Autoren:	Winkelmeier, Laurens Filosa, Carla Hartig, Renée Scheller, Max Sack, Markus Reinwald, Jonathan R. Becker, Robert Wolf, David Gerchen, Martin Fungisai Sartorius, Alexander Meyer-Lindenberg, Andreas Fahr-Weber, Wolfgang Clemm von Hohenberg, Christian Russo, Eleonora Kelsch, Wolfgang
Titel:	Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning
Online-Publikationsdatum:	10-Feb-2023
Erscheinungsdatum:	2022
Sprache des Dokuments:	Englisch
Zusammenfassung/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.
DDC-Sachgruppe:	610 Medizin 610 Medical sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 04 Medizin
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-8796
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Nature Communications 13
Seitenzahl oder Artikelnummer:	3305
Verlag:	Nature Publishing Group
Verlagsort:	London
Erscheinungsdatum:	2022
ISSN:	2041-1723
DOI der Originalveröffentlichung:	10.1038/s41467-022-30978-1
Enthalten in den Sammlungen:	DFG-491381577-G