Spiking ANNs are interesting precisely because they sit at the intersection of neural-network engineering, computational models, and biologically inspired approaches to information processing.
A separate research challenge here is the training methods. Many familiar techniques from the standard deep learning world do not transfer directly, so architectural design and training strategy have to be discussed as a single block.
This publication complements the more general work on spiking neural networks and shows a move from a survey-level overview to a more structured discussion of specific architectures and learning methods.