Interesting perspective, @sunguralikaan. Thanks for sharing your experience.  

[quote="sunguralikaan, post:9, topic:3000"]
Almost everytime it did not converge onto an actual activation that happened as 
@jakebruce said. It contained bits and pieces from multiple activations which 
was very hard to utilize in a meaningful way.
[/quote]

I wonder if a single activation for a high order prediction is really always 
the "correct" answer here. I think it depends on the question that is being 
asked. In situations where multiple simultaneous sequences are known, from a 
given point in time the system rightfully <b>does not know</b> which sequence 
is going to continue. It'll ideally only have some idea of the frequency of 
patterns which you might interpret as their probability of occurring. So, you 
can ask the system different things here. You could ask it "what do you think 
is the most likely activation to occur at time step t+n where n>1, or, 
alternatively, you could ask it for everything that it thinks <i>could</i> 
happen which would best be represented as a union of activations which 
individually represent possible futures. 

In both cases, I imagine the predictions are going to get less reliable the 
farther out you go. In the case of the single activation, the single activation 
prediction is going to be less reliable due to the  number of possible answers 
blowing up exponentially leading to more uncertainty. In the case of a union of 
possible futures, the farther out you go the denser the SDR is going to become 
because you're unioning so many possible futures. The resulting dense SDR is 
then rendered uninformative as you go further out.

In any case, if it's still accessible, would you mind sharing your code that 
you used to tie cellular activation predictions back to the TM algorithm? I'd 
be very interested to see your approach to do this from an implementation 
standpoint.

Best,
Brody





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