It’s been 40 years since I did bit-slice level CPU design; I made a nice little 8/16-bit CPU based on the 74170/74181/74182/74200 chips. Microcode used to be chip logic and not Verilog code. FPGAs were not really a thing yet. Things have gotten much more complicated since then.
That said - do you have a pointer to different papers that do deep predictive learning?
As far as I know - this is an emergent property of a “larger” multi-layered processing system.
I have been a long-time fan of the Global workspace theory, in particular the work of Stanislas Dehaene. This is also a large scale model of interconnected maps. There are also interactions between the counter-flowing streams that drive much of the interesting behavior in these systems. I see this emergent behavior as a recurring theme in larger systems.
My focus in all this is more oriented to the larger system level engineering and how the maps have to work together. The HTM model is surely part of this on one end of the size scale. The engineering scale at this level covers several orders of magnitude. You have to consider everything from individual synapses to large scale fiber tracts and population densities of fiber projections.
I could be way off base but I do think it’s time to take off the training wheels and put the HTM model in play as part of larger systems. I expect that this will change some of the current notions of what various parts are doing and refine the model.
In particular, I expect that the focus will shift from object representation to communications of object representation and deeper consideration of the distributed nature of those representations. I also expect that the coordination functions of cortical waves will take on more importance than it currently holds in the HTM canon.