I don’t want to put words in other peoples mouths but I believe that this is an attempt to move beyond the +/- 8 column reach of the dendrites of a single cell and allow binding of the features of an object over a larger area.
Binding is a central problem in neural network representations. How do we combine all these individual features sensed by individual columns into a larger object?
This is one of the main driving forces that lead me to Calvin’s hex-grid coding method as outlined in my “HTM to hex_grid” model. This model allows a unified object representation to be bound over a large area of a map using all local operations without resorting to the magic of the inter-map connections to do representation spreading. If tract bundles diverged as is sometimes proposed that would be a plausible method but study of real brains shows that they maintain loose topography as the tracts connect one map to the next.