If you think a 2 year old is doing deep learning, you're probably wrong. But if you think natural selection was providing end to end loss optimization, you might be closer to right. An _awful lot_ of our brain structure and connectivity is born, vs learned, and that goes for Mice and Men.
Why not both? A pre-trained LLM has an awful lot of structure, and during SFT, we're still doing deep learning to teach it further. Innate structure doesn't preclude deep learning at all.
There's an entire line of work that goes "brain is trying to approximate backprop with local rules, poorly", with some interesting findings to back it.
Now, it seems unlikely that the brain has a single neat "loss function" that could account for all of learning behaviors across it. But that doesn't preclude deep learning either. If the brain's "loss" is an interplay of many local and global objectives of varying complexity, it can be still a deep learning system at its core. Still doing a form of gradient descent, with non-backpropagation credit assignment and all. Just not the kind of deep learning system any sane engineer would design.
I don't know what you mean by end to end loss optimization in particular, but if you mean something that involves global propagation of errors e.g. backpropagation you are dead wrong.
Predictive coding is more biologically plausible because it uses local information from neighbouring neurons only.