The only good answer here: the brain is an incredibly complex thing, encompassing many parts that are structured differently, and we know extremely little about it. We cannot definitely answer the question.
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So, "deep learning" is the idea of doing representation learning via a chain of learned feature extractors. It's all about describing some input data via *deep hierarchies of features*, where features are *learned*. A further question is then: is the brain "deep learning"?
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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).
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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.
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I don’t think brain is doing a back prop which is driver of deep learning . Also a child learns from sparse data . One aspect is what a network has learnt. Is it really features or statistical regularities which also bengio talks of in context of cnn
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May be the answer lies in evolving network architecture on evolutionary algorithms rather then a back propagation
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"conceptual inspiration, not direct emulation" - @ToluClassics the hype.
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Kindly, there's a bit of false equivalency comparison here between "deep learning" & expression, output, of learned value(s). Mechanisms of input & output in the brain may overlap but they're also not uniform in execution - probably.
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