The promise of Artificial Neural Networks has lead to their immense usage intertwined with concerns over energy consumption. This has led to development of alternatives, such as Spiking Neural Networks (SNNs), which allows their implementation on neuromorphic hardware. In effect, the network must be time discretized. SNNs learn through backpropagation, similarly to ANNs, where two main methods exist: spike-based backpropagation and backpropagation through time. This study investigates and compares the effect of varying timesteps on the convergence rate of BATS, an example of spikebased backpropagation, and of SLAYER - an example of backpropagation through time on the NMNIST and MNIST dataset. The results conclude that BATS withstands growing timestep sizes. Although SLAYER maintains a good performance for small timestep sizes, it seems to self-destruct as they grow.