Cargo cult programming abounds in a lot of the ML stuff I read. It's complex and complicated, and I think it happens because a lot of the research happens in corporate environments with top-down pressure. I have worked in computational modeling of sensory motor systems, and it is really challenging work. This is way more interesting than banal model performance bragging!
As a neuroscientist who just started doing ML research, I would call this paper cargo cult programming. If you cobble together a hodgepodge of ideas from neuroscience and build a network to accomplish some trivial task with no baseline to compare it to, I find it really difficult to take anything away from that. Ignoring the cortical column aspect, I'm not particularly convinced that Hawkins's model is a better approximation of biology than a typical deep neural network, just different (and likely far less capable, if you were to apply it to a challenging task). Why not start with a network that we know works and make a biologically-inspired change, and then see if that improves performance on a well-studied problem? If it does, then you have 1) an improvement on the previous network and 2) weak evidence that your idea of how the brain works may be right, if we assume that the brain is a highly optimized information processing device.
