For a image down sample to a dot. keep or stop at point when the NN stops working at detecting accurately:
Then keep a ROI, region of interest. The focus.
The brain focus onto a small place. It will us a smaller fraction of its hardware, software or NNs:
So everything here is RNN / LSTM pinpoint scanner or a sequential temporal NN point scanner detector.
Its scan movement by eye muscles. The decision of scanning, Hilbert's Curve?:
What do know, NN do a really good job at chain codes:
Or the NN brain can chase lines.
This is unsupervised learning so the brain assumes that something exist and goes out and looks for it
with a simple NN. if it fine something with this simple tiny detector it build on it and with other tiny NN.
Then AI rebuild the world with its brain from this focus point scanning. The brain has its internal doodly bored
and recreates drawings, complete images, and they are perfected when they match with what the focus point detector.
Now with data record it can now work with it: