Learning with reinforcement prediction errors in a model of the Drosophila mushroom body

Bennett, James E M, Philippides, Andrew and Nowotny, Thomas (2021) Learning with reinforcement prediction errors in a model of the Drosophila mushroom body. Nature Communications, 12 (1). a2569 1-14. ISSN 2041-1723

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Abstract

Effective decision making in a changing environment demands that accurate predictions are learned about decision outcomes. In Drosophila, such learning is orchestrated in part by the mushroom body, where dopamine neurons signal reinforcing stimuli to modulate plasticity presynaptic to mushroom body output neurons. Building on previous mushroom body models, in which dopamine neurons signal absolute reinforcement, we propose instead that dopamine neurons signal reinforcement prediction errors by utilising feedback reinforcement predictions from output neurons. We formulate plasticity rules that minimise prediction errors, verify that output neurons learn accurate reinforcement predictions in simulations, and postulate connectivity that explains more physiological observations than an experimentally constrained model. The constrained and augmented models reproduce a broad range of conditioning and blocking experiments, and we demonstrate that the absence of blocking does not imply the absence of prediction error dependent learning. Our results provide five predictions that can be tested using established experimental methods.

Item Type: Article
Keywords: Animals, Dopaminergic Neurons, Drosophila, Feedback, Learning, Models, Neurological, Mushroom Bodies, Neuronal Plasticity, Reinforcement, Psychology, Reward
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 10 Sep 2021 09:48
Last Modified: 10 Sep 2021 10:00
URI: http://sro.sussex.ac.uk/id/eprint/101623

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