I'll attempt to answer this question with the best of my understanding. Someone more knowledgeable, please feel free to correct me where I'm wrong!
I'll make a couple of assumptions in order to come up with a concrete answer. 1. Since you didn't specify how the sensory region that can accept arbitrary strings works, let's say it works like this: It splits up the string by semicolons, and encodes each word in the resulting list as a category. Category encoder treats each unique input as independent, and assigns a random dense encoding for it. This means that 'flower' would look something like 1101, and 'bee' would look something like 1010, and 'horse' would look something like 0110, so the string 'flower;bee;horse' would be encoded as 1101 1010 0110. 2. When you insert the word 'dog', you're actually inserting the word '___;___;dog', such that the category encoding for the word 'dog' shows up at the right side of the input to the spatial pooler. 3. Let's assume that the spatial pooler's columns are connected to a small locality of the input, so a subset of the columns would be connected to each of the three words in the input. If this were the case, then the spatial pooler would learn to produce an SDR with 'on' values representing each word, since each word can be considered a spatial coincidence of the exact configuration of bits outputted by the sensory region. Further, each SDR would represent sparsely the words in the input *in order* due to the second assumption of spatial pooler column locality. Now we can finally consider your exact question. TOR = boolean.OR(T1, T2) would now be the union of the SDRs of 'flower;bee;horse' and 'bee;flower;dog', and since '___;___;dog' would overlap on 'dog' with 'bee;flower;dog', X = boolean.AND(TOR,C) would meaningfully overlap with TOR on the right third of the SDRs. Keep in mind that you wouldn't get full overlap, since 'dog' is only a third of the input. Also, you probably wouldn't get exact overlap on the 'dog' third, because of the stochastic nature of the CLA. All I'm saying is that with enough training data it would eventually learn to recognize words in particular positions in the input, so the 'dog' overlap would eventually be detected. On Thu, Sep 12, 2013 at 12:37 PM, Stewart Mackenzie <[email protected]>wrote: > Hi all, > > A very intelligent chap said in a 3 part video on the numenta YouTube > channel that a python dictionary goes into a sensory region. This region > outputs a variable length SDR which is fed into the spatial pooler. The > Spatial pooler then spits out a 2048 binary vector which has been made > sparse. > > Say the sensory region can accept arbitrary strings. > > Say I insert 'flower;bee;horse' into the sensory region and record the > SP's output and call it T1. Then I insert 'bee;flower;dog' and record the > SP's output, called T2. > > Now I only insert the word 'dog' into the sensory region and record the > SP's output, calling it C. > > If I TOR = boolean.OR(T1, T2) then do a X = boolean.AND(TOR,C) will C and > X be identical? > > Thank you. > > Stewart > -- > Sent from my Android device with K-9 Mail. Please excuse my brevity. > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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