Energy efficient implementations and deployments of Spiking neural networks (SNNs) have been of interest due to the possibility of developing artificial systems that can achieve the computational powers and energy efficiency of the brain. Two techniques employed in the efficient deployment of DNNs -- the quantization and pruning of parameters, can both compress the model size, reduce memory footprints, and facilitate low-latency execution. The interaction between quantization and pruning and how they might impact model performance on SNN accelerators is currently unknown. We study various combinations of pruning and quantization in isolation, cumulatively, and simultaneously (jointly) to a state-of-the-art SNN.