I feel like they're making up their own terminology in a bubble instead of integrating with the rest of mainline research?
CLA - Closest 'mainline' term would be a recurrent neural network, but the neurons and network organization are very different.
OPF - This is more of a runtime environment or a set of tools to work with.
Encoders - These are actually a step below what you think of as an autoencoder. They translate many data types into useable inputs for the network. Then the spatial pooler takes over and tries to find efficient representations.
While it's possible to describe Numenta's work with existing frameworks, and unifying nomenclature is always useful, I hope you'll take the time to learn the system so that we can apply the correct shared terminology!
Full Disclosure: I used to work for Numenta.
https://www.groksolutions.com/technology.html#cla-whitepaper
There were some earlier ones that used to be on the Numenta website (before the name-change), but I don't see those posted now.
With the exception of being able to handle on-line learning, I have a slight preference for their previous, more abstract approach. Their current approach seems to model the details of the neural connections at a lower level than what seems necessary to me. This is not, by the way, any kind of valid scientific opinion, just the views of a widely-read amateur.
Then Chapter 6 of "On Intelligence" From there you should be and to tackle his whitepapers which can be found here: http://blog.mohammadzadeh.info/index.php/hierarchical-tempor...
If you are interested in generalized strong AI, this is some of the best work out there.
Just describe the basic mathematical formulations and define what is different between you and the other. I wish they would do this more and write less verbose papers.