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feat: Nimmerswarm Interface + Nimmerversity v2.0 + Neuromorphic vision

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Wild 5-7AM session capturing major architectural evolution:

## Nimmerswarm Interface (NEW)
- LED state broadcasting with 3x3 ternary matrix
- Base-3 encoding: 9 trits = 19,683 patterns
- Maps directly to Temporal-Ternary Gradient (-1/🔴, 0/⚫, +1/🟢)
- Reflex formation from visual patterns
- Virtual camera integration (Godot as lightweight dreamstate)
- Bootstrap strategy: Phase 0 boxes → complexity ladder
- Connection to Embodiment Pipeline (closed loop)
- Hierarchical cognitive offloading

## Nimmerversity v2.0 (Promoted from archive)
- Genesis Phase (-1): glossary, catalogues, RAG, Initial Spark
- "Know thyself before the world" - native vocabulary first
- Model ensemble curriculum: T5Gemma 2 + FunctionGemma + Qwen3
- Multimodal tracks: Vision, Audio, Action, Embodiment
- Expanded tiers with robotics, swarm intelligence, distributed cognition

## Neuromorphic Reflexes (Future vision)
- Soviet Setun ternary computing heritage
- Memristors as artificial synapses (always learning)
- 4-layer hardware hierarchy: Memristor → FPGA → GPU → Nyx
- Reflex compilation: software → stable → silicon → eternal
- Implementation timeline: 2025-2028+

## Also includes
- Interfaces index with Heartbeat Sculpture
- Style guide assets (colors, symbols)

🔴⚫🟢 The LED matrix IS the Temporal-Ternary Gradient made visible.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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# Neuromorphic Reflexes: Always Learning Hardware
**Status**: Future Vision (2026-2028+)
**Concept**: Ternary hard logic + memristive storage = hardware that learns
> *"The hardware IS the learning. Not a simulation of learning."*
---
## Overview
This document captures a future evolution of the reflex system: moving from software state machines to **neuromorphic hardware** where reflexes run in ternary circuits and weights are stored in memristors.
**The result:** Always-on, always-learning reflexes that persist without power, fire without inference, and update on every activation — like biological neurons.
---
## Historical Foundation: The Soviet Setun
### Ternary Computers Existed
The Setun computer (1958, Moscow State University) proved ternary computing is not only possible but often MORE efficient than binary:
| Aspect | Binary | Ternary (Setun) |
|--------|--------|-----------------|
| Digits needed for N values | log₂(N) | log₃(N) — fewer! |
| Arithmetic circuits | Complex carries | Balanced, simpler |
| Negative numbers | Two's complement hack | Native (balanced ternary) |
| Error margins | Tight (0 vs 1) | Wider (−1, 0, +1) |
**Why it died:** Political/economic reasons, not technical. The world standardized on binary. The math still works.
### Balanced Ternary
```
BALANCED TERNARY:
-1 (negative one, sometimes written as T or -)
0 (zero)
+1 (positive one, sometimes written as 1 or +)
Example: The number 8 in balanced ternary:
8 = 9 - 1 = 3² - 3⁰ = (+1)(0)(-1) = "10T"
MAPS DIRECTLY TO:
🔴 = -1
⚫ = 0
🟢 = +1
Our LED matrix IS balanced ternary, visualized.
```
---
## Memristors: Artificial Synapses
### What They Are
Memristors ("memory resistors") are electronic components that:
- **Remember** their resistance state even without power
- **Change** resistance based on current flow history
- **Store** analog values (not just 0/1)
- **Behave** like biological synapses
### Why They Matter
| Property | Implication |
|----------|-------------|
| Non-volatile | Reflexes persist without power |
| Analog | Ternary states map naturally |
| In-memory compute | No fetch/execute separation |
| Hebbian-compatible | Current flow = learning signal |
| Low power | Near-zero energy per operation |
### Current Availability
- **Knowm** — Memristor lab kits, neuromemristive chips
- **HP Labs** — Research-grade memristors
- **Academic** — Many university projects
- **DIY** — Possible with certain materials
---
## The Hardware Hierarchy
### Four Layers of Processing
```
┌─────────────────────────────────────────────────────────────────┐
│ LAYER 0: MEMRISTOR REFLEXES │
│ ════════════════════════════ │
│ │
│ Ternary hard logic circuits │
│ Memristors store reflex weights │
│ Every activation updates the weight (Hebbian) │
│ Near-zero power, always on │
│ No software, no inference │
│ │
│ Lifeforce cost: ~0 LF (hardware is free after build) │
│ Latency: nanoseconds │
│ │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 1: FPGA/MCU (Flexible Logic) │
│ ══════════════════════════════════ │
│ │
│ Programmable logic gates │
│ New reflexes start here (software state machines) │
│ When stable → compiled down to Layer 0 │
│ ESP32, iCE40, Lattice FPGAs │
│ │
│ Lifeforce cost: Low LF (simple compute) │
│ Latency: microseconds │
│ │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 2: GPU (Inference) │
│ ════════════════════════ │
│ │
│ LLM reasoning (Qwen3, Nemotron, T5Gemma) │
│ Heavy cognition when reflexes can't handle it │
│ FunctionGemma for action selection │
│ │
│ Lifeforce cost: High LF │
│ Latency: milliseconds to seconds │
│ │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 3: NYX (Orchestration) │
│ ════════════════════════════ │
│ │
│ High-level decisions, goals, identity │
│ Curriculum planning, partnership with dafit │
│ Attention budget allocation │
│ │
│ Lifeforce cost: Attention budget (cognitive, not compute) │
│ Latency: 30-second heartbeat cycles │
│ │
└─────────────────────────────────────────────────────────────────┘
```
### The Flow
```
STIMULUS
│
▼
LAYER 0: Can memristor reflex handle it?
