The human visual system uses two parallel pathways for spatial processing and object recognition. In contrast, computer vision systems tend to use a single feedforward pathway, rendering them less robust, adaptive, or efficient than human vision. To bridge this gap, we developed a dual-stream vision model inspired by the human eyes and brain. At the input level, the model samples two complementary visual patterns to mimic how the human eyes use magnocellular and parvocellular retinal ganglion cells to separate retinal inputs to the brain. At the backend, the model processes the separate input patterns through two branches of convolutional neural networks (CNN) to mimic how the human brain uses the dorsal and ventral cortical pathways for parallel visual processing. The first branch (WhereCNN) samples a global view to learn spatial attention and control eye movements. The second branch (WhatCNN) samples a local view to represent the object around the fixation. Over time, the two branches interact recurrently to build a scene representation from moving fixations. When compared to brain responses in humans watching a movie, the WhereCNN and WhatCNN branches matched the dorsal and ventral pathways of the visual cortex, respectively. We also conducted experiments to disentangle the roles of retinal sampling, learning objective, and attention-guided eye movement in the functional alignment between the model and the brain. Results suggest that the functional segregation of the brain's dorsal and ventral visual pathways is driven primarily by their different learning objectives - spatial attention vs. object recognition, secondarily by the sampling biases of their retinal inputs. This dual-stream model takes a further step in brain-inspired computer vision, enabling parallel neural networks to actively explore and understand the visual surroundings.