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Journal article

Uncertainty-guided learning with scaled prediction errors in the basal ganglia

Abstract:
To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We show that the new model has an advantage over conventional reinforcement learning models in a value tracking task, and approaches a theoretic limit of performance provided by the Kalman filter. Further, we propose a possible biological implementation of the model in the basal ganglia circuit. In the proposed network, dopaminergic neurons encode reward prediction errors scaled by standard deviation of rewards. We show that such scaling may arise if the striatal neurons learn the standard deviation of rewards and modulate the activity of dopaminergic neurons. The model is consistent with experimental findings concerning dopamine prediction error scaling relative to reward magnitude, and with many features of striatal plasticity. Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1371/journal.pcbi.1009816

Authors


More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
ORCID:
0000-0002-0399-574X
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
ORCID:
0000-0003-0735-4349
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Oxford college:
All Souls College
Role:
Author
ORCID:
0000-0002-8994-1661


More from this funder
Funder identifier:
https://ror.org/03x94j517
Grant:
MC_UU_12024/5
MC_UU_00003/1
More from this funder
Funder identifier:
https://ror.org/00cwqg982
Grant:
BB/S006338/1


Publisher:
Public Library of Science
Journal:
PLoS Computational Biology More from this journal
Volume:
18
Issue:
5
Article number:
e1009816
Place of publication:
United States
Publication date:
2022-05-27
Acceptance date:
2022-05-05
DOI:
EISSN:
1553-7358
ISSN:
1553-734X
Pmid:
35622863


Language:
English
Keywords:
Pubs id:
1262389
Local pid:
pubs:1262389
Deposit date:
2024-02-22

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