<p>Event-based imaging is at the forefront of high-speed sensing applications due to the low latency of asynchronous detection and low data volume. Conventionally, events used in this form of sensing correspond to changes in intensity on the microsecond scale, this inherently makes the approach incompatible with scenarios where single photon detection is required such as single photon Light Detection and Ranging (LiDAR), and low-light-level imaging. Here we propose a new imaging modality which is driven instead by events generated from the detection of individual photons. We use a form of single-pixel imaging in which information from a 3-Dimensional scene is encoded entirely in the time-of-arrival of the photons such that each detected photon can be used to update an estimation of all transverse positions simultaneously. The image reconstruction is performed by a Spiking Convolutional Neural Network (SCNN) which has a natural complementarity with single photon detection that allows the scheme to run fully asynchronously and be driven by the detection of each individual photon. Our approach for processing LiDAR information asynchronously, driven by each detected photon, has the potential for minimal latency 3D imaging and sensing even in weak light conditions with applications in high speed target detection and robotics.</p>