Presentation 2003/12/1
Stochastic Action Selection in Reinforcement Learning Model of Basal Ganglia
Kazuhiro MATSUYAMA, Kazuyuki SAMEJIMA, Kenji DOYA,
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Abstract(in English) In this paper, we proposed a computational model about how stochastic action selection is realized in the output nuclei of the basal ganglia, more specifically, substantia nigra pars reticulata(SNr) and internal globus pallidus(Gpi). In order to verify our model, we simulated a firing rate model of action selection given stochastic rewards. Our model could acquire proper actions according to changes in the probability of reward. Furthermore, the simulation suggested that action selection becomes more greedy with an increased activity of the subthalamic nuclei.
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Keyword(in English) reinforcement learning / basal ganglia stchastic action selection / globus pallidus internal / substantia nicrra nars rptiriilata / BG-Thalamir loop
Paper # NC2003-100
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Committee NC
Conference Date 2003/12/1(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Stochastic Action Selection in Reinforcement Learning Model of Basal Ganglia
Sub Title (in English)
Keyword(1) reinforcement learning
Keyword(2) basal ganglia stchastic action selection
Keyword(3) globus pallidus internal
Keyword(4) substantia nicrra nars rptiriilata
Keyword(5) BG-Thalamir loop
1st Author's Name Kazuhiro MATSUYAMA
1st Author's Affiliation Nara Institute of Science and Technology : ATR, Computational Neuroscience Laboratories : Creating the Brain, CREST, Japan Science and Technology Corporation()
2nd Author's Name Kazuyuki SAMEJIMA
2nd Author's Affiliation ATR, Computational Neuroscience Laboratories : Creating the Brain, CREST, Japan Science and Technology Corporation
3rd Author's Name Kenji DOYA
3rd Author's Affiliation Nara Institute of Science and Technology : ATR, Computational Neuroscience Laboratories : Creating the Brain, CREST, Japan Science and Technology Corporation
Date 2003/12/1
Paper # NC2003-100
Volume (vol) vol.103
Number (no) 490
Page pp.pp.-
#Pages 6
Date of Issue