Hi,
I'm not reading this thread in depth, so if I say something inappropriate
in this context, please forgive me...
I believe NuPIC's SP is completely deterministic, IF you think of the
temporal changes in the input pattern. When you feed the same input
pattern "sequence" it will give you the same SDR always. No random
generator necessary within SP.
This is because SP tries to best represent the input patterns with the
usable amount of memory (the number of columns). If you provide only one
input pattern, it tries to create SDR using all columns to represent that
single pattern (you may end up with several SDRs to mean the same single
pattern). If you provide 100 patterns, the columns will get segregated by
themselves so that the region can convert all 100 patterns to 100 SDRs with
its best accuracy and usage of all columns. If some columns become
defunct, then the HTM region changes its SDR (e.g. using some least used
columns to replace the dead ones) to again best represent the input
patterns.
Everything is autonomous.
It is a beautiful algorithm to me.
Considering pattern change in time during creating sparse representations
might be one of the unique features of HTM CLA. (I'm not sure because I'm
not a neuroscientist).
In short, you can get the same representations by either of two ways.
1. always feed the same input pattern sequence.
or
2. always reset SP after feeding one (i.e. disabling learning)
Best Regards,
Hideaki Suzuki.
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