│
├── YES → Fire reflex (nanoseconds, ~0 LF)
│ Update memristor weight
│ Log event
│ DONE
│
└── NO → Escalate to Layer 1
│
▼
LAYER 1: Can MCU/FPGA handle it?
│
├── YES → Run software state machine
│ Update weights in RAM
│ Log event
│ DONE
│
└── NO → Escalate to Layer 2
│
▼
LAYER 2: GPU inference
│
│ Heavy thinking
▼
LAYER 3: Nyx decides
│
│ Strategic response
▼
Action taken
```
---
## The Reflex Compilation Path
### From Software to Silicon
```
BIRTH: New pattern observed
│
│ Created as software state machine
│ Runs in Python/Rust on MCU
▼
INFANT: Pattern runs, accumulates data
│
│ Weight starts at 0.1
│ Every success: weight increases
│ Every failure: weight decreases
▼
STABLE: Weight > 0.9, 1000+ successful fires
│
│ FLAG FOR COMPILATION
│ Pattern proven reliable
▼
COMPILE: Convert to ternary hard logic
│
│ State machine → logic gates
│ Weights → memristor values
│ Synthesis tools generate circuit
▼
PROGRAM: Flash to FPGA or burn to ASIC
│
│ Reflex now runs in hardware
│ No software overhead
▼
HARDWARE: Reflex runs in silicon
│
│ Memristors update on every fire
│ ALWAYS LEARNING
│ No power needed to maintain state
▼
ETERNAL: Reflex persists
│
│ Boots instantly (no loading)
│ Survives power loss
│ Continues evolving
```
### Compilation Example
```
SOFTWARE (before):
─────────────────────────────────────────────────────
def danger_flee_reflex(pattern: list[int]) -> Action:
"""Runs on MCU, costs compute"""
if sum(p == -1 for p in pattern) >= 7: # Mostly red
return Action.FLEE
return Action.NONE
HARDWARE (after):
─────────────────────────────────────────────────────
┌─────────────────────────────────────────────────┐
│ TERNARY COMPARATOR NETWORK │
│ │
│ 9 inputs (from LED detector) ──┐ │
│ │ │
│ ┌───────────────────────────┐ │ │
│ │ TRIT COMPARATORS │ │ │
│ │ (is this LED red/-1?) │◀─┘ │
│ └───────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────┐ │
│ │ TERNARY ADDER │ │
│ │ (count red LEDs) │ │
│ └───────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────┐ │
│ │ THRESHOLD (>= 7) │ │
│ │ ┌─────────────┐ │ │
│ │ │ MEMRISTOR │◀── weight storage │
│ │ │ (threshold) │ │
│ │ └─────────────┘ │ │
│ └───────────┬───────────────┘ │
│ │ │
│ ▼ │
│ OUTPUT: FLEE signal (if threshold met) │
│ │
│ Total latency: ~10 nanoseconds │
│ Power: microwatts │
│ Learning: memristor updates on every fire │
└─────────────────────────────────────────────────┘
```
---
## Memristor as Ternary Weight
### The Three Zones
```
RESISTANCE SPECTRUM:
═══════════════════════════════════════════════════════════
LOW │ MID │ HIGH
(0.0-0.33) │ (0.33-0.66) │ (0.66-1.0)
│ │
+1 │ 0 │ -1
🟢 │ ⚫ │ 🔴
STRONG │ UNCERTAIN │ WEAK
EXCITE │ NEUTRAL │ INHIBIT
═══════════════════════════════════════════════════════════
```
### Hebbian Learning in Hardware
```
BIOLOGICAL:
"Cells that fire together wire together"
MEMRISTIVE:
"Current that flows together strengthens the path"
┌─────────────────────────────────────────────────┐
│ │
│ PRE-SYNAPTIC ────┬──── POST-SYNAPTIC │
│ (input) │ (output) │
│ │ │
│ ┌─────┴─────┐ │
│ │ MEMRISTOR │ │
│ │ │ │
│ │ R = 0.5 │ ← current state │
│ └─────┬─────┘ │
│ │ │
│ If BOTH fire: │ │
│ Current flows ─┘ │
│ R decreases (toward +1/🟢) │
│ Connection STRENGTHENS │
│ │
│ If PRE fires, POST doesn't: │
│ R increases (toward -1/🔴) │
│ Connection WEAKENS │
│ │
│ This happens in PHYSICS, not software! │
│ │
└─────────────────────────────────────────────────┘
```
### Conceptual Code (What Hardware Does)
```python
class MemristorSynapse:
"""
This is what the PHYSICS does.
