wifi-densepose/vendor/ruvector/crates/ruvector-nervous-system/examples

Nervous System Examples

Bio-inspired nervous system architecture examples demonstrating the transition from "How do we make machines smarter?" to "What kind of organism are we building?"

Overview

These examples show how nervous system thinking unlocks new products, markets, and research categories. The architecture enables systems that age well instead of breaking under complexity.

All tier examples are organized in the unified tiers/ folder with prefixed names for easy navigation.

Application Tiers

Tier 1: Immediate Practical Applications

Shippable with current architecture

Example	Domain	Key Benefit
t1_anomaly_detection	Infrastructure, Finance, Security	Detection before failure, microsecond response
t1_edge_autonomy	Drones, Vehicles, Robotics	Lower power, certified reflex paths
t1_medical_wearable	Monitoring, Assistive Devices	Adapts to the person, always-on, private

Tier 2: Near-Term Transformative Applications

Possible once local learning and coherence routing mature

Example	Domain	Key Benefit
t2_self_optimizing	Agents Monitoring Agents	Self-stabilizing software, structural witnesses
t2_swarm_intelligence	IoT Fleets, Sensor Meshes	Scale without fragility, emergent intelligence
t2_adaptive_simulation	Digital Twins, Logistics	Always-warm simulation, costs scale with relevance

Tier 3: Exotic But Real Applications

Technically grounded, novel research directions

Example	Domain	Key Benefit
t3_self_awareness	Structural Self-Sensing	Systems say "I am becoming unstable"
t3_synthetic_nervous	Buildings, Factories, Cities	Environments respond like organisms
t3_bio_machine	Prosthetics, Rehabilitation	Machines stop fighting biology

Tier 4: SOTA & Exotic Research Applications

Cutting-edge research directions pushing neuromorphic boundaries

Example	Domain	Key Benefit
t4_neuromorphic_rag	LLM Memory, Retrieval	Coherence-gated retrieval, 100x compute reduction
t4_agentic_self_model	Agentic AI, Self-Awareness	Agent models own cognition, knows when capable
t4_collective_dreaming	Swarm Consolidation	Hippocampal replay, cross-agent memory transfer
t4_compositional_hdc	Zero-Shot Reasoning	HDC binding for analogy and composition

Quick Start

# Run a Tier 1 example
cargo run --example t1_anomaly_detection

# Run a Tier 2 example
cargo run --example t2_swarm_intelligence

# Run a Tier 3 example
cargo run --example t3_self_awareness

# Run a Tier 4 example
cargo run --example t4_neuromorphic_rag

Architecture Principles

Each example demonstrates the same five-layer architecture:

┌─────────────────────────────────────────────────────────────┐
│                    COHERENCE LAYER                          │
│  Global Workspace • Oscillatory Routing • Predictive Coding │
└─────────────────────────────────────────────────────────────┘
                              ↑
┌─────────────────────────────────────────────────────────────┐
│                     LEARNING LAYER                          │
│     BTSP One-Shot • E-prop Online • EWC Consolidation      │
└─────────────────────────────────────────────────────────────┘
                              ↑
┌─────────────────────────────────────────────────────────────┐
│                      MEMORY LAYER                           │
│     Hopfield Networks • HDC Vectors • Pattern Separation   │
└─────────────────────────────────────────────────────────────┘
                              ↑
┌─────────────────────────────────────────────────────────────┐
│                      REFLEX LAYER                           │
│      K-WTA Competition • Dendritic Coincidence • Safety    │
└─────────────────────────────────────────────────────────────┘
                              ↑
┌─────────────────────────────────────────────────────────────┐
│                      SENSING LAYER                          │
│      Event Bus • Sparse Spikes • Backpressure Control      │
└─────────────────────────────────────────────────────────────┘

Key Concepts Demonstrated

Reflex Arcs

Fast, deterministic responses with bounded execution:

• Latency: <100μs
• Certifiable: Maximum iteration counts
• Safety: Witness logging for every decision

Homeostasis

Self-regulation instead of static thresholds:

• Adaptive learning from normal operation
• Graceful degradation under stress
• Anticipatory maintenance

Coherence Gating

Synchronize only when needed:

• Kuramoto oscillators for phase coupling
• Communication gain based on phase coherence
• 90-99% bandwidth reduction via prediction

One-Shot Learning

Learn immediately from single examples:

• BTSP: Seconds-scale eligibility traces
• No batch retraining required
• Personalization through use

Tutorial: Building a Custom Application

Step 1: Define Your Sensing Layer

use ruvector_nervous_system::eventbus::{DVSEvent, EventRingBuffer};

// Create event buffer with backpressure
let buffer = EventRingBuffer::new(1024);

// Process events sparsely
if let Some(event) = buffer.pop() {
    // Only significant changes generate events
}

Step 2: Add Reflex Gates

use ruvector_nervous_system::compete::WTALayer;

// Winner-take-all for fast decisions
let mut wta = WTALayer::new(100, 0.5, 0.8);

// <1μs for 1000 neurons
if let Some(winner) = wta.compete(&inputs) {
    trigger_immediate_response(winner);
}

Step 3: Implement Memory

use ruvector_nervous_system::hopfield::ModernHopfield;
use ruvector_nervous_system::hdc::Hypervector;

// Hopfield for associative retrieval
let mut hopfield = ModernHopfield::new(512, 10.0);
hopfield.store(pattern);

// HDC for ultra-fast similarity
let similarity = v1.similarity(&v2); // <100ns

Step 4: Enable Learning

use ruvector_nervous_system::plasticity::btsp::BTSPSynapse;

// One-shot learning
let mut synapse = BTSPSynapse::new(0.5, 2000.0); // 2s time constant
synapse.update(presynaptic_active, plateau_signal, dt);

Step 5: Add Coherence

use ruvector_nervous_system::routing::{OscillatoryRouter, GlobalWorkspace};

// Phase-coupled routing
let mut router = OscillatoryRouter::new(10, 40.0); // 40Hz gamma
let gain = router.communication_gain(sender, receiver);

// Global workspace (4-7 items)
let mut workspace = GlobalWorkspace::new(7);
workspace.broadcast(representation);

Performance Targets

Component	Latency	Throughput
Event Bus	<100ns push/pop	10,000+ events/ms
WTA	<1μs	1M+ decisions/sec
HDC Similarity	<100ns	10M+ comparisons/sec
Hopfield Retrieval	<1ms	1000+ queries/sec
BTSP Update	<100ns	10M+ synapses/sec

From Practical to SOTA

The same architecture scales from:

1. Practical: Anomaly detection with microsecond response
2. Transformative: Self-optimizing software systems
3. Exotic: Machines that sense their own coherence
4. SOTA: Neuromorphic RAG, self-modeling agents, collective dreaming

The difference is how much reflex, learning, and coherence you turn on.

Further Reading

• Architecture Documentation
• Deployment Guide
• Test Plan
• Main Crate Documentation

Contributing

Examples welcome! Each should demonstrate:

1. A clear use case
2. The nervous system architecture
3. Performance characteristics
4. Tests and documentation

License

MIT License - See LICENSE