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Mesolimbic dopamine adapts the rate of learning from action

View ORCID ProfileLuke T. Coddington, Sarah E. Lindo, View ORCID ProfileJoshua T. Dudman
doi: https://doi.org/10.1101/2021.05.31.446464
Luke T. Coddington
1Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA
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  • For correspondence: coddingtonl@hhmi.org dudmanj@janelia.hhmi.org
Sarah E. Lindo
1Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA
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Joshua T. Dudman
1Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA
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  • ORCID record for Joshua T. Dudman
  • For correspondence: coddingtonl@hhmi.org dudmanj@janelia.hhmi.org
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Abstract

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that optimize behavioral performance and reward prediction, respectively. In animals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction; however, to date there has been little consideration of how direct policy learning might inform our understanding. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioral policies evolve as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioral policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically-calibrated manipulations of mesolimbic dopamine produced multiple effects inconsistent with value learning but predicted by a neural network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioral policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioral policies, expanding the explanatory power of reinforcement learning models for animal learning.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Substantial editing of text and reorganization of figures. Description of new experimental data / modeling.

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 4.0 International license.
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Posted May 31, 2022.
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Mesolimbic dopamine adapts the rate of learning from action
Luke T. Coddington, Sarah E. Lindo, Joshua T. Dudman
bioRxiv 2021.05.31.446464; doi: https://doi.org/10.1101/2021.05.31.446464
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Mesolimbic dopamine adapts the rate of learning from action
Luke T. Coddington, Sarah E. Lindo, Joshua T. Dudman
bioRxiv 2021.05.31.446464; doi: https://doi.org/10.1101/2021.05.31.446464

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