V Srinivasa Chakravarthy

Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG. © 2014 Balasubramani, Chakravarthy, Ravindran and Moustafa.

We model the role played by the Basal Ganglia (BG) in the generation of voluntary saccadic eye movements. The BG model explicitly represents key nuclei like the striatum (caudate), Substantia Nigra pars reticulata (SNr) and compata (SNc), the Subthalamic Nucleus (STN), the two pallidal nuclei and Superior Colliculus. The model is cast within the Reinforcement Learning (RL) framework, with the dopamine representing the temporal difference error, the striatum serving as the critic, and the indirect pathway playing the role of the explorer. Performance of the model is evaluated on a set of tasks such as feature and conjunction searches, directional selectivity and a successive saccade task. Behavioral phenomena such as independence of search time on number of distractors in feature search and linear increase in search time with number of distractors in conjunction search are observed. It is also seen that saccadic reaction times are longer and search efficiency is impaired on diminished BG contribution, which corroborates with reported data obtained from Parkinson's Disease (PD) patients. © 2011 Elsevier Ltd.

Basal ganglia (BG) constitute a network of seven deep brain nuclei involved in a variety of crucial brain functions including: action selection, action gating, reward based learning, motor preparation, timing, etc. In spite of the immense amount of data available today, researchers continue to wonder how a single deep brain circuit performs such a bewildering range of functions. Computational models of BG have focused on individual functions and fail to give an integrative picture of BG function. A major breakthrough in our understanding of BG function is perhaps the insight that activities of mesencephalic dopaminergic cells represent some form of 'reward' to the organism. This insight enabled application of tools from 'reinforcement learning,' a branch of machine learning, in the study of BG function. Nevertheless, in spite of these bright spots, we are far from the goal of arriving at a comprehensive understanding of these 'mysterious nuclei.' A comprehensive knowledge of BG function has the potential to radically alter treatment and management of a variety of BG-related neurological disorders (Parkinson's disease, Huntington's chorea, etc.) and neuropsychiatric disorders (schizophrenia, obsessive compulsive disorder, etc.) also. In this article, we review the existing modeling literature on BG and hypothesize an integrative picture of the function of these nuclei. © 2010 Springer-Verlag.

We present a model of basal ganglia (BG) that departs from the classical Go/NoGo picture of the function of its key pathways-the Direct and Indirect Pathways (DP and IP). Between the Go and NoGo regimes, we posit a third Explore regime, which denotes random exploration of action alternatives. Striatal dopamine (DA) is assumed to switch between DP and IP activation. The IP is modeled as a loop of the subthalamic nucleus (STN) and the Globus Pallidus externa (GPe). Simulations reveal that while the model displays Go and NoGo regimes for extreme values of DA, at intermediate values of DA, it exhibits exploratory behavior, which originates from the chaotic activity of the STN-GPe loop. We describe a series of BG models based on Go/Explore/NoGo approach, to explain the role of BG in three cases: (1) a simple action selection task, (2) reaching, and (3) willed action. © 2013 Elsevier B.V.

In neuroscience literature, dopamine is often considered as a pleasure chemical of the brain. Dopaminergic neurons respond to rewarding stimuli which include primary rewards like opioids or food, or more abstract forms of reward like cash rewards or pictures of pretty faces. It is this reward-related aspect of dopamine, particularly its association with reward prediction error, that is highlighted by a large class of computational models of dopamine signaling. Dopamine is also a neuromodulator, controlling synaptic plasticity in several cortical and subcortical areas. But dopamine's influence is not limited to the nervous system; its effects are also found in other physiological systems, particularly the circulatory system. Importantly, dopamine agonists have been used as a drug to control blood pressure. Is there a theoretical, conceptual connection that reconciles dopamine's effects in the nervous system with those in the circulatory system? This perspective article integrates the diverse physiological roles of dopamine and provides a simple theoretical framework arguing that its reward related function regulates the processes of energy consumption and acquisition in the body. We conclude by suggesting that energy-related book-keeping of the body at the physiological level is the common motif that links the many facets of dopamine and its functions.

There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making (Balasubramani et al., 2014). 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 (RL)-based model of the BG that simulates the interactions between dopamine (DA) and serotonin (5HT) in a diverse set of experimental studies including reward, punishment and risk based decision making (Balasubramani et al., 2014). Starting with the classical idea that the activity of mesencephalic 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 the neural substrates for value computation, we propose that DA D1R and D2R co-expressing MSNs are capable of computing risk. Though the existence of MSNs that co-express D1R and D2R are reported by various experimental studies, prior existing computational models did not include them. Ours is the first model that accounts for the computational possibilities of these co-expressing D1R-D2R MSNs, and describes how DA and 5HT mediate activity in these classes of neurons (D1R-, D2R-, D1R-D2R- MSNs). Starting from the assumption that 5HT modulates all MSNs, our study predicts significant modulatory effects of 5HT on D2R and co-expressing D1R-D2R MSNs which in turn explains the multifarious functions of 5HT in the BG. The experiments simulated in the present study relates 5HT to risk sensitivity and reward-punishment learning. Furthermore, our model is shown to capture reward-punishment and risk based decision making impairment in Parkinson's Disease (PD). The model predicts that optimizing 5HT levels along with DA medications might be essential for improving the patients' reward-punishment learning deficits.