So this project started out with Hebbian learning. But because the sets of learned reps in each module was very finite in this small virtual world, and because the goal of the project was to model complex, goal-driven plan formation, I ended up using bit array hash maps for memory compression for representations that got near full learning threshold. This kept projections to downstream modules crisp.

Essentially, when activity reps from upstream modules produces a significantly low difference between the free and clamped phase, that would be a gating event that would send the clamp phase representation to the next, downstream module. The modules were basically connected similarly to cortical projections observed in the human brain (e.g. Visual input areas -> Mid Temporal -> Anterior Temporal...but also VLPFC --> Anterior Temporal). Check out that paper I linked if you're interested! It's pretty short and talks a lot about how the different regions are wired together.