Computational Modelling of Neurotransmitter Mediated Motor Learning in Basal Ganglia
Neurotransmitters are chemical messengers that carry signals from one neuron to the next and even to muscles or glands. In recent years several computational and mathematical analysis of the model has been widely used to study the dysfunction of Basal Ganglia (BG). The proposed study focuses on the neural dynamics of Neurotransmitters associated with motor learning in Basal Ganglia. This study will implement an artificial Reinforcement learning model which explains the dynamic behavior of Parkinson’s Disease, a neurological condition associated with the lesion of the Basal Ganglia and thereby bridging the gap between Artificial Intelligence (AI) and Neuroscience. The demonstration of the condition of Parkinson’s Disease under the context of reinforcement learning is planned with the help of a robotic arm or a virtual robot. The Reinforcement Learning model should automatically select an indirect pathway by learning the factors causing the depletion of neurons and thus inhibiting the movements or generating motor symptoms like tremor. In addition to this, a 3-Dimensional demonstration of the excitation and inhibition of the neurons in the PD condition will be done with the help of Unity Real-time Development Platform, for better understanding.