Modeling the role of basal ganglia in saccade generation: Is the indirect pathway the explorer?

Krishnan, Ravi; Ratnadurai, Shivakeshavan; Subramanian, Deepak; Chakravarthy, V. Srinivasa; Rengaswamy, Maithreye

doi:10.1016/j.neunet.2011.06.002

Cited by 30 publications

(39 citation statements)

References 42 publications

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“…The idea that MSNs are probably cellular substrates for value computation has found its place in recent modeling literature (Morita et al, 2012). Starting from the fact that the effect of dopamine on the D1R—expressing MSNs of the striatum is to increase the firing rate, it has been shown in a computational model of the BG that the D1R-expressing MSNs are capable of computing value (Krishnan et al, 2011). We then extend this idea and show that a model of D1R-D2R co-expressing MSNs in the striatum is capable of computing the risk function in Section Cellular Correlates for the Value and the Risk Computation.…”

Section: Discussionmentioning

confidence: 99%

“…These neurons were not modeled in any of the earlier studies (Frank et al, 2004; Ashby et al, 2010; Humphries and Prescott, 2010; Krishnan et al, 2011). Such unacknowledged nature of the D1R-D2R MSNs in the striatum might be due to the following: The existence of co-expressing D1R-D2R MSNs has been debated for years.…”

Section: Discussionmentioning

confidence: 99%

“…The activity of MSNs with D1R expression (y D 1 ) are appropriately suited for value computation (Krishnan et al, 2011; Kalva et al, 2012). They express λ D 1 (δ) as an increasing function of δ.…”

Section: Modelmentioning

confidence: 99%

“…RL theory has been able to account for many crucial functions of DA in BG- mediated learning and behavior (Houk et al, 2007; Schultz, 2010a). Classical models of the BG cast their dynamics in a value function based decision making framework, where value function is the expectation of observed rewards (Joel et al, 2002; Frank et al, 2004; Krishnan et al, 2011). We showed in a recent study (Balasubramani et al, 2014) that BG dynamics can be better modeled using utility based decision making framework mediated by the neuromodulators DA and serotonin (5HT).…”

Section: Introductionmentioning

confidence: 99%

“…While the MSNs that express DA D1-receptor (D1R) compute value as earlier suggested in modeling studies (O'Doherty et al, 2004), those that co-express D1R and D2R are now shown to be capable of computing risk. No earlier computational models of the BG (Frank et al, 2004; Ashby et al, 2010; Humphries and Prescott, 2010; Krishnan et al, 2011) have taken these D1R-D2R co-expressing neurons into consideration, though their existence in the BG was shown by many experiments (Nadjar et al, 2006; Bertran-Gonzalez et al, 2010; Hasbi et al, 2010, 2011; Perreault et al, 2010, 2011; Calabresi et al, 2014). The neuromodulator DA is represented as the TD error mediating either the update of the cortico-striatal weights or the action selection dynamics occurring downstream of the striatum.…”

Section: Introductionmentioning

confidence: 99%

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A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making

Balasubramani

Chakravarthy

Ravindran

et al. 2015

Front. Comput. Neurosci.

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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 (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.

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Section: Discussionmentioning

confidence: 99%