I am really impressed by your work. That is awesome. I started to learn HTM and read your papers recently. I have some questions, maybe they are basic questions, but I really appreciate if you guide me regarding them.
In " Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex" paper in part 5. Materials and Methods the authors explained about the matrix of set of active cells A, and predictive cells \Pi, and a matrix of set of distal segments D^d_ij. This matrix D^d_ij shows all cells are connected to segment 1 when d=1, or all cells are connected to segment 2 when d=2, and so on? I mean elements in matrix D are q^d_ij something like this? How we should consider segments and synapses when we are writing them in this format (by matrices). Can I say these segments are expressing connections between cells?
I tried two combine these two papers “Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory” and “Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex”.
In paper “Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory” for predicting state, element-wise multiplications is not used, the method is different? Also, it uses a way to find SDR representation from input, while in other papers it is mentioned by Encoder we will get an SDR representation of input, that is correct?
In paper “Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex” authors mentioned “We assume that an inhibitory process has already selected a set of k columns that best match the current feed forward input pattern.” wuld you please explain me, what we mean by the best match? How we get it?