I've had similar thoughts when thinking about how current Deep Neural Networks different from biological neurons. We know that in biology the brain uses Hebbia

I've had similar thoughts when thinking about how current Deep Neural Networks different from biological neurons. We know that in biology the brain uses Hebbian learning (neurons that fire together wire together) rather than backpropagation and that the network topography changes dynamically. Under those conditions I would think there is much more redundancy and generalization than what we typically have from fixed topology networks. I think this is key why (generalization) humans only need 1 or 2 instances of something to learn it, while deep learning requires thousands of samples to learn something.