Neural network rotational architecture mirrors minicolumns


I found a paper on a NN archetechture that I think mirrors part of htm theory.

I think that if you squint you can see that mini columns / grid cells work more on rotation than adding or muliplying vector points together.

In a simple case, a mini column with 3 cells on it that when not bursting only has enough ions to activate one cell, looks like a unit vector in 3d space that rotates from the x axis to the y axis to the z axis without changing magnitude.

If instead two cells can be active at a time, the vector would have intermediate steps half way between the axis. (1,0,0) to (1, 1, 0) to (0, 1, 0)

The magnitude does change some in this case, but I do think that a mini column with a lot of cells and 2% activation probably looks a lot like a rotation in a high dimensional space, especially if the the activations don’t all change at once every cycle.

Maybe thinking about activations as rotations could help link the theory to head direction cells?


This is the approach outlined in the PDP books, in particular, the tutorial for linear algebra. If memory serves correctly, this is also a big part of the Cooper RBF model.

Considering that SDRs are positional codes have you considered set theory as a tool for analysis?

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