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A vector reward prediction error model explains dopaminergic heterogeneity

View ORCID ProfileRachel S. Lee, Ben Engelhard, View ORCID ProfileIlana B. Witten, View ORCID ProfileNathaniel D. Daw
doi: https://doi.org/10.1101/2022.02.28.482379
Rachel S. Lee
1Princeton Neuroscience Institute, Princeton University, Princeton NJ USA 08544
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  • For correspondence: rachelstephlee@gmail.com
Ben Engelhard
1Princeton Neuroscience Institute, Princeton University, Princeton NJ USA 08544
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Ilana B. Witten
1Princeton Neuroscience Institute, Princeton University, Princeton NJ USA 08544
2Department of Psychology, Princeton University, Princeton NJ USA 08544
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Nathaniel D. Daw
1Princeton Neuroscience Institute, Princeton University, Princeton NJ USA 08544
2Department of Psychology, Princeton University, Princeton NJ USA 08544
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Abstract

The hypothesis that midbrain dopamine (DA) neurons broadcast an error signal for the prediction of reward (reward prediction error, RPE) is among the great successes of computational neuroscience1–3. However, recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. Instead, when animals are placed in a high-dimensional environment, DA neurons in the ventral tegmental area (VTA) display substantial heterogeneity in the features to which they respond, while also having more consistent RPE-like responses at the time of reward. Here we introduce a new “Vector RPE” model that explains these findings, by positing that DA neurons report individual RPEs for a subset of a population vector code for an animal’s state (moment-to-moment situation). To investigate this claim, we train a deep reinforcement learning model on a navigation and decision-making task, and compare the Vector RPE derived from the network to population recordings from DA neurons during the same task. The Vector RPE model recapitulates the key features of the neural data: specifically, heterogeneous coding of task variables during the navigation and decision-making period, but uniform reward responses. The model also makes new predictions about the nature of the responses, which we validate. Our work provides a path to reconcile new observations of DA neuron heterogeneity with classic ideas about RPE coding, while also providing a new perspective on how the brain performs reinforcement learning in high dimensional environments.

Competing Interest Statement

The authors have declared no competing interest.

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-ND 4.0 International license.
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Posted March 02, 2022.
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A vector reward prediction error model explains dopaminergic heterogeneity
Rachel S. Lee, Ben Engelhard, Ilana B. Witten, Nathaniel D. Daw
bioRxiv 2022.02.28.482379; doi: https://doi.org/10.1101/2022.02.28.482379
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A vector reward prediction error model explains dopaminergic heterogeneity
Rachel S. Lee, Ben Engelhard, Ilana B. Witten, Nathaniel D. Daw
bioRxiv 2022.02.28.482379; doi: https://doi.org/10.1101/2022.02.28.482379

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