Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making

DOI10.1016/j.neunet.2006.03.006zbMath1099.92009OpenAlexW2006414608WikidataQ42500759 ScholiaQ42500759MaRDI QIDQ853280

Michael J. Frank

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.2006.03.006

zbMATH Keywords

decision makingreinforcement learningneural network modelParkinson's diseasebasal gangliasubthalamic nucleus

Mathematics Subject Classification ID

Neural biology (92C20) Medical applications (general) (92C50) Cognitive psychology (91E10)

Related Items (5)

Rapid decision threshold modulation by reward rate in a neural network ⋮ Approaches to analysis in model-based cognitive neuroscience ⋮ A non-linear deterministic model of action selection in the basal ganglia to simulate motor fluctuations in Parkinson's disease ⋮ Integration of Reinforcement Learning and Optimal Decision-Making Theories of the Basal Ganglia ⋮ The Basal Ganglia Optimize Decision Making over General Perceptual Hypotheses

Cites Work

• Rapid decision threshold modulation by reward rate in a neural network
• Agents of the mind
• A simplified neuron model as a principal component analyzer
• How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades
• A computational model of action selection in the basal ganglia. I: A new functional anatomy
• Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning
• SIMPLE NEURAL NETWORKS THAT OPTIMIZE DECISIONS
• Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

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