Phasic dopamine reinforces distinct striatal stimulus encoding in the olfactory tubercle driving dopaminergic reward prediction
The learning of stimulus-outcome associations allows for predictions about the environment. Ventral striatum and dopaminergic midbrain neurons form a larger network for generating reward prediction signals from sensory cues. Yet, the network plasticity mechanisms to generate predictive signals in th...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
10 July 2020
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| In: |
Nature Communications
Year: 2020, Volume: 11, Pages: 1-14 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-020-17257-7 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41467-020-17257-7 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41467-020-17257-7 
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| Author Notes: | Lars-Lennart Oettl, Max Scheller, Carla Filosa, Sebastian Wieland, Franziska Haag, Cathrin Loeb, Daniel Durstewitz, Roman Shusterman, Eleonora Russo & Wolfgang Kelsch |
| Summary: | The learning of stimulus-outcome associations allows for predictions about the environment. Ventral striatum and dopaminergic midbrain neurons form a larger network for generating reward prediction signals from sensory cues. Yet, the network plasticity mechanisms to generate predictive signals in these distributed circuits have not been entirely clarified. Also, direct evidence of the underlying interregional assembly formation and information transfer is still missing. Here we show that phasic dopamine is sufficient to reinforce the distinctness of stimulus representations in the ventral striatum even in the absence of reward. Upon such reinforcement, striatal stimulus encoding gives rise to interregional assemblies that drive dopaminergic neurons during stimulus-outcome learning. These assemblies dynamically encode the predicted reward value of conditioned stimuli. Together, our data reveal that ventral striatal and midbrain reward networks form a reinforcing loop to generate reward prediction coding. |
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| Item Description: | Gesehen am 14.09.2026 |
| Physical Description: | Online Resource |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-020-17257-7 |