I’ve always thought HTM was superior to NN in at least one major way and I’d like to know if the community feels the same way or if I’m not thinking about it correctly.
HTM does not need to do any backpropagation because it gets updated as the information flows through the structure the first time.
To me, this seems like a massive feature - that the structure essentially achieves the same ability to update itself, but can do it in real time rather than needing data to flow all the way to the end of the structure then get bounced back all the way to the front.
Not only does this seem like a good feature of the algorithm it also feels like it should be entirely self-evident. Am I thinking about this correctly?
Furthermore, the larger scale HTM structure, the hierarchy of regions does seem to have this ping-ponging of information from the bottom of the hierarchy to the top and back down. But this is, as I understand it, done more primarily to produce appropriate motor behavior, rather than simply adjust weights of connections between regions. I bring this up to make the point that it also seems self-evident that there is a space for this reverberation of information throughout the entire structure, but that it can be used to do so much more than what NNs use it for today.
Am I over-simplifying everything or are these principles in play in about the same way I’ve articulated them?