A mathematical model is formulated to examine the stochastic synaptic noise dynamics within subthalamic neuron electrophysiology, particularly concerning Parkinson's disease. By simulating the stochastic processes underlying synaptic noise, the model serves as a tool to explore the intricate dynamics of subthalamic neurons, contributing to the elucidation of Parkinson's disease mechanisms. Utilizing the stochastic Ornstein–Uhlenbeck process, we represent excitatory synaptic conductance and integrate it into a comprehensive whole-cell model to generate spontaneous and evoked cellular electrical activities. This single-cell model incorporates numerous biophysically detailed ion channels, depicted by a set of ordinary differential equations in Hodgkin–Huxley and Markov formalisms. Consequently, this approach effectively induces irregular spontaneous depolarizations (SDs) and spontaneous action potentials (sAPs), mirroring electrical activity observed in vitro. The model reveals that alterations in the ability to reach the action potential threshold are observed, alongside significant decreases in input resistance and increased firing rates of spontaneous action potentials. Additionally, background synaptic activity can modify the input/output characteristics of nonneuronal excitatory cells, providing further insights into the role of synaptic noise in modulating neuronal behavior. These findings are critical, as the subthalamic nucleus (STN) plays a central role in the motor circuitry affected by Parkinson's disease, and its abnormal activity is a hallmark of the disease. The stochastic model serves as a robust platform for simulating disease conditions and testing potential interventions, ultimately contributing to a better understanding of how synaptic noise influences neuron behavior and the pathophysiology of Parkinson's disease.