Three different neuromorphic vision sensors; a 2D smooth optical flow sensor, a 1D tracker sensor and a 1D motion sensor are modeled in a simulator. The sensors are modeled according to their spatio-temporal properties and the model is validated with experimental data obtained in previous work. The model parameters are fitted according to validation data, where a cost function is optimized with a gradient descent approach. The simulator used is the Open Dynamics Engine (ODE) and the experimental data from previous work was collected in a typical RoboCup scenario. A soccer playing robot is here used to provide reference information from a standard digital tracker system. By modeling the output from neuromorphic vision sensors, it is possible to reconstruct the experiment in the simulator. We will present simulated data from this particular experiment and compare it to the ground truth validation data. This work is important in order to reduce the time needed for real-world experiments for the set-up and optimization phase during the integration of such sensors into robotic systems. At the end we will also make an indication of the direction of future research.