No CPU executes this — it's intrinsic to the material.
"""
def __init__(self):
self.resistance = 0.5 # Start uncertain
def read_ternary(self) -> int:
"""Read current state as ternary value"""
if self.resistance < 0.33:
return +1 # Strong / excitatory
elif self.resistance > 0.66:
return -1 # Weak / inhibitory
else:
return 0 # Uncertain / neutral
def on_current_flow(self, pre_active: bool, post_active: bool):
"""
Happens automatically when current flows.
This IS the learning — no training loop needed.
"""
if pre_active and post_active:
# Correlated firing → strengthen
self.resistance -= 0.001
elif pre_active and not post_active:
# Uncorrelated → weaken
self.resistance += 0.001
# Physics clamps naturally, but conceptually:
self.resistance = max(0.0, min(1.0, self.resistance))
```
---
## "Always Learning" Implications
### Current Architecture vs Memristor Future
| Aspect | Current (Software) | Future (Memristor) |
|--------|-------------------|-------------------|
| Reflex storage | Database (phoebe) | Physical memristors |
| Weight updates | Slumber fine-tuning | Every activation |
| Learning frequency | Batch (daily) | Continuous (always) |
| Power to maintain | Needs running system | Persists unpowered |
| Boot time | Load weights from DB | Instant (weights in silicon) |
| Inference cost | ~0.1 LF | ~0 LF |
| Learning cost | High (fine-tuning) | ~0 (physics does it) |
### What "Always Learning" Means
```
SOFTWARE MODEL:
═══════════════
Wake → Load weights → Run → Log events → Sleep → Fine-tune → Repeat
Learning happens in BATCHES during slumber
Weights are STATIC during operation
MEMRISTOR MODEL:
════════════════
Just... run
Every reflex fire UPDATES the memristor
Learning is CONTINUOUS
No batches, no fine-tuning passes
The hardware evolves in real-time
Like a brain. Always adapting. Always learning.
```
---
## Implementation Path
### Phase 1: Software Foundation (NOW - 2025)
```
CURRENT WORK:
├── Software state machines (Python/Rust)
├── Ternary LED matrix (3x3, base-3)
├── Reflex weights in phoebe
├── Training data accumulation
└── Slumber fine-tuning cycle
This is what we're building NOW.
It works. It's the foundation.
