Consider this example of the possible set of input patterns on L1 that might be found on a given section of cortex:
We would like the trained mini-column to vote on what pattern they might be sensing and settle on one of these sets of output patterns on the projecting axons from L2/3:
So in this given section of the cortex (spanning hundreds of mini-columns) the alignment between these input and output pattern is this:
I want this input pattern on L1 — > to form this output pattern in L2/3:
This samples a tiny subset of these mini-columns in this area. The actual number of input-output pairs co-existing in this section of cortex could be a fantastically large number. Naturally, the number of activated grid elements is related for a given pattern to the size of the learned pattern; as time goes on the pattern could get larger as more “fence sitting” elements are induced to learn the edge cases.
Note the important binding feature that allows mini-columns with very limited dendrite spans to work with very distant mini-columns to learn a very large continuous pattern. Sparsity is preserved at both the dendrite and the mini-column levels.
