Identification of Shape by a Charge Simulation Retina Model

Abstract A model of a charge simulation retina was developed to distinguish different shapes and sizes. Sensory cells were evenly distributed on the retina to acquire the image information while work cells were appropriately located in the retina to yield distinct output signals. Signals at the work cells were computed based on images generated by different 3-D shapes which were arbitrarily located in the retina’s vision field, and the stimuli applied on the sensory cells. Using neural networks and based on the signals, overall classification rates of 73% for both shape and size were obtained. Hence, it is feasible for the retina to identify different 3-D shapes and sizes, in spite of arbitrarily locating the shapes in the retina’s vision field.