I thought I would take a moment and explain the theory behind what I am trying to do.

Essentially it all has to do with information entropy

As we learn we integrate information, as we integrate information it becomes more entropic.

The Maximum Entropy Principle states that the most entropy is in the best learned data. In other words Maximum Entropy exists where the information integration is most completed.

mEPLA, my theory takes this into account, and turns it around to find new opportunities for learning. Essentially what it says is that the minimum entropy is found in areas where information integration is less complete, By extension this means that by allocating learning resources to areas of low entropy we can find opportunities to learn and better integrate information.

simularity is an approximation of entropy, the more a thing is simular the more integrated it’s information is, and the less learning that can be achieved.

What I think I am doing, is proving that similarity detection is a cheap way to detect learning opportunities. By inverting the logic, I am converting what I think is a natural similarity detector into a novelty detector which can detect learning opportunities and by applying learning resources to them, increase the information integration of the whole brain. I am just doing it almost as soon as the information gets to the cortex.

Theoretically novelty is an indication of a learning opportunity.

But to monitor entropy I need to operate in bulk, comparing the entropy of each input with all the others around it. I can then relatively easily gate the lowest entropy locations so that the lowest entropy triggers the gating of the outputs this is probably an early example of salience.