I think one layer of neurons can learn a spatial pattern. Where each segment is an SDR, and a bit in an SDR represents a synapse, an HTM Neuron can be defined as:
- a list of proximal segments
- a list of distal segments
So I guess the right nomenclature would be “a map of lists of SDRs”? (and don’t forget the permanences, too)
But they all must work together by exhibiting the same properties and processes. The learning is a population effect within many neurons.
When we process sensory input associated with an object in the world, we end up storing these sensations as a part of the object’s representation in these “maps of lists of SDRS”. So given the union properties of SDRs, if we could produce an SDR that represents a similar sensation at a similar location (sort of like a search parameter) it should union with our known sensations to uncover objects that have similar features. This could be done with or without the location (but with location search will be better).