As to 1, it has already been established that there's no biological plausibility of backprob whatsoever. You can only call the current models "neural" networks

As to 1, it has already been established that there's no biological plausibility of backprob whatsoever. You can only call the current models "neural" networks in the vaguest sense of analogy. There is significant academic interest in this intersection between AI and neuroscience, to design biologically plausible neural networks (see e.g. spiking networks). I guess the reasons there not very well known in the larger ML community is simply that these approaches don't work that well (as of yet). Personally I don't believe chasing perfect biological plausibility will be very fruitful (in short term). An algorithm that runs efficiently on wetware will probably not be very efficient on current hardware like gpu's. The reason deep learning is so successful is for a large part that they are very good at exploiting the efficient linear algebra devices we have at our disposal (transformers are only the latest evidence of this).