Reconfigurable devices enable adaptive neuromorphic computing by dynamically allocating circuit resources. However, integrating diverse functionalities with ultralow energy consumption in a single device remains challenging. Here, we demonstrate reconfigurable zinc phosphorus trisulfide (ZnPS₃) memristors that exhibit both volatile and non-volatile switching with superior performance metrics, including a low switching voltage (~0.180 V), minimal energy consumption (143 aJ per volatile switching), high on/off ratio (10⁷), and 256 distinct conductive states, ideal for implementing adaptive neuromorphic computing. These ZnPS₃ memristors can be reconfigured using a single electrical pulse, allowing for on-demand emulation of neuron-like temporal dynamics and synapse-like weight memorization. Leveraging these device characteristics, we developed a reservoir computing network that integrates dynamic physical reservoirs with steady-weighted readouts, successfully achieving 99% accuracy in electrocardiogram classification. Our findings highlight the potential of ZnPS₃-based adaptive neuromorphic computing for energy-efficient spatiotemporal signal processing and recognition, advancing the development of ultralow-energy brain-inspired computing systems.