Neuromorphic processing architectures seek to emulate the functionality of the brain by realizing parallel, efficient, event-based processing which can be directly applied to solve many of pressing problems within artificial intelligence and big data. However, implementation of these systems leads to slow response times or highly-varied output behavior. In this paper, an analog cellular neural network processing element is demonstrated to perform asynchronous spatiotemporal filtering operations in an area and power efficient manner. It utilizes a pair of analog memories to encode spike timings and perform event-based bandpass temporal processing. Information from the local clique of temporal filters is leveraged by a parallel, spatial processor which maps CNN arithmetic to the current-domain for compact computation. Preliminary circuit verification demonstrated the ability of the element to perform spatiotemporal filtering operations with latencies less than 1.8us while only consuming 1.6pJ/spike.