Thanks for the explanation. It's certainly something I want to try in my research if I have the time.
Another thing I was thinking was making use of the capabilities of the current NuPIC code base to produce scalar/class predictions of the data signal. Consider a single signal that takes on scalar values. Concretely, I want to look at the probability distribution that is produced for the next time step's scalar value. This prediction of the scalar value can be assumed to be 100% correct and then used as a simulated next time step data input into the model. The rest of the algorithm follows normally until you get a new set of predicted cells and, voila, a high-order prediction of cellular activations. In the case of branching sequences, ideally the probability distribution at the fork in the road will appear significantly bimodal, trimodal, etc. There's no reason each path couldn't be followed separately to get a sense of multiple possible futures. I'm aware that the scalar value prediction mechanisms in NuPIC support high order predictions, but that might be too limiting for my purposes. I need knowledge of all possible branching sequences. --- [Visit Topic](https://discourse.numenta.org/t/getting-high-order-predictions-of-cellular-activations/3000/12) or reply to this email to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discourse.numenta.org/email/unsubscribe/aaaf13729b13b4db7212f9b183976f0b568437dd2af47d779ffff0258aa83f2d).
