Recent advances in spiking neural networks (SNNs), standing as the next generation of artificial neural networks, have demonstrated clear computational benefits over traditional frame- or image-based neural networks. In contrast to more traditional artificial neural networks (ANNs), SNNs propagate spikes, i.e., sparse binary signals, in an asynchronous fashion. Using more sophisticated neuron models, such brain-inspired architectures can in principle offer more efficient and compact processing pipelines, leading to faster decision-making using low computational and power resources, thanks to the sparse nature of the spikes. A promising research avenue is the combination of SNNs with event cameras (or neuromorphic cameras), a new imaging modality enabling low-cost imaging at high speed. Event cameras are also bio-inspired sensors, recording only temporal changes in intensity. This generally reduces drastically the amount of data recorded and, in turn, can provide higher frame rates, as most static or background objects (when seen by the camera) can be discarded. Typical applications of this technology include detection and tracking of high-speed objects, surveillance, and imaging and sensing from highly dynamic platforms.