Hoping to start a discussion on this (IMO) interesting topic:
What I know of HTM all focuses around learning to recognize, represent and predict patterns. But these same systems that process incoming data also send output signals to the motor neurons.
At a high level, these commands have to incorporate a goal of some kind, even something as simple as ‘move towards food’ or ‘move away from danger’.
But how do you make the system learn to produce action outputs in response to its sensory inputs that are in accordance with those goals? It seems to me that you need some kind of supervised learning mechanism that modulates the HTM algorithm. How does the brain accomplish this?
Spitballing here… from my tiny, anecdotal knowledge of neuroscience, the brain uses neurotransmitters to help interpret different kinds of inputs as ‘good’, ‘bad’ etc and these somehow affect the resultant learning after an experience. (For example, our brains are hardwired to enjoy sugar, however the actual ‘taste’ qualia are encoded)
Similarly, perhaps we could designate certain HTM inputs as desirable or undesirable a priori and use reinforcement learning methods. E.G. determine the sign of the permanence updates in the motor control region (layers 5 and 6?) based on the desirability of the outcome.