Hello, This is an update for those who are interested in continuous HTM.
I have modified the formulas of the continuous spatial pooler to result in different active column combinations instead of just different column intensities when the input intensity is modified (but the pattern is kept the same). So, I can now feed inputs to the region by varying the strength of the input instead of producing entirely unique input patterns. I can directly pass scalars to the region. This was the original intent of the algorithm, but my previous formulas didn't actually work. As far as I can tell from the tests I have made the new formulas do work. It now works very similarly to a self-organizing map. The weights of the columns act like prototype vectors for their receptive field. The activity of a column is given by a function of the distance between the prototype and the input. They then inhibit those of lower activity around them, and learn using Oja's rule. To calculate the activation of a column: Where A is the activation, x is the input vector (the values in the receptive field), p is the prototype (weight) vector, and C is some constant scaling factor. The column state S can then be computed as : Where C is a constant scaling factor that may be different from the one in the previous formula. The learning rule is still the same as it used to be. I have updated the repository. For those of you who see this for the first time, it is here: https://github.com/222464/ContinuousHTMGPU
