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Augmenting the Bayesian Brain with learned and reusable world-model components for flexible cognition

2026-05-08

Abstract excerpt

The Bayesian Brain hypothesis assumes that cognition relies on internal generative models of the world, yet existing implementations remain constrained by pre-specified, task-specific generative structures and computationally heavy iterative inference schemes. Here, we introduce modular neural state-space models as a scalable realization of the Bayesian Brain, replacing fixed generative structures and pre-specifie...

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Literature Corpus work
abec35fc-3e8f-5d82-a4b3-0440c7a101c5
DOI
10.64898/2026.05.06.722922
Open publication

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Augmenting the Bayesian Brain with learned and reusable world-model components for flexible cognitionDOI 10.64898/2026.05.06.722922
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