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

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posted on 2023-06-10, 00:55 authored by James BennettJames Bennett, Andy PhilippidesAndy Philippides, Thomas NowotnyThomas Nowotny
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Nature Communications

ISSN

2041-1723

Publisher

Nature Research

Issue

1

Volume

12

Page range

1-14

Article number

a2569

Event location

England

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2021-09-10

First Open Access (FOA) Date

2021-09-10

First Compliant Deposit (FCD) Date

2021-09-10

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