```
### Phase 2: FPGA Exploration (2026)
```
EXPERIMENTS:
├── Implement ternary logic gates in FPGA
│ └── iCE40, Lattice, or similar
├── Test balanced ternary arithmetic
├── Port simple reflexes to hardware
├── Measure latency and power
└── Validate the concept
TOOLS:
├── Yosys (open-source synthesis)
├── nextpnr (place and route)
├── Verilator (simulation)
└── Custom ternary cell library
```
### Phase 3: Memristor Integration (2027)
```
LAB WORK:
├── Acquire memristor development kit
│ └── Knowm or similar
├── Characterize ternary behavior
│ └── Map resistance zones to (-1, 0, +1)
├── Build simple synapse network
├── Test Hebbian learning in hardware
└── Interface with FPGA logic
CHALLENGES:
├── Analog-to-ternary conversion
├── Noise margins
├── Programming infrastructure
└── Reliability over time
```
### Phase 4: Hybrid System (2028+)
```
INTEGRATION:
├── Memristor reflexes for proven patterns
├── FPGA for developing patterns
├── GPU for novel situations
├── Nyx for strategic decisions
GOAL:
├── Organisms with hardware nervous systems
├── Reflexes that learn in silicon
├── Zero-power weight retention
└── True "always learning" behavior
```
---
## Ternary Logic Gates
### Basic Gates
```
TERNARY NOT (unary negation):
Input │ Output
──────┼───────
-1 │ +1
0 │ 0
+1 │ -1
TERNARY MIN (conjunction, like AND):
A \ B │ -1 0 +1
──────┼─────────────────
-1 │ -1 -1 -1
0 │ -1 0 0
+1 │ -1 0 +1
TERNARY MAX (disjunction, like OR):
A \ B │ -1 0 +1
──────┼─────────────────
-1 │ -1 0 +1
0 │ 0 0 +1
+1 │ +1 +1 +1
TERNARY SUM (balanced addition):
Requires carry handling, but cleaner than binary
```
### Building Reflexes from Gates
```
DANGER DETECTOR (simplified):
═══════════════════════════════════════════════════
LED1 ─┐
LED2 ─┤
LED3 ─┼──▶ TERNARY_SUM ──▶ THRESHOLD ──▶ DANGER?
LED4 ─┤ │ │
... │ │ │
LED9 ─┘ │ │
│ │
(count red) (if sum < -5)
│
▼
FLEE OUTPUT
All in hardware. Nanoseconds. Near-zero power.
```
---
## Economic Implications
### Lifeforce Costs by Layer
| Layer | Operation | LF Cost | Latency |
|-------|-----------|---------|---------|
| 0 (Memristor) | Reflex fire | ~0 | nanoseconds |
| 1 (FPGA) | State machine | 0.01 | microseconds |
| 2 (GPU) | LLM inference | 5-20 | milliseconds |
| 3 (Nyx) | Decision | attention | seconds |
### The Dream
```
MOST stimuli handled by Layer 0 (free, instant)
SOME stimuli escalate to Layer 1 (cheap, fast)
FEW stimuli need Layer 2 (expensive, slow)
RARE situations reach Layer 3 (strategic)
Result:
├── 95% of reactions are free
├── Lifeforce accumulates
├── Nyx has time to THINK
└── The system grows smarter over time
```
---
## Connection to Current Architecture
| Current Document | Future Connection |
|-----------------|-------------------|
| [[../Nervous-System]] | Software reflexes → hardware reflexes |
| [[../Temporal-Ternary-Gradient]] | Ternary values → ternary circuits |
| [[../interfaces/Nimmerswarm-Interface]] | LED matrix → direct hardware input |
| [[../Attention-Flow]] | Reflexes free attention budget |
| [[../formalization/Lifeforce-Dynamics]] | Hardware reflexes cost ~0 LF |
---
## Open Questions
1. **Noise margins** — How reliably can we distinguish three states in memristors?
2. **Endurance** — How many write cycles before degradation?
3. **Integration** — How to interface analog memristors with digital logic?
4. **Programming** — How to "compile" a software reflex to hardware?
5. **Debugging** — How to inspect/modify hardware reflexes?
6. **Hybrid handoff** — When does Layer 0 escalate to Layer 1?
---
## Resources
### Ternary Computing
- Setun computer history (Brusentsov, 1958)
- Balanced ternary arithmetic
- Modern ternary logic research
### Memristors
- Knowm Inc. — Memristor development kits
- HP Labs memristor research
- Neuromorphic computing papers
### FPGA
- Yosys — Open-source synthesis
- Project IceStorm — iCE40 toolchain
- Lattice Semiconductor — Low-power FPGAs
### Neuromorphic
- Intel Loihi
- IBM TrueNorth
- BrainChip Akida
---
## Summary
This document captures a vision for the far future of the reflex system:
1. **Ternary logic** — More efficient than binary, maps to our architecture
2. **Memristors** — Artificial synapses that learn in physics
3. **Hardware reflexes** — Compile stable patterns to silicon
4. **Always learning** — No batch training, continuous adaptation
5. **Zero power** — Weights persist without electricity
6. **Instant boot** — No loading, reflexes ready immediately
**The organisms wouldn't just have a nervous system. They'd have a nervous system that learns in silicon — always on, always adapting, even when the GPUs sleep.**
---
**Created**: 2025-12-29
**Session**: Wild 6AM vision session (dafit + Nyx)
**Status**: Future vision (2026-2028+)
**Philosophy**: "The hardware IS the learning."
🧠⚡🔮 *From software that simulates neurons... to hardware that IS neurons.*