Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning

Laurens Winkelmeier, Carla Filosa, Renée Hartig, Max Scheller, Markus Sack, Jonathan R. Reinwald, Robert Becker, David Wolf, Martin Fungisai Gerchen, Alexander Sartorius, Andreas Meyer-Lindenberg, Wolfgang Weber-Fahr, Christian Clemm von Hohenberg, Eleonora Russo and Wolfgang Kelsch ()
Additional contact information
Laurens Winkelmeier: Heidelberg University
Carla Filosa: University Medical Center, Johannes Gutenberg University
Renée Hartig: University Medical Center, Johannes Gutenberg University
Max Scheller: University Medical Center, Johannes Gutenberg University
Markus Sack: Heidelberg University
Jonathan R. Reinwald: Heidelberg University
Robert Becker: Heidelberg University
David Wolf: Heidelberg University
Martin Fungisai Gerchen: Heidelberg University
Alexander Sartorius: Heidelberg University
Andreas Meyer-Lindenberg: Heidelberg University
Wolfgang Weber-Fahr: Heidelberg University
Christian Clemm von Hohenberg: Heidelberg University
Eleonora Russo: Heidelberg University
Wolfgang Kelsch: Heidelberg University

Nature Communications, 2022, vol. 13, issue 1, 1-21

Abstract: Abstract Identifying the circuits responsible for cognition and understanding their embedded computations is a challenge for neuroscience. We establish here a hierarchical cross-scale approach, from behavioral modeling and fMRI in task-performing mice to cellular recordings, in order to disentangle local network contributions to olfactory reinforcement learning. At mesoscale, fMRI identifies a functional olfactory-striatal network interacting dynamically with higher-order cortices. While primary olfactory cortices respectively contribute only some value components, the downstream olfactory tubercle of the ventral striatum expresses comprehensively reward prediction, its dynamic updating, and prediction error components. In the tubercle, recordings reveal two underlying neuronal populations with non-redundant reward prediction coding schemes. One population collectively produces stabilized predictions as distributed activity across neurons; in the other, neurons encode value individually and dynamically integrate the recent history of uncertain outcomes. These findings validate a cross-scale approach to mechanistic investigations of higher cognitive functions in rodents.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30978-1

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DOI: 10.1038/s41467-022-30978-1

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