Hi Subutai, thank you very much for your answer. Apparently I do not understand correctly, when a cell is considered to be predicted and should be re-used when the sequence occurs again.
> If I understand your diagram correctly, picture (h) is the key one. In this > case you want a different cell in V to become active. The first time through > (h), when you see E that column bursts and the old V cell will become > predicted. The next time through though E will not burst and the cell in E > you show as L will get predicted. Then when V happens, it will burst because > it has not been predicted and a new cell in V will get chosen to be the > learning cell. And so on. Something is not clear for me, this is how I understand it: If the next time the sequence occurs, "the cell in E I show as L is predicted", it will get active at the next time step, because E actually occurs. This activation will cause the cell in V to be predicted and therefore the column will not burst. You meant it will burst, because it would not be predicted. I thought, that a cell becomes predicted as soon as enough cells are active which have a forward connection to that cell. With desired local act of 1, it would be enough if the cell in E is active and predicts the cell in V. Please tell me where I am wrong. Thank you again and all the best, Stefan > > A lot depends on how you set the parameters, but I believe this is the > correct intuition. > > In NuPIC, you can look at the (admittedly complex) test script > $NUPIC/examples/tp/tp_test.py In there we construct pretty high order > sequences such as: > > A B 0 1 2 3 4 E > G H 0 1 2 3 4 I > > In this example, in order to make the correct predictions for the last > element, the TP has to learn a very high order sequence. To learn this the TP > will likely have to see several repetitions. > > —Subutai > > > > On Wed, Dec 11, 2013 at 10:58 AM, Stefan Lattner <[email protected]> > wrote: > Hey guys! > > I am new to this list, just found out that it even exists. > > Since the white paper on the CLA was published in 2011, I am working on a > JAVA implementation of that model. > Because there was no such in NuPIC in 2011, I wrote everything from the > scratch. Many questions came up during this process but there was no detailed > description on how it works the white paper was the only resource I had. > > That is kind of sad, I would have had many questions. I am currently > finishing my masters thesis where I am writing about everything I found out > about the HTM and CLA during my experiments, because I didn't expect Numenta > to come up with more details (which is actually the case, except the code > Numenta is providing). > > A very important question for me is still that, according to Numenta, the CLA > should have a variable Markov Order. However, the way learning is described > in the CLA white paper leads to an order of two only. > > I made an illustration for my thesis how learning takes place, this is how I > understand it. > (At first, the sequence E-V-E-N is learnt (Desired local activity = 1). Then, > the HTM is reset and E-V-E-N is provided to the HTM again. Then, the HTM is > not reset while E-V-E-N is provided again). > Can somebody tell me how the order of learnt chains can get increased or what > I am understanding wrong? > > Thank you and all the best, > Stefan > > <temporal_pooler.png> > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
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