Wireless brain-inspired computing for online learning applications <Abstract> Wireless Brain-Inspired Computing (WiBIC) has been proposed as a neuromorphic AIoT(AI + IoT) platform that integrates wireless communication and spiking neural computation in a distributed, serverless manner. While prior studies have demonstrated the feasibility of WiBIC through simple single-task examples, its fundamental capability as a practical AIoT platform has not yet been fully established. This paper evaluates the foundational capabilities and practical viability of WiBIC through the development of two representative systems. First, an operant conditioning learning system is implemented to demonstrate reward-driven behavioral adaptation via distributed reinforcement learning without centralized control. Second, an in-room occupancy estimation system is developed, in which multiple ambient sensorsilluminance, human-detection, and current-are treated as multimodal inputs. Using reservoir computing with delay adaptation, the system performs privacy-preserving, serverless edge inference to estimate occupancy levels. Through these systems, we demonstrate that WiBIC can consistently unify computation and communication under spike-based processing, enabling distributed learning and inference on AIoT devices. The results provide concrete evidence that WiBIC possesses the fundamental capabilities required of a practical neuromorphic AIoT platform |