Hello @sheiser1, I think this is a very good question. Just to think out aloud, whenever I read this type of a suggestion I immediately think about the main goal of HTM, which is to functionally imitate biological intelligence in the most basic sense. In that perspective, the more you deviate from neuroscience the more hand crafted it becomes. In my experience, it always leads to problems that you cannot foresee in the long run. To make matters worse, these are structures that we do not even fully grasp what they exactly solve. So adding and subtracting things means a lot of assumptions with limited experience. Not sure about the computational benefits but biologically it is kind of counterproductive to grow cells rather than segments.
I still think this is a valuable question. My implementation works as a part of an autonomous agent in a 3D environment and after 5 minutes of learning, the distal segment count per cell does not really seem to increase above 4 in a 1024 column and 8 cell per column configuration. Although I reserved space for 64 segments, this shows me that the temporal pattern combinations inside the environment is relatively low that it barely fills the unused cells. When an unknown sequence is encountered, Cortical Learning Algorithm picks the cell that is least used. In other words, new segments does not get created unless there is at least 1 segment in all cells of the active column. So if I had 64 cells per column, there wouldn't be any cells that have more than a single distal segment.
To extract a direct answer to your question; yes it would work but you would be limiting your prediction capacity if you ran out of cells that do not already have distal segments. On the memory footprint size, I think you would need more memory for the same prediction capacity compared to multiple segment implementation and I would not underestimate this part. In my implementation the cell size/count is the deciding factor after the size of all synapses. If I understand you correctly, your idea would require you to have more cellular information (more cells) with same distal dendrite information size.
This seems like a flaw but on the contrary, I believe this is something that is needed to guide the learning.
I would definitely welcome more insights on this question.