Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning
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Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning. / 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; Weber-Fahr, Wolfgang; Clemm von Hohenberg, Christian; Russo, Eleonora; Kelsch, Wolfgang.
in: NAT COMMUN, Jahrgang 13, Nr. 1, 3305, 08.06.2022.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning
AU - Winkelmeier, Laurens
AU - Filosa, Carla
AU - Hartig, Renée
AU - Scheller, Max
AU - Sack, Markus
AU - Reinwald, Jonathan R
AU - Becker, Robert
AU - Wolf, David
AU - Gerchen, Martin Fungisai
AU - Sartorius, Alexander
AU - Meyer-Lindenberg, Andreas
AU - Weber-Fahr, Wolfgang
AU - Clemm von Hohenberg, Christian
AU - Russo, Eleonora
AU - Kelsch, Wolfgang
N1 - © 2022. The Author(s).
PY - 2022/6/8
Y1 - 2022/6/8
N2 - 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.
AB - 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.
U2 - 10.1038/s41467-022-30978-1
DO - 10.1038/s41467-022-30978-1
M3 - SCORING: Journal article
C2 - 35676281
VL - 13
JO - NAT COMMUN
JF - NAT COMMUN
SN - 2041-1723
IS - 1
M1 - 3305
ER -