Aseem, I think you might be confusing the tasks of the Temporal Pooler and the Classifier. The Temporal Pooler's job is to store the permanence values between cells and determine the predictive state of the cells in the next time step.
The Classifier is trying to solve a different problem: How do we map a given set of a cells to an actual set of input bits? The temporal pooler is capable of making predictions about which cells are going to be activated next, but it has no idea of how these cells correlate to input bits. Because at the end of the day, we care about predicting the actual input, not cells. Cells are only a means to an end. There's a history lesson also hiding in here. In the past, Numenta tried to tackle this problem of mapping cells back to input bits by using a technique called "reconstruction". Reconstruction, also referred to in the code as "Top Down Computing" entails feeding in a set of cells into the temporal pooler and asking the temporal pooler for the set of columns for which the predictive cells reside. These columns, in turn, would be fed into the spatial pooler, who would be ask to produce the most likely set of input bits that could have activated these set of columns. For those of you familiar with deep learning and restricted boltzman machines, the idea is pretty similar. This approach didn't work out very well in practice so Numenta switched to using the CLA classifier. Personally, however, I think reconstruction would be a very interesting avenue to explore. Gil. From: Scott Purdy <[email protected]<mailto:[email protected]>> Reply-To: "NuPIC general mailing list." <[email protected]<mailto:[email protected]>> Date: Friday, August 9, 2013 10:54 AM To: "NuPIC general mailing list." <[email protected]<mailto:[email protected]>> Subject: Re: [nupic-dev] CLA classifer Aseem, what limitations of the current classifier are you trying to address? On Fri, Aug 9, 2013 at 12:34 AM, Ramesh Ganesan <[email protected]<mailto:[email protected]>> wrote: Good thoughts Aseem Hegshetye :) I think, storing weight matrix itself wouldn't enough to calculate 2nd, 3rd etc., prediction steps. we have to find out activation of cells using the predicted state values and do the inhibition. The process might be like pattern -> get predicted state cells -> next phase without learning using predicted value of that predicted cells (2nd step)-> get predicted state cells -> next phase without learning using predicted value of that predicted cells (3rd step) -> ... is it make sense? Thanks Ramesh Ganesan. _______________________________________________ nupic mailing list [email protected]<mailto:[email protected]> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
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