So, some thoughts:
This seems to suggest that the idea that Numenta has about displacement cells is not entirely accurate. It does not appear as though individual macrocolumns are modelling complex objects. Rather, compound objects seem to be implemented by ensembles of active macrocolumns.
Numenta’s current understanding of displacement cells and object composition, as I understand it, suggests that a composite object is represented in the cortex as a union of SDRs for each of the subcomponents, and their relative locations. The exact mechanisms for this don’t seem to be figured out yet, other than just that there’s unions involved.
On the other hand, the paper I linked suggests that different subobjects are modelled by separate macrocolumns.
Rather than every macrocolumn modelling everything in parallel, different macrocolumns appear to specialize, creating these “Macro SDRs” of sparse macrocolumn-level activity. Compound objects are still represented by unions, but these unions exist on the macrocolumn level, not within a macrocolumn.
Say you look at a coffee mug. The cup part of the mug may be represented by one set of active macrocolumns, and the handle may be represented by a separate set of active macrocolumns. If there is a logo on the cup, another set of macrocolumns will be modelling it. If rather than looking at a mug with that logo, you instead look at the logo on a piece of paper, the macrocolumns that represented the logo on the mug will still be active, but the other macrocolumns will be replaced by a set of macrocolumns specialized to modelling the paper.
This is exactly what was observed in the paper above.
It seems to me that there’s likely a lot of interesting dynamics that emerge from HTM on large scales, across a large number of macrocolumns, that Numenta is currently ignoring. I understand that there are computational limits on simulations on that scale, but I can’t help but think that a combination of extrapolating properties of smaller-scale simulations, and looking at the neuroscience more (like this paper) would bring research into a useful direction.