Network state changes in sensory thalamus represent learned outcomes.

Hasegawa, Masashi; Huang, Ziyan; Paricio-Montesinos, Ricardo; Gründemann, Jan

doi:10.1038/s41467-024-51868-8

Journal Article

DZNE-2024-01121

Network state changes in sensory thalamus represent learned outcomes.

Hasegawa, M. (First author)DZNE* ; Huang, Z.DZNE* ; Paricio-Montesinos, R.DZNE* ; Gründemann, J. (Last author)DZNE*

2024
Nature Publishing Group UK [London]

Nature Communications 15(1), 7830 (2024) [10.1038/s41467-024-51868-8]

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Please use a persistent id in citations: doi:10.1038/s41467-024-51868-8

Abstract: Thalamic brain areas play an important role in adaptive behaviors. Nevertheless, the population dynamics of thalamic relays during learning across sensory modalities remain unknown. Using a cross-modal sensory reward-associative learning paradigm combined with deep brain two-photon calcium imaging of large populations of auditory thalamus (medial geniculate body, MGB) neurons in male mice, 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.

Keyword(s): Animals (MeSH) ; Male (MeSH) ; Mice (MeSH) ; Geniculate Bodies: physiology (MeSH) ; Thalamus: physiology (MeSH) ; Reward (MeSH) ; Neurons: physiology (MeSH) ; Learning: physiology (MeSH) ; Mice, Inbred C57BL (MeSH)

Classification:

ddc:500

Contributing Institute(s):

Neural Circuit Computations (AG Gründemann)

Research Program(s):

351 - Brain Function (POF4-351) (POF4-351)

Appears in the scientific report 2024

Database coverage:
Medline

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