Abstract
Using the spiking Izhikevich model used in the earlier chapters, we studied the effect of medication [L-Dopa and dopamine agonists (DAA)] and subthalamic nucleus (STN) deep brain stimulation on decision making using two cognitive tasks, i.e., Iowa gambling task (IGT) and the probabilistic learning task (PLT) and were validated using the experimental results. Based on the experimental observations that dopaminergic activity is analogous to temporal difference (TD) and induces cortico-striatal plasticity, we introduced learning in the cortico-striatal weights using the reinforcement learning framework. For PLT and IGT, the model in PD condition under medication (L-Dopa) was unable to learn from punishments which is attributed to excess dopamine levels in striatum even during punishment. The model under DAA was impulsive reflected in the lower RT in PLT and negative score in IGT. We varied two parameters during DBS (1) the electrode position within STN and (2) antidromic activation of GPe neurons. The performance in both IGT (Score) and PLT (reaction time) was dependent on the position of the electrode and amplitude of the current for a specific electrode position. We also observed that a higher antidromic activation of GPe neurons does not impact the learning ability but decreases reaction time as reported in DBS patients for PLT. These results suggest a probable role of electrode and antidromic activation in modulating the STN activity and eventually affecting the patient’s performance.
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Mandali, A., Srinivasa Chakravarthy, V. (2018). Studying the Effect of Dopaminergic Medication and STN–DBS on Cognitive Function Using a Spiking Basal Ganglia Model. In: Computational Neuroscience Models of the Basal Ganglia. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-8494-2_11
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DOI: https://doi.org/10.1007/978-981-10-8494-2_11
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