Predictive learning shapes the representational geometry of the human brain.

Autor: Greco A; Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany. antonino.greco@uni-tuebingen.de.; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany. antonino.greco@uni-tuebingen.de.; MEG Center, University of Tübingen, Tübingen, Germany. antonino.greco@uni-tuebingen.de., Moser J; IDM/fMEG Center of the Helmholtz Center Munich, University of Tübingen, Tübingen, Germany.; Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, USA., Preissl H; IDM/fMEG Center of the Helmholtz Center Munich, University of Tübingen, Tübingen, Germany.; German Center for Mental Health (DZPG), Tübingen, Germany.; German Center for Diabetes Research (DZD), Tübingen, Germany.; Department of Internal Medicine IV, University Hospital of Tübingen, Tübingen, Germany.; Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany., Siegel M; Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany. markus.siegel@uni-tuebingen.de.; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany. markus.siegel@uni-tuebingen.de.; MEG Center, University of Tübingen, Tübingen, Germany. markus.siegel@uni-tuebingen.de.; German Center for Mental Health (DZPG), Tübingen, Germany. markus.siegel@uni-tuebingen.de.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Nov 08; Vol. 15 (1), pp. 9670. Date of Electronic Publication: 2024 Nov 08.
DOI: 10.1038/s41467-024-54032-4
Abstrakt: Predictive coding theories propose that the brain constantly updates internal models to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link prediction error encoding and optimization of sensory representations remain unclear. Here, we provide evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in humans listening to acoustic sequences with different levels of regularity. We found that the brain aligns its representational geometry to match the statistical structure of the sensory inputs, by clustering temporally contiguous and predictable stimuli. Crucially, the magnitude of this representational shift correlates with the synergistic encoding of prediction errors in a network of high-level and sensory areas. Our findings suggest that, in response to the statistical regularities of the environment, large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas to enhance sensory processing.
(© 2024. The Author(s).)
Databáze: MEDLINE
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