Modeling serotonin’s contributions to basal ganglia dynamics

Pragathi Priyadharsini Balasubramani, V. Srinivasa Chakravarthy*, Balaraman Ravindran, Ahmed A. Moustafa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

2 Citations (Scopus)


In addition to dopaminergic input, serotonergic (5-HT) fibers also widely arborize through the basal ganglia circuits and strongly control their dynamics. Although empirical studies show that 5-HT plays many functional roles in risk-based decision making, reward, and punishment learning, prior computational models mostly focus on its role in behavioral inhibition or timescale of prediction. This chapter presents an extended reinforcement learning (RL)-based model of DA and 5-HT function in the BG, which reconciles some of the diverse roles of 5-HT. The model uses the concept of utility function—a weighted sum of the traditional value function expressing the expected sum of the rewards, and a risk function expressing the variance observed in reward outcomes. Serotonin is represented by a weight parameter, used in this combination of value and risk functions, while the neuromodulator dopamine (DA) is represented as reward prediction error as in the classical models. Consistent with this abstract model, a network model is also presented in which medium spiny neurons (MSN) co-expressing both D1 and D2 receptors (D1R–D2R) is suggested to compute risk, while those expressing only D1 receptors are suggested to compute value. This BG model includes nuclei such as striatum, Globus Pallidus externa, Globus Pallidus interna, and subthalamic nuclei. DA and 5-HT are modeled to affect both the direct pathway (DP) and the indirect pathway (IP) composing of D1R, D2R, D1R–D2R projections differentially. Both abstract and network models are applied to data from different experimental paradigms used to study the role of 5-HT: (1) risk-sensitive decision making, where 5-HT controls the risk sensitivity; (2) temporal reward prediction, where 5-HT controls timescale of reward prediction, and (3) reward–punishment sensitivity, where punishment prediction error depends on 5-HT levels. Both the extended RL model (Balasubramani, Chakravarthy, Ravindran, & Moustafa, in Front Comput Neurosci 8:47, 2014; Balasubramani, Ravindran, & Chakravarthy, in Understanding the role of serotonin in basal ganglia through a unified model, 2012) along with their network correlates (Balasubramani, Chakravarthy, Ravindran, & Moustafa, in Front Comput Neurosci 9:76, 2015; Balasubramani, Chakravarthy, Ali, Ravindran, & Moustafa, in PLoS ONE 10(6):e0127542, 2015) successfully explain the three diverse roles of 5-HT in a single framework.

Original languageEnglish
Title of host publicationComputational Neuroscience Models of the Basal Ganglia
EditorsV. Srinivasa Chakravarthy, Ahmed A. Moustafa
Number of pages29
ISBN (Electronic)978-981-10-8494-2
ISBN (Print)978-981-13-4168-7, 978-981-10-8493-5
Publication statusPublished - 2018
Externally publishedYes

Publication series

NameCognitive Science and Technology
ISSN (Print)2195-3988
ISSN (Electronic)2195-3996


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