Reinforcement Learning and HTM Algorithm

Yes. The way I see it: at the “subconscious level” other examples would be where hand is too close to flame and amygdala kicks in to quickly recoil back with fear the amygdala state is enough to make sure confidence in that action working gets lowered due to something having gone wrong from getting that close. We did not have to learn that sudden pain like that is bad as though there is choice over responding or not then take time thinking about it, the amygdala already knows it’s bad and immediately responds with fast reflex in other direction.

When something bad looking jumps in our way we can get startled by fear, without our at the logical level deciding whether we should be or not.

A one-way feed into the hippocampus is what the amygdala would need to have some control over what gets mapped in as the attractor to navigate towards. As a result “love is blind”, will “walk miles for” and all that.

While traveling: as long as everything that could go wrong didn’t the confidence in an action working increases (or remains at max value).

In the ID Lab model confidence goes down from bashing into solid objects, shocks to feet and navigational errors like heading not matching the spatial map given angle and magnitude. For at least us an amygdala adds extra feeling to confidence changing experiences, but for neocortical modeling purposes a feedback bit representing an “Ouch!” or “Oh crap!” from any system that can sense one is enough to know when something just went wrong somewhere.

The HTM part would be a neocortical sensory to sparsely addressed parallel memory made of virtual neurons, instead of sensory to densely addressed digital RAM made of silicone that is not at all parallel but similarly works well enough for ~28 or less address bits to have been worth experimenting with that over the years.

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