Bio-inspired visual neural network on spatio-temporal depth rotation perception

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In primates’ cerebral cortex, depth rotation sensitive (DRS) neurons have the property of preferential selectivity for depth rotation motion, whereas such a property is rarely adopted to create computational models for depth rotation motion detection. To fill this gap, a novel feedforward visual neural network is developed to execute depth rotation object detection, based on the recent neurophysiologic achievements on the mammalian vision system. The proposed neural network consists of two parts, i.e., presynaptic and postsynaptic neural networks. The former comprises multiple lateral inhibition neural sub-networks for the capture of visual motion information, and the latter extracts the cues of translational and depth motion and later, synthesizes such clues to perceive the process of depth rotation of an object. Experimentally, the neural network is sufficiently examined by different types of depth rotation under multiple conditions and settings. Numerical experiments show that not only it can effectively detect the spatio-temporal energy change of depth rotation of a moving object, but also its output excitation curve is a quasi-sinusoidal one, which is compatible with the hypothesis suggested by Johansson and Jansson in projective geometry. This research is a critical step toward the construction of artificial vision system for depth rotation object recognition.