Excellent! Thank you for your help gentlemen.

- Jeff


On Mon, Oct 21, 2013 at 2:15 AM, Garikoitz Lerma-Usabiaga <
[email protected]> wrote:

> Hi,
> I think this video explains it quite nicely too:
> http://www.youtube.com/watch?v=z6r3ekreRzY
>
> Gari
>
>
> On Mon, Oct 21, 2013 at 10:35 AM, Michael Ferrier <
> [email protected]> wrote:
>
>> Hi Jeff,
>>
>> That is what the spatial pooler does. See pages 21-22 of the white paper
>> for more details. Each column in an HTM region receives input from a unique
>> subset of input bits, so when the region receives a pattern of input bits,
>> regardless of what percentage of the input bits are on, some columns will
>> receive more excitation than others. Only the 2% of columns that receive
>> the highest amount of excitation will be activated. So regardless of what
>> proportion of input bits are active, the region will end up with a sparse
>> distributed representation of the input, with 2% of columns active.
>>
>> -Mike
>>
>> _____________
>> Michael Ferrier
>> Department of Cognitive, Linguistic and Psychological Sciences, Brown
>> University
>> [email protected]
>>
>>
>> On Sun, Oct 20, 2013 at 9:40 PM, Jeff Fohl <[email protected]> wrote:
>>
>>>  Hello -
>>>
>>> I hope this is not being posted to the wrong list. This is my first post
>>> here. Please let me know if there is a more appropriate place for this
>>> question.
>>>
>>> In preparation for learning NuPIC, I have read "On Intelligence", and I
>>> am now reading the HTM white paper put out by Numenta.
>>>
>>> Making my way through the white paper, I got stuck on one passage, which
>>> I can't really make sense of. Wondering if anyone can help me through this
>>> part. The passage in question is on pages 11-12 of the white paper PDF -
>>> specifically the second paragraph included below.
>>>
>>> *HTM regions also use sparse distributed representations. In fact, the
>>> memory mechanisms within an HTM region are dependent on using sparse
>>> distributed representations, and wouldn’t work otherwise. The input to an
>>> HTM region is always a distributed representation, but it may not be
>>> sparse, so the first thing an HTM region does is to convert its input into
>>> a sparse distributed representation.*
>>>
>>> *For example, a region might receive 20,000 input bits. The percentage
>>> of input bits that are “1” and “0” might vary significantly over time. One
>>> time there might be 5,000 “1” bits and another time there might be 9,000
>>> “1” bits. The HTM region could convert this input into an internal
>>> representation of 10,000 bits of which 2%, or 200, are active at once,
>>> regardless of how many of the input bits are “1”. As the input to the HTM
>>> region varies over time, the internal representation also will change, but
>>> there always will be about 200 bits out of 10,000 active. *
>>>
>>> So, what exactly is going on here? How does a fluctuating input flow of
>>> 20,000 bits get converted into 200 bits? Obviously there is something
>>> important going on here, but I don't understand what it is. Any help
>>> illuminating this would be greatly appreciated!
>>>
>>> Many thanks,
>>>
>>> Jeff
>>>
>>> _______________________________________________
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>>> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
>>>
>>>
>>
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