ion migration induce a phase transition, leading to sharp resistance switching and efficient spiking. This device successfully mimics key neuronal dynamics, including leaky integrate-and-fire, threshold tuning, and spatiotemporal dynamics, without requiring auxiliary reset circuits. Furthermore, SNN is constructed by integrating CIPS-based synaptic and neuron devices and evaluate face classification performance using an unsupervised learning approach, achieving a recognition accuracy of 95.83% via the lateral inhibition function of the neuron device. The findings highlight the potential of CIPS TS-FET as energy-efficient spiking neuron device applications for next-generation SNN-based neuromorphic computing systems.