory formation and memory update. Recent experimental results in Drosophila melanogaster suggest that, after the behavioral extinction of a memory, two parallel but opposing memory traces coexist, residing at different sites within the mushroom body. Here we propose a minimalistic circuit model of the Drosophila mushroom body that supports classical appetitive and aversive conditioning and memory extinction. The model is tailored to the existing anatomical data and involves two circuit motives of central functional importance. It employs plastic synaptic connections between Kenyon cells and mushroom body output neurons (MBONs) in separate and mutually inhibiting appetitive and aversive learning pathways. Recurrent modulation of plasticity through projections from MBONs to reinforcement-mediating dopaminergic neurons implements a simple reward prediction mechanism. A distinct set of four MBONs encodes odor valence and predicts behavioral model output. Subjecting our model to learning and extinction protocols reproduced experimental results from recent behavioral and imaging studies. Simulating the experimental blocking of synaptic output of individual neurons or neuron groups in the model circuit confirmed experimental results and allowed formulation of testable predictions. In the temporal domain, our model achieves rapid learning with a step-like increase in the encoded odor value after a single pairing of the conditioned stimulus with a reward or punishment, facilitating single-trial learning.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵# magdalena.springer{at}uni-koeln.de

  • www.computational-systems-neuroscience.de

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted December 04, 2020.
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A mechanistic model for reward prediction and extinction learning in the fruit fly
Magdalena Springer, Martin Paul Nawrot
bioRxiv 2020.12.03.409490; doi: https://doi.org/10.1101/2020.12.03.409490
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A mechanistic model for reward prediction and extinction learning in the fruit fly
Magdalena Springer, Martin Paul Nawrot
bioRxiv 2020.12.03.409490; doi: https://doi.org/10.1101/2020.12.03.409490

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