Hi @bkutt,
I have tried what was proposed above in one of the previous architectures that I worked on. Almost everytime it did not converge onto an actual activation that happened as @jakebruce said. It contained bits and pieces from multiple activations which was very hard to utilize in a meaningful way.
So I went even further by creating a circular loop of two layers (reciprocal connections in some sense) in order to extract the dominant actual activation (an activation that happened) from this sparse union that contained bits and pieces from separate states. At the time I believed this was how basal ganglia resolved conflicting activations from a union of predictions (Cortex->Striatum->Gpe/Gpi->Gpi->Thalamus->Cortex):
Lets say you have A and B layers that only have spatial pooler. Layer B classifies Layer A by taking its columnar/neural activation as input. In turn, Layer A takes input from B’s columnar/neural activation. Assume we trained this layer loop some time so that they both classify each other correctly. If you activate some union activation in A and let the circular flow continue, it converges onto an actual state as it passes to B and comes back to A and goes back to B and so on. This convergence is due to the merits of SP algorithm. The method worked in terms of extracting the dominant real activation but it solved the wrong problem for me which helped me understand a more fundamental problem about the architecture.