The Universal Constraint Engine defines simple rules about what can and can't happen. The computer figures out the rest — memory, logic, learning — all by itself. No programming required.
Click "Start Walkthrough" to see how constraints create computational behaviors step by step. Or toggle individual constraints below to experiment.
The system has 7 binary quantities: E, I, T, F, R, L, P. Each is either ON or OFF. This is the building block.
Rule: "E can only turn on when L is on" — this is a constraint. When L is off, E is locked. This gates the excitation.
Add more constraints: E gates T, T gates F, F gates P. Each constraint depends on the previous. The system now has a pathway.
Add another rule: "I and E can never both be on." This creates mutual exclusion. The system can now make decisions.
The constraints lock into 45 valid states. Within those states, it discovers memory (each quantity remembers), logic gates (cascading pathways), oscillators (self-sustaining cycles), and stable attractors.
Every major neuromorphic chip on the market today requires engineers to hand-code neuron behavior. That's like building a brain one cell at a time.
Engineers design each neuron's behavior before fabrication. The computational properties are baked in, not emergent. Want a different neuron? Redesign it.
1,000-2,500 transistors per neuron. A million-neuron network needs billions of transistors. That limits density and drives up power and cost.
There's no systematic way to find which designs produce which computational behaviors. It's all trial and error. There was no framework — until now.
Instead of designing neurons, you declare constraints. The UCE automatically discovers every emergent behavior — without writing a single line of neuron code.
The Constraint-Driven Self-Emergent Neuron (CSEN) has seven binary quantities. Each maps to a biological neural function. Their interactions produce all computational behavior.
All computational richness comes from just three types of constraints applied to the seven quantities.
6 rules — AND conjunction
Define when a quantity may change. Multiple conditions must ALL be true. Create the gating network that chains quantities together into pathways.
8 rules — OR disjunction
Define when a quantity cannot change. ANY satisfied condition blocks the toggle. Create inhibitory constraints and safety locks.
4 rules — exclusive-or
Eliminate states where two quantities are both active. Create phase opposition (excitation vs. inhibition, firing vs. refractory).
The engine processes the 18 constraint rules over 7 quantities and discovers 18 distinct computational behaviors across four categories.
All seven quantities retain their value unless specific gating conditions unlock them. Each acts as an independent binary memory cell — 7 bits of per-neuron storage, with no flip-flops required.
A dual-pathway cascade emerges: excitation (L→E→T→F→P) and inhibition (L→R→I). Leak (L) acts as the master control for the entire neuron. Gating creates logic operations.
Four attractor cycles emerge as oscillatory patterns: potentiation oscillator, sensitization cycle, and more. These are self-sustaining excitation-learning feedback loops.
Nine stable equilibrium states form an attractor basin. The neuron naturally settles into predictable resting configurations, providing the foundation for reliable computation.
These behaviors are not programmed. They emerge from the constraint rules. This means the CSEN can discover computational properties that engineers never explicitly designed. Traditional neuromorphic chips do the opposite — every behavior must be hand-coded. UCE flips the paradigm: define rules, discover behaviors.
UCE is a fundamentally different architecture. Here's how it stacks against the state of the art.
| Feature | Conventional Chip A | Conventional Chip B | CSEN (Ours) |
|---|---|---|---|
| Neuron Design | Hand-programmed | Fixed LIF model | Emergent from constraints |
| Transistors/Neuron | ~2,300 | ~5,400 | Target: < 100 |
| Per-Neuron Memory | Configurable | Limited | 7 bits intrinsic |
| Behavior Discovery | Manual design | Manual design | Automatic enumeration |
| Learning Rule | Programmable STDP | External | Emergent (potentiation) |
| Memory Scaling | Linear | Fixed | 3N-1 bits (N neurons) |
| How It Works | Engineer programs each neuron | Engineer programs each neuron | Engineer defines rules, behaviors emerge |
Patent applications filed with the United States Patent and Trademark Office, establishing priority dates for the core UCE technology stack.
Application No. 64/036,854
System and method for discovering emergent behaviors from declarative constraint rules over conserved quantities.
Application No. 64/039,741
Constraint-driven self-emergent neuron architecture for neuromorphic computing with 7-quantity, 18-rule design.
Our technical paper presents the UCE architecture, worked examples, and implications for the future of neuromorphic computing.
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