The claim is that the topology in HTM might be more about biological constraints and less about functionality. I’ll demonstrate why I think so more and more, especially after digging deep into SMI
Let’s first examine a case where topology works - in convolutional neural networks. Without going into too much detail. CNN works because of 3 key reasons
- Local feature and featur representation are consistent globally (a line is a line no matter where it is)
- Local feature groups are meaningful (elements close by in the input vector have relations)
- After operation, the 2 previous statement is still true (you can stack convolutions)
Like CNN, topology in HTM tries to capture the localized information of an SDR. Then by stacking/connecting cortical columns together to form representation of objects. However in HTM the properties does not hold true. Let’s assume an visual pathway and we have an signal from the retna encoding a picture of a horse. The first problems arise right after we get to V1. Since every neuron have their own synapses and permeances. Any nicely encoded SDR will break into different representation. Neuron A will have a different representation of a tail vs neuron B. Breaking property 1 and 2 for V2. Thus no information sharing within V2 can happen since every neuron is dealing with different a representation. This happens for every layer in every column. Rendering topology useless, there’s no localness in the generated SDR.
Note: 1000 brains theory can solve this via movement. But still topology takes no functional rolw in processing.