I thought about this too for a while. We-re nothing like CNN that scan all possible tiny patches/kernel at every given time step.
We use fovea, which behaves like an animal: focuses on a variable size area then it moves on a different area - not necessarily the same size, fovea’s “attention” motion is 3D: left-right, up-down, zoom in-out.
Now how this can help?
First, amount of data gathered is much smaller (or sparser if you like), that gorilla in the room experiment proves we mostly see only what we-re looking for. So it might help with limiting memory usage.
But how would it work in practice with HTM? well, it would need to predict an optimal patch/motion/patch/motion/patch… sequence.
That would require some form of RL which rewards “good motions” and “good predictions”. And that kind of sucks.
For simpler tasks like MNIST where all samples have a decent, common position/size you might go with predefined motions - e.g. all streams trained on same sequence of 7-10 patches, like start from a general image, then zoom in center, move left, right, etc…