I wrote a blog post about Numenta (among other topics).
I have two questions:
Connectionist theories often have the drawback that they don’t represent hierarchical concepts or composite objects.
Before the sensory-motor work you did recently, I would have thought that HTM-theory could not represent such concepts. Reading your material, I could understand that your inputs could handle an encoder for a date, or a scalar or a GPS coordinate, but it seemed you could not represent a composite object.
But now that you can represent any object as a kind of 3D CAD representation, maybe composite objects (that are composed of simpler objects) can be represented as well as concepts that are made up of simpler concepts.
There are two requirements of composite objects:
- You would have to be able to decompose an SDR that represents a chair to several SDRs, one of which might represent a chair-leg (for example). Likewise you can decompose an SDR that represents a sentence to several SDRs, one for each word (for example).
- SDRS of similar objects would be more similar than SDRs of very different objects. From what I can see of your material, that is true, since they share more active columns.
The reason I ask this is that my article contrasts two research approaches, yours and a group at the University-of-Waterloo group, and this is one possible comparison.
Recently I’ve been reading “free-energy-minimization” theories of how the brain works. Basically, these theories deal with the issue of the “unexpected” input, or “surprise”. Some theories say the brain is a hierarchy of predictive levels. If there is a “surprise” at any level (for instance, you listen to a melody and hear an unexpected note) that surprise is forwarded up to a higher level, to see if it can be explained away. Perception is supposed to be (in this theory) a balance between prediction and sensory input. How would HTM theory fit into this template?
Any help is appreciated.