Several stochastic models, with various degrees of complexity, have been proposed to model the neuronal activity from different parts of the human brain. In this paper, we use an Ornstein-Uhlenbeck Process (OUP) to model the spike activity recorded from the thalamus of a patient suffering from Essential Tremor at the time of implantation of the electrodes for Deep Brain Stimulation. From the recorded data, which contains information about the spike times of a single neuron, we identify the model parameters of the OUP.We then use these parameters to numerically simulate the inter-spike interval distribution. We show that the OUP provides excellent fits to the data recorded both without any external stimulation as well as with stimulation. We finally compare the fits with other stochastic models commonly used and we show the superiority of the OUP model in general.
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