How do convolutional neural networks mimic the way human eyes process visual information?

The answer

Convolutional Neural Networks (CNNs) were inspired by Hubel and Wiesel's 1959 Nobel Prize-winning research showing that neurons in a cat's visual cortex respond to specific local regions and edge orientations. Yann LeCun applied this insight in 1989 with LeNet, which used shared-weight filters that slide across images to detect features hierarchically - edges in early layers, shapes in middle layers, and complex objects in deeper layers. CNNs became the backbone of modern computer vision after AlexNet's 2012 triumph, and by 2015 they were achieving superhuman accuracy on image classification tasks while using parameter sharing to stay dramatically more efficient than fully connected networks.