I have a question regarding the way HTM learns long-range dependencies, and in particular how it compares to the way LSTM does it.
It is my understanding that in order to learn long-range dependencies between two events A and B separated by x number of events, HTM would need to memorize the entire sequence of x events, even though those events may not be causally related to A or B. In contrast, LSTM does not need to learn the entire sequence, thanks to its gating mechanism. Rather, it learns a direct relationship between A and B. Am I understanding correctly that currently, in the particular scenario where the x events are not causally related to A or B, HTM cannot actually learn a direct connection between A and B?
I am aware of the good results achieved by HTM on long sequences learning as shown by Cui et al. (2016). However, the need for HTM to learn the entire sequence, instead of learning a direct connection between A and B, seems like a important limitation. Would you agree on that?
Thank you very much