The aperture problem is a direct consequence of any local detection in the visual perception of motion. It results in ambiguous responses of the local motion detectors. Biological systems, such as the brain of different mammals, are able to disambiguate motion detection. Such disambiguation is usually seen as a possible result of a pyramidal feedforward processing with growing receptive fields, but this approach is not able to detect motion in a simultaneously unambiguous and precise way. In this work we define a neural model of motion disambiguation that achieves both criteria, mainly with the help of excitatory feedback. Our model mostly differs from previous ones by incorporating lateral inhibition. Its main advantages are: tolerance to noise and stability. We perform tests on synthetic image sequences that show the effectiveness of our approach.