Parkinson's Disease (PD) impacts millions globally, causing debilitating motor symptoms. While Closed-Loop Deep Brain Stimulation (CL-DBS) has emerged as a promising treatment, existing systems often suffer from high energy consumption, making them impractical for wearable or implantable devices. This research introduces an innovative neuromorphic approach to enhance CL-DBS performance, utilizing Leaky Integrate-and-Fire (LIF) neuron-based controllers to adaptively modulate stimulation signals based on symptom severity. Two controller designs, the on-off LIF and dual LIF models, are proposed, achieving significant reductions in power consumption by 19% and 56%, respectively, while enhancing suppression efficiency by 4.7% and 6.77%. Additionally, this work addresses the scarcity of datasets for PD symptoms by developing a novel dataset featuring neural activity from the subthalamic nucleus (STN), incorporating beta oscillations as key physiological biomarkers. This dataset aims to support further advancements in neuromorphic CL-DBS systems and is openly shared with the research community. By combining energy-efficient neuromorphic controllers with a comprehensive dataset, this study not only advances the technological feasibility of CL-DBS systems for PD treatment but also provides a foundation for personalized and adaptive neuromodulation therapies, paving the way for improved quality of life for individuals with Parkinson's Disease.