Spiking neural networks (SNNs), which are regarded as the third generation of neural networks, have attracted significant attention due to their promising applications in various scenarios. Based on SNNs, neuromorphic coprocessors, designed to emulate the structure and functionality of biological brains, hold the potential to revolutionize computing. However, these coprocessors encounter challenges related to adaptability and flexibility in various application environments once they are manufactured. To tackle this challenge, our project introduces a neuromorphic System-on-Chip (SoC), which seamlessly integrates a RISC-V CPU with an SNN coprocessor, utilizing sparse time-to-first-spike encoding (TTFS). The primary goal of this SoC is to facilitate the complete reconfigurability of the SNN coprocessor with the RISC-V CPU. By leveraging this neuromorphic SoC and successfully simulating the novel loop learning work model to achieve an accuracy of 92.2\% on the MNIST dataset, we demonstrate its capability to adapt the SNN coprocessor for various application scenarios, such as text recognition and face detection.