Simulating large networks of spiking neurons is a very common task in the areas of Neuroinformatics and Computational Neurosciences. These simulations are time-consuming but also often intrinsically parallel. The recent advent of powerful and programmable graphic cards seems to be a pertinent solution to the problem: they offer a cheap but efficient possibility to serve as very fast co-processors for the parallel computing that spiking neural networks need. We describe our implementation of three different problems on such a card: two image-segmentation algorithms using spiking neural networks and one multi-purpose spiking neural-network simulator. Using these examples we show the benefits, the challenges and the limits of such an implementation.