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| Home > Publications Database > Network state changes in sensory thalamus represent learned outcomes > print |
| 001 | 263628 | ||
| 005 | 20230924001302.0 | ||
| 024 | 7 | _ | |a 10.1101/2023.08.23.554119 |2 doi |
| 024 | 7 | _ | |a altmetric:153282692 |2 altmetric |
| 037 | _ | _ | |a DZNE-2023-00847 |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Hasegawa, Masashi |0 P:(DE-2719)9001582 |b 0 |e First author |u dzne |
| 245 | _ | _ | |a Network state changes in sensory thalamus represent learned outcomes |
| 260 | _ | _ | |a Cold Spring Harbor |c 2023 |b Cold Spring Harbor Laboratory, NY |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1695284126_8401 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Thalamic brain areas play an important role in adaptive behaviors. Nevertheless, the population dynamics of thalamic relays during learning across sensory modalities remain mostly unknown. Using a cross-modal sensory reversal learning paradigm combined with deep brain two-photon calcium imaging of large populations of auditory thalamus (MGB) neurons, we identified that MGB neurons are biased towards reward predictors independent of modality. Additionally, functional classes of MGB neurons aligned with distinct task periods and behavioral outcomes, both dependent and independent of sensory modality. During non-sensory delay periods, MGB ensembles developed coherent neuronal representation as well as distinct co-activity network states reflecting predicted task outcome. These results demonstrate flexible cross-modal ensemble coding in auditory thalamus during adaptive learning and highlight its importance in brain-wide cross-modal computations during complex behavior. |
| 536 | _ | _ | |a 351 - Brain Function (POF4-351) |0 G:(DE-HGF)POF4-351 |c POF4-351 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Huang, Ziyan |0 P:(DE-2719)9001374 |b 1 |u dzne |
| 700 | 1 | _ | |a Gründemann, Jan |0 P:(DE-2719)9001219 |b 2 |e Last author |
| 773 | _ | _ | |a 10.1101/2023.08.23.554119 |0 PERI:(DE-600)2766415-6 |t bioRxiv beta |y 2023 |
| 856 | 4 | _ | |y OpenAccess |u https://pub.dzne.de/record/263628/files/DZNE-2023-00847.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://pub.dzne.de/record/263628/files/DZNE-2023-00847.pdf?subformat=pdfa |
| 909 | C | O | |o oai:pub.dzne.de:263628 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 0 |6 P:(DE-2719)9001582 |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 1 |6 P:(DE-2719)9001374 |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 2 |6 P:(DE-2719)9001219 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Neurodegenerative Diseases |1 G:(DE-HGF)POF4-350 |0 G:(DE-HGF)POF4-351 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Brain Function |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 920 | 1 | _ | |0 I:(DE-2719)5000069 |k AG Gründemann |l Neural Circuit Computations |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-2719)5000069 |
| 980 | 1 | _ | |a FullTexts |
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