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However, my gut feeling is that the brain is generally not DL, although some submodules could be described as DL or part-DL (e.g. the visual cortex is a deep hierarchy of features, albeit not all are learned, and has been a considerable source of inspiration in DL).
I would add that our current understanding and usage of modern deep learning -- its genealogy -- lies mostly in earlier modern machine learning techniques, not in neuroscience. The influence of neuroscience has been one of high-level conceptual inspiration, not direct emulation.
On the surface, that seems relatively reductionist, but something like that that is the kind of complexity that composes well at massive scales. I don't think we can comprehend the vast oceans within oceans of complexity 100 trillion synapses give rise to.
As far as genealogy goes you may want to have a look at the affiliations on the backprop nature paper. Or the wonderful PDP books, which read remarkably modern today. I think the genealogy is shared, and that should be celebrated. Interdisciplinary science is great!
Humans clearly have some genetically coded feature extractors, and even genetically coded behavioural packages.
I always agreed that deep learning is to neuroscience what modern aviation is to a bird (Minus the fact that deep learning didn't supersede it's analogue counterpart)
I wonder, when at last people will notice Numenta and Jeff's neocortex HTM?
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