What you're kinda describing is something that the "godfather" (if you will) of Deep Learning, Geoffrey Hinton, also has suspicions about. https://www.quora.co

What you're kinda describing is something that the "godfather" (if you will) of Deep Learning, Geoffrey Hinton, also has suspicions about. https://www.quora.com/Why-is-Geoffrey-Hinton-suspicious-of-backpropagation-and-wants-AI-to-start-over https://www.quora.com/Why-is-Geoffrey-Hinton-suspicious-of-b... Basically, we have yet (from what I know - I am not a researcher, and this field moves fast) to find any form or method by which biological neural networks (aka brains) perform "backpropagation" - ie, neurons seem to be feedforward only systems. Furthermore, we (people) don't need thousands or millions of labeled examples in order to learn something. Now, I know someone else replied to you about how you see millions of cat images based on some flawed reasoning that the eyes are seeing at 60 Hz - which is untrue, because it's a continuous analog system - plus there's the thing of neurons being spiking potential systems (vs what common ANNs model); but the idea is intriguing that we do see much more than one or two "images". But there are other situations where we learn things that don't require this at all, and there aren't such vast amounts of "labeled input" to learn a concept. These two issues are problems from mainly a conceptual point; they obviously don't stop current ANNs from functioning or being useful. But they do call into question how our brains are actually doing things, and especially doing this with such low amounts of power (vs the huge amounts of power required for deep learning systems, for what would be basic tasks for humans). Another wrench that may come into play: I recently learned (I think it was from an HN post) that biologists and others are coming around to the idea that genes don't work like we once thought they actually did, and that cells - internally and externally - have some kind of communications system and other "stuff" going on that makes the whole system much more complex than the simplification most people learn about. I tend to wonder how such new information and realizations and "mechanics" will play into how we understand how neurons actually work and communicate biologically - whether any of that plays into "how brains learn". There's long been questions about chemical pathways and other things that aren't captured by common (and even uncommon) ANN models. Then there's also been questions as to what roles other cells in the brain that aren't neurons (glial cells) in the process, if any.