HTM is intended to be a theory of the neocortex. But in some ways the algorithm seems to apply more appropriately to the hippocampus. In particular, both HTM and the hippocampus learn from experience online, and very quickly. In many theories and experiments, the neocortex on the other hand appears to learn gradually.
I recently reviewed this paper  summarizing and extending Complementary Learning Systems theory. Briefly, the theory can be summarized as follows. The neocortex has relatively dense activity and connections with weights that are generally updated slowly, whereas the hippocampus has very sparse activity and connections with weights that are updated rapidly. The hippocampus learns episodic memories in a one-shot fashion, which are then replayed to the neocortex gradually during and interleaved with experience, and during sleep and rest, in order to gradually update the less-plastic synapses in the neocortex.
This is supported by the observed sparsity in the various regions (~10% in many upper regions of neocortex, ~4% in CA1 of hippocampus, ~2.5% in CA3 of hippocampus, ~0.5-1% in dentate gyrus of hippocampus), the highly-plastic synapses in the hippocampus compared to the neocortex, the severe learning impairments of hippocampus-lesioned animals, and theoretical problems with attempting to support both generalization ability and fast learning free from catastrophic interference (also called catastrophic forgetting).
In many respects the hippocampus can be seen as a primordial neocortex from which the true neocortex evolved, and there is preserved structural similarity between the two (at least in CA3/CA1) in terms of lamination and cell types and so on.
It’s clear to me as someone who does a lot of “traditional” machine learning that some sort of high-plasticity episodic memory needs to be combined with a low-plasticity generalizing memory, so I do expect intelligent online learning agents to eventually require both of these. But can a fast-learning system like HTM successfully generalize while preserving its one-shot capability? I’m leaning toward HTM being an effective theory of CA3/CA1, with slower-updating neocortex (while surely preserving some of the insights of HTM) ultimately having more in common with current deep networks.
Thoughts? I recommend reading the paper in any case, it’s a great modern look on a long-standing theory of the systems-level learning mechanisms in animals.
 Kumaran, Dharshan, Demis Hassabis, and James L. McClelland. “What learning systems do intelligent agents need? Complementary learning systems theory updated.” Trends in cognitive sciences 20.7 (2016): 512-534.