Is the visual cortex of newborn babies right off the bat capable of making sense of raw visual data, for instance, converting the constant stream of raw RGB images perceived by the eyes into a meaningful higher-level representation of objects in motion in a 3D world? Yes? No? If not, then does it mean this skill has to be developed over time by means of some learning mechanism?
If the visual cortex needs time to learn advanced visual skills, how does the actual learning mechanism work? Does the visual cortex have to optimize the connections between neurons, in a way analogous to how artificial neural networks optimize their parameters through backpropagation algorithm (machine learning)? If this is the case, then where does the visual cortex get its error signals from? To make the last question clear, in machine learning the typical approach is to compute the gradient of a loss function which compares the model's prediction with the ground truth, and the model's parameters are updated by moving the parameters in the direction of the gradient. If the visual cortex is learning advanced visual skills by virtue of a similar learning mechanism, then what kind of loss function is the visual cortex optimizing?