Uncertainty estimation with prediction-error circuits

Abstract Neural circuits continuously integrate noisy sensory stimuli with predictions that often do not perfectly match, requiring the brain to combine these conflicting feedforward and feedback inputs according to their uncertainties. However, how the brain tracks both stimulus and prediction unce...

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Main Authors: Loreen Hertäg, Katharina A. Wilmes, Claudia Clopath
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-58311-6
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author Loreen Hertäg
Katharina A. Wilmes
Claudia Clopath
author_facet Loreen Hertäg
Katharina A. Wilmes
Claudia Clopath
author_sort Loreen Hertäg
collection DOAJ
description Abstract Neural circuits continuously integrate noisy sensory stimuli with predictions that often do not perfectly match, requiring the brain to combine these conflicting feedforward and feedback inputs according to their uncertainties. However, how the brain tracks both stimulus and prediction uncertainty remains unclear. Here, we show that a hierarchical prediction-error network can estimate both the sensory and prediction uncertainty with positive and negative prediction-error neurons. Consistent with prior hypotheses, we demonstrate that neural circuits rely more on predictions when sensory inputs are noisy and the environment is stable. By perturbing inhibitory interneurons within the prediction-error circuit, we reveal their role in uncertainty estimation and input weighting. Finally, we link our model to biased perception, showing how stimulus and prediction uncertainty contribute to the contraction bias.
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institution Kabale University
issn 2041-1723
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publishDate 2025-03-01
publisher Nature Portfolio
record_format Article
series Nature Communications
spelling doaj-art-233116cc57d9487b87a72cbf1facfd802025-08-20T03:41:14ZengNature PortfolioNature Communications2041-17232025-03-0116111510.1038/s41467-025-58311-6Uncertainty estimation with prediction-error circuitsLoreen Hertäg0Katharina A. Wilmes1Claudia Clopath2Modeling of Cognitive Processes, TU BerlinDepartment of Physiology, University of BernBioengineering Department, Imperial College LondonAbstract Neural circuits continuously integrate noisy sensory stimuli with predictions that often do not perfectly match, requiring the brain to combine these conflicting feedforward and feedback inputs according to their uncertainties. However, how the brain tracks both stimulus and prediction uncertainty remains unclear. Here, we show that a hierarchical prediction-error network can estimate both the sensory and prediction uncertainty with positive and negative prediction-error neurons. Consistent with prior hypotheses, we demonstrate that neural circuits rely more on predictions when sensory inputs are noisy and the environment is stable. By perturbing inhibitory interneurons within the prediction-error circuit, we reveal their role in uncertainty estimation and input weighting. Finally, we link our model to biased perception, showing how stimulus and prediction uncertainty contribute to the contraction bias.https://doi.org/10.1038/s41467-025-58311-6
spellingShingle Loreen Hertäg
Katharina A. Wilmes
Claudia Clopath
Uncertainty estimation with prediction-error circuits
Nature Communications
title Uncertainty estimation with prediction-error circuits
title_full Uncertainty estimation with prediction-error circuits
title_fullStr Uncertainty estimation with prediction-error circuits
title_full_unstemmed Uncertainty estimation with prediction-error circuits
title_short Uncertainty estimation with prediction-error circuits
title_sort uncertainty estimation with prediction error circuits
url https://doi.org/10.1038/s41467-025-58311-6
work_keys_str_mv AT loreenhertag uncertaintyestimationwithpredictionerrorcircuits
AT katharinaawilmes uncertaintyestimationwithpredictionerrorcircuits
AT claudiaclopath uncertaintyestimationwithpredictionerrorcircuits