A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

[gmirey]

@MaxLee

anything close (such as a puppy) was also a kitty. In humans, the way we get around this is some parental/classmate/teacher/sibling feedback mechanism

I was thinking about that very subject quite recently.
One way to have that teacher effect taken into account, for our online-learning models, is:
(I was taking the example of learning your ABC in a chat with @Bitking)

@gmirey

• we settle on some form of supervised learning (we take the view that mommy’s here)
• we’d need to replace the ‘error to expected output’+backprop of classic SL as used by DL, with something else
• this something else is simply the presentation of a pre-formed SDR (=‘expected output’), somewhere into the scheme
• there would thus be 26 pre-formed SDR for the alphabet. Here are your (precomputed) buckets.
• the rationale behind it is that the SDR stands for a platonic ideal of the letter, brought to ‘mind’ during the visualization of the sample, by another mean (like - the unmodelled sound pathway : mommy is repeating ‘A’ to make that puzzle piece keep resonating somewhere… and we’ve arguably learned to understand speech sounds before learning to read)
• since this is an ideal, an abstraction, the ‘somewhere’ is likely an higher area (like, one responsible for combining vision with sound in the example above - but any cortical patch ‘higher than visual’ would do for your model)
• we’d then need to wire the existing SDRs for letters (kept constant on the higher area) to the output of some area from the visual pathway, while the latter experiences the multiple presentations of different samples of the MNIST, at different positions/scales (and you can bring SMI and location to the thing, why not… I don’t think it’s fundamental here)

Any thoughts/criticism appreciated ^^

Note : Of course, the above is oriented towards training a feedforward path, for the purpose of evoking ‘A’ back in the higher area when we experience a sight of ‘A’, but from almost same scheme, we could also wire apical tufts of visual pathway, in reverse, to feedback from the higher-area-‘A’, in the hope that part of the ‘object recognition’ functions finally… percolates? compress? … down to early areas of vision themselves, like Numenta is maybe proposing recently (if I got this right).

[Edit] Although… maybe what I have in mind is not really anything new… since

@subutai

During learning, the output layer is determined randomly for each object, and then it is kept fixed while learning that object.

Reading this more carefully… well, in essence, I’m only replacing ‘randomly’ by ‘preset’, here…
However those input/outputs would be split in two hierarchically distinct areas in the proposal above, while I believe the HTM ‘output layer’ represents some bunch of nerve cells in same area.