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Understanding the role of serotonin in basal ganglia through a unified model
Balasubramani Pragathi Priyadharsini, V. Srinivasa Chakravarthy,
Published in
2012
Volume: 7552 LNCS
   
Issue: PART 1
Pages: 467 - 473
Abstract
We present a Reinforcement Learning (RL)-based model of serotonin which tries to reconcile some of the diverse roles of the neuromodulator. The proposed model uses a novel formulation of utility function, which is a weighted sum of the traditional value function and the risk function. Serotonin is represented by the weightage, α, used in this combination. The model is applied to three different experimental paradigms: 1) bee foraging behavior, which involves decision making based on risk, 2) temporal reward prediction task, in which serotonin (α) controls the time-scale of reward prediction, and 3) reward/punishment prediction task, in which punishment prediction error depends on serotonin levels. The three diverse roles of serotonin - in time-scale of reward prediction, risk modeling, and punishment prediction - is explained within a single framework by the model. © 2012 Springer-Verlag.
About the journal
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN03029743
Open AccessNo
Concepts (16)
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    BASAL GANGLIA
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    Dopamine
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    FORAGING BEHAVIORS
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    PREDICTION ERRORS
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    PREDICTION TASKS
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    RISK FUNCTION
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    RISK MODELING
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    Serotonin
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    Time-scales
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    Traditional values
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    UNIFIED MODEL
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    Utility functions
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    Weighted sum
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    Neural networks
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    Reinforcement learning
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    Forecasting