Abstract

All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction and rotational motion. In biological vision systems, scientists have found specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perception, however, little has been done in the past to create computational models for rotation motion perception. To fill this gap, we proposed a neural network which utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited directional selective neural networks for rotational motion perception. The proposed neural network consists of two parts - presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited directional selective neural networks to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these directional selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each directional selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise or counterclockwise, to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting clockwise or counterclockwise rotational motion. This research is a critical step further towards dynamic visual information processing.