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There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making as well. Despite this evidence, little is known about the computational principles and neural correlates of risk computation in this subcortical system. We have previously proposed a reinforcement learning based model of the BG that simulates the interactions between dopamine (DA) and serotonin (5HT) in a diverse set of experimental effects including reward, punishment and risk based decision making. Starting with the idea that the activity of DA represents reward prediction error, the model posits that serotoninergic activity in the striatum controls risk-prediction error. Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data. In this work, we expand the earlier model into a detailed network model of the BG and demonstrate the joint contributions of DA-5HT in risk and reward-punishment sensitivity. At the core of the proposed network model is the following insight regarding cellular correlates of value and risk computation. Just as DA D1 receptor (D1R) expressing medium spiny neurons (MSNs) of the striatum were thought to be neural substrates for value computation, we propose that DA D1R and D2R co-expressing MSNs, reported to occupy a significant proportion of the striatum and are implicated in disorders like schizophrenia and drug addiction, are capable of computing risk. Ours is the first-of-its-kind model that accounts for the significant computational possibilities of these co-expressing D1R-D2R MSNs, and describes how DA-5HT mediated activity in these classes of neurons (D1R-, D2R-, D1R-D2R- MSNs) contribute to the BG dynamics. We also apply the model to capture the behaviour of PD patients in a probabilistic learning paradigm. The study observes that optimizing 5HT levels along with DA medication could be essential to improving the patients' learning