Avoiding looming objects (possible predators) is essential for animals'survival. This article presents a neural network model to account for the detection of and response to a looming stimulus. The generation of an appropriate response includes five tasks: detection of a looming stimulus, localization of the stimulus position, computation of the direction of the stimulus movement, determination of escape direction, and selection of a proper motor action. The detection of a looming stimulus is achieved based on the expansion of the retinal image and depth information. The spatial location of the stimulus is encoded by a population of neurons. The direction of the looming stimulus is computed by monitoring the shift of the peak of neuronal activity in this population. The signal encoding the stimulus location is gated by the direction- selective neurons onto a motor heading map, which specifies the escape direction. The selection of a proper action is achieved through competition among different groups of motor neurons. The model is based on the analysis of predator-avoidance in frog and toad but leads to a comparative analysis of mammalian visual systems.
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