Transparent motion is perceived when multiple motions are presented in the same part of visual space that move in different directions or with different speeds. Several psychophysical as well as physiological experiments have studied the conditions under which motion transparency occurs. Few computational mechanisms have been proposed that allow to segregate multiple motions. We present a novel neural model which investigates the necessary mechanisms underlying initial motion detection, the required representations for velocity coding, and the integration and segregation of motion stimuli to account for the perception of transparent motion. The model extends a previously developed architecture for neural computations along the dorsal pathway, particularly, in cortical areas V1, MT, and MSTd. It emphasizes the role of feedforward cascade processing and feedback from higher to earlier processing stages for selective feature enhancement and tuning. Our results demonstrate that the model reproduces several key psychophysical findings in perceptual motion transparency using random dot stimuli. Moreover, the model is able to process transparent motion as well as opaque surface motion in real-world sequences of 3-d scenes. As a main thesis, we argue that the perception of transparent motion relies on the representation of multiple velocities at one spatial location; however, this feature is necessary but not sufficient to perceive transparency. It is suggested that the activations simultaneously representing multiple activities are subsequently integrated by separate mechanisms leading to the segregation of different overlapping segments.