Incidentally, I’m skimming through Tutorial and Discussion on Cortical Column Voting Mechanisms Developed by Numenta - 5 April, 2022 right now, which addresses the voting mechanism too and appears more recent. I think it clears out my confusion in the OP, that there is no such thing as “SDR schema”, at least in Numenta’s SDR conception. Subutai Ahmad explicitly states that “Object IDs” will problematic at 10:27, and around that moment, he explained a lot that there’s no same SDRs among cortical columns, even no one-to-one mappings.
The HTM learning algorithm as discussed in that video seems doing heavy skyhooking by randomly allocating new SDRs for new objects to learn, and keeping their stable activation for learning purpose. So very true, learning should be very different to sensing.
This leads me to another interesting question: how about the single CC SDR for an object, across the (possibly long) course of learning it? Does it change while its features gradually learnt? How will it converge/diverge after it turns out to be same/different objects?
It’s made stable per current Numenta simulation algorithm, but as some asked in that video, in reality one can learn half-way for some object, then switch to learn other things, then resume learning the previous object. It doesn’t seem that an exact same SDR can be resumed perfectly in that case.
I later realized that “reference frames” is one of the key points of 《Thousand Brains》, my imaginary circles do occupy certain space (at certain location) in the canvas in my mind, so yes I think they use the reference frame of my imaginary canvas.
Yet there’s still one thing I’m not clear, that the “roundness” is not an object by itself but some attribute/property/feature. An imaginary circle is an object, I still wonder what SDRs have anything to do with associating the roundness to a circle, and further, for 2 circles, it’s very unintuitive for me to conceive they posses 2 separate “instances” of roundness.