Eric posted about his efforts on Reddit/ML, and is actually getting UPVOTES for it! http://www.reddit.com/r/MachineLearning/comments/2key72/on_sparse_distributed_representations_and/ .
It is practically unheard of for the Reddit ML community to upvote anything involving HTM, so this is quite an achievement! ;) Thanks for sharing your progress, Eric. --------- Matt Taylor OS Community Flag-Bearer Numenta On Fri, Oct 24, 2014 at 9:46 PM, Eric Laukien <[email protected]> wrote: > 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 >
