The first computations of any motion analysis involve image motion estimation, segmentation on the basis of movement, and 3D motion estimation. In previous studies, we have proposed hypotheses on basic computational principles in human motion estimation, which can be observed in optical illusions - patterns that cause misestimation, because something goes wrong. Examples of such computational principles are statistical bias in the estimation or causal filtering for estimating temporal derivatives (i.e. to compute changes in time, biology can only use the signal from the present and the past, but not the future). In this project, we seek to explore these principles and look at the properties a neural network would need to replicate the perception in illusions. We also will explore the role of the transient signal (that is only changes are recorded) for the early motion processes. Using the DVS, we have available a technical tool to simulate computations.