With vanilla TM distal segments become active, making their cells predictive. I guess I don’t see why the distal segments themselves couldn’t become predictive. So if a distal segment is predictive and then its column is activated, that segment could activate and inhibit all other segments on the column – as would normally happen with cells.
The premise is that we don’t strictly need 2 structures to encode sequential context (cells and segments). For its core functionality, HTM separates inputs’ spatial traits through activating different sets of columns (S.P.) and temporal traits by activating different versions of these columns (T.M.) – allowing inputs which share spatial traits to be distinguished by context. These different versions are normally embodied by different activated cells within the columns.
My point (and @bizunow13’s too I think) is that these different versions can be captured by segments alone. It should work as long as one activation of a column can be distinguished from another. This system is of course less bio-realistic, but I think it would behave the same in ML practice. But maybe I’m missing something? I’d be curious to see this structure implemented on the HotGym data to find out.