Hi, I have gone through the HTM school videos (16 episodes) at youtube and I still can’t quite grasp HTM and would appreciate your advise on my questions below:
In typical deep learning if one would like to approximate a very complicated function one would increase the depth (hidden layers) and the amount of neurons per layer. What are some recommended ways to do such a thing in HTM (i.e. increasing the network’s complexity or capacity to approximate more complicated functions)?
If I have an input in the form of a floating number vector and an output that is also a scalar vector how would HTM take in those inputs and output the outputs (i.e. is it serially or in parallel or both somehow)?