One of the difficulties I am seeing in trying to test my own theories is the fact that when position is encoded with feature, you tend to see a lot of overlap due to the cells representing the same position on different objects.
Now of course “position” can be encoded in the active columns, and “context” unique to different objects can be encoded in the active cells within those columns, but the difficulty comes when the position columns burst or have multiple predictive cells, and you end up encoding significant numbers of the same cells into multiple different object representations.
This is actually a similar problem I am seeing with sequence memory (which I mentioned on another thread), so guessing there is probably a common solution for both cases.