Perceptrons were modeled after neurons in the sense of simple distributed computational units connected by weights. It doesn’t go any further than that. Convne

Perceptrons were modeled after neurons in the sense of simple distributed computational units connected by weights. It doesn’t go any further than that. Convnets were not inspired by biology directly and the only property they share in common is a spatial receptive field. The weight sharing and overall architecture were not based on the visual cortex. ReLUs were chosen because of their constant derivative in the positive domain. The reduced compute was a nice side benefit and newer methods (swish, gelu, mish, etc.) all do away with the simple computation in favor of smoother loss landscapes (while maintaining a mostly linear positive domain). Backprop, however, takes no inspiration from biology.