Identifying interesting or salient regions in an image plays an important role for multimedia search, object tracking, active vision, segmentation, and classification. Existing saliency extraction algorithms are implemented using the conventional von Neumann computational model. We propose a bottom-up model of visual saliency, inspired by the primate visual cortex, which is compatible with TrueNorth-a low-power, brain-inspired neuromorphic substrate that runs large-scale spiking neural networks in real-time. Our model uses color, motion, luminance, and shape to identify salient regions in video sequences. For a three-color-channel video with 240 × 136 pixels per frame and 30 frames per second, we demonstrate a model utilizing ∼ 3 million neurons, which achieves competitive detection performance on a publicly available dataset while consuming ∼ 200 mW.