Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning

DOI10.1016/j.neunet.2006.06.007zbMath1100.92013OpenAlexW2046814205WikidataQ48417235 ScholiaQ48417235MaRDI QIDQ853317

Masahiko Haruno, Mitsuo Kawato

Publication date: 15 November 2006

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2006.06.007

zbMATH Keywords

reinforcement learninginformation integrationdopaminecortico-striatal loopmodel-based neuroimagingPPTNreward predictionstimulus-action-reward association

Mathematics Subject Classification ID

Neural biology (92C20) Cognitive psychology (91E10) Memory and learning in psychology (91E40)

Related Items (4)

MOSAIC for Multiple-Reward Environments ⋮ Modeling nucleus accumbens. A computational model from single cell to circuit level ⋮ DAPath: distance-aware knowledge graph reasoning based on deep reinforcement learning ⋮ FLOW SHOP SCHEDULING WITH REINFORCEMENT LEARNING

Cites Work

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• Recursive estimation and time-series analysis. An introduction
• MOSAIC Model for Sensorimotor Learning and Control
• Using Expectation-Maximization for Reinforcement Learning

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