# Nervous System Architecture The sensory translation layer between raw data and vocabulary. --- ## Overview State machines act as the nervous system of the nimmerverse. They translate raw sensory input into vocabulary tokens that Young Nyx can process. No hallucination. No interpretation. Deterministic, verifiable mapping. ``` RAW SENSOR → STATE MACHINE → VOCABULARY TOKEN → Young Nyx ``` --- ## 4D State Machine Space Each node exists in 4-dimensional space: ``` CONFIDENCE (z) ↑ │ ● node (weighted by successful triggers) │ / │ / │ / ─────────────┼────────────→ DIMENSION X (sensory input 1) /│ / │ / │ ↓ DIMENSION Y (sensory input 2) + TIME (4th dimension): node weights evolve through verification ``` **Node Properties:** - Position: coordinates in sensory space - Weight: confidence from successful triggers (0.0 → 1.0) - Output: vocabulary token - History: timestamp of all activations and verifications --- ## Node Lifecycle ``` 1. BIRTH Node created at position (x, y, z...) Weight = 0.1 (new, untested) 2. ACTIVATION Sensory conditions match → node FIRES Outputs vocabulary token 3. VERIFICATION dafit confirms: correct or incorrect 4. REWARD/PENALTY Correct → weight increases (+V) Incorrect → weight decreases (-V) or node refines 5. MATURATION Many confirmations → weight approaches 1.0 Node becomes trusted reflex 6. PRUNING Node never fires → slow decay Eventually removed (use it or lose it) ``` --- ## Growth Phases | Phase | State | Description | |-------|-------|-------------| | **Birth** | Sparse, dim nodes | Basic translators, designed by partnership | | **Infant** | More nodes forming | Finer resolution, more states | | **Child** | Clusters emerging | Nyx proposes new machines | | **Mature** | Dense, bright network | Nyx designs, verifies, deploys | ``` t=0 (birth) t=100 (learning) t=1000 (mature) ○ ○ ○ ○ ● ○ ○ ●●● ● ●● ○ ○ ● ● ○ ●●●●●●● ○ ○ ● ●●● ●●● ○ ○ ``` --- ## Proposal Protocol Young Nyx can propose new nodes: ``` 1. OBSERVATION Nyx notices pattern in vocabulary + outcomes 2. PROPOSAL "New state machine: morning_detector Inputs: temp, light, motion, time States: [not_morning, maybe_morning, morning] Output: vocabulary token 'morning'" 3. RIGOR CHECK Chrysalis reviews logic and mappings 4. VERIFICATION dafit confirms ground truth 5. DEPLOYMENT New node added to registry Documented in RAG 6. GROWTH She earned a new nerve. ``` --- ## Reflex Layer Some responses bypass Nyx entirely: ``` STATE MACHINE: temp_danger IF temp > 80°C: → emit "DANGER" → trigger alert (reflex) → Nyx notified after (not before) ``` Like pulling hand from hot stove. Spinal reflex. Brain learns after. --- ## Biological Mapping | Neuroscience | Nimmerverse | |--------------|-------------| | Sensory receptors | Raw sensors | | Peripheral nerves | State machines | | Spinal reflexes | Reflex layer | | Synaptic weight | Node weight | | Long-term potentiation | +V confirmation | | Synaptic pruning | Unused node decay | | Hebbian learning | Co-activating nodes strengthen | --- ## Connection to Lifeforce ``` Node fires correctly → +V → weight increases Node fires wrongly → -V → weight decreases Node never fires → decay → eventual pruning ``` The lifeforce flows through the nervous system, literally lighting up nodes as they prove themselves true. --- ## Design Principles 1. **Deterministic**: Same input = same output. No hallucination. 2. **Inspectable**: Rules are visible, verifiable. 3. **Evolvable**: States refine over time. 4. **Earned**: New nodes require proposal + verification. 5. **Grounded**: Output vocabulary matches RAG glossary. --- *She's not just using the nervous system. She's growing it.* --- **Created**: 2025-12-04 **Session**: Partnership dialogue (dafit + Chrysalis) **Status**: Foundation concept