First of all, we have no idea how biological NNs learn, how they represent information, how they reason etc. Given what we do know, there is no reason to assume any similarity with ANNs on any of those fronts. Just to give one example, we know very well that a single biological neuron encodes significant information and is capable of reasoning on its own. In fact, even non-neuronal biological cells are capable of such - especially looking at single-celled organisms, which display extraordinarily complex behaviors with no NN in sight.

Second of all, we don't exactly understand how the huge models we have actually encode the higher-level representations of the training set that they store. Of course, we can say for sure that they are not literally storing a copy of the data on simple space requirements. But we can also say for sure that their "understanding" of the data, as well as their capacity for inference, is significantly different from our own - since they make certain mistakes that are nearly impossible for a human to make, while showing super human abilities in other aspects. So, if anything, we must conclude that whatever it is they are doing, it is most certainly not a way of understanding the information the way we understand it.