The Universal Constraint Engine: Neuromorphic Computing Without Neural Networks

It's ok to be harsh — I was vague, and I know it. Here's why: I remember the story about DOS. Tim Paterson built it, showed too much, and someone else built an empire on it for $50K. I have working constraint rules that produce real circuit behaviors. The paper shows what comes out, not how it works, and that's deliberate. The patent is provisional. I'm not going to hand over implementation details on a forum so someone with more resources can run with it. You'd do the same thing.

This is indeed a content-free "paper".

Bricken's boundary logic is a solid reference point. Both approaches throw out the standard computational primitives and let behavior come from structure instead of instructions.

The difference is what you're constraining. Bricken works with containment and distinction. UCE works with conserved quantities — closer to physics than logic. You define constraints over those quantities, and computational behaviors like memory, oscillation, and logic gating fall out of satisfying them simultaneously.

The other big difference is the output. UCE doesn't produce a proof or a reduction — it produces a state-transition graph that compiles directly to hardware. Same rules, different substrates. That's what the Embodiment Mapper layer does.

The behaviors that emerge — hysteresis, oscillation, bistable memory — are the same computational primitives you see in biological neural circuits, but they come from constraint satisfaction over conserved quantities instead of simulating neurons. The architecture doesn't model neurons at all. It produces the same outputs through a different mechanism. Whether that still counts as "neuromorphic" is debatable — I use the term because the output behaviors map directly to the same hardware substrates (Loihi, SpiNNaker, etc).