Its hard to follow what you try to commounicate at least the last half.

Nonetheless, yes we do know certain brain structures like your image net analogy but the way you describe it, sounds a little bit of.

Our virtual cortex is not 'just a layer' its a component i would say and its optimized of detecting things.

Other components act differently with different structures.

A bit confusing for sure, but I think (not sure) I get what they're saying. Training a nn (for visual tasks at least) consists of training a model with much more dimensions (params) than the input space (eg: controller inputs + atari pixels). This contrasts with a lot of what humans do, which is take higher dimensional information (tons of data per second combining visual, audio, touch/vibration, etc) and synthesizing much lower dimensional models / heuristics / rules of thumb, like the example they give of the 5 second per mile rule for thunder.