Understanding the Role of Serotonin in Basal Ganglia through a Unified Model

  • Balasubramani Pragathi Priyadharsini
  • Balaraman Ravindran
  • V. Srinivasa Chakravarthy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7552)


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.


Serotonin Dopamine Reinforcement Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Balasubramani Pragathi Priyadharsini
    • 1
  • Balaraman Ravindran
    • 2
  • V. Srinivasa Chakravarthy
    • 1
  1. 1.Department of BiotechnologyIndian Institute of Technology, MadrasChennaiIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology, MadrasChennaiIndia

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