# RuVector Nervous System Architecture
## Executive Summary
The RuVector Nervous System is a biologically-inspired, event-driven cognitive architecture built on top of the RuVector vector database ecosystem. It combines modern neuroscience principles with high-performance Rust implementations to create a scalable, modular system for real-time pattern recognition, associative memory, and adaptive learning.
## Table of Contents
- [Crate Layout](#crate-layout)
- [Module Dependency Graph](#module-dependency-graph)
- [Core Traits](#core-traits)
- [Data Structures](#data-structures)
- [Module Specifications](#module-specifications)
- [Integration with Existing Crates](#integration-with-existing-crates)
- [Feature Flags](#feature-flags)
- [Cargo Configuration](#cargo-configuration)
---
## Crate Layout
```
crates/ruvector-nervous-system/
├── Cargo.toml
├── src/
│ ├── lib.rs # Public API and re-exports
│ ├── traits.rs # Core trait definitions
│ ├── types.rs # Shared data structures
│ ├── error.rs # Error types
│ │
│ ├── eventbus/ # Event-driven communication layer
│ │ ├── mod.rs
│ │ ├── bounded_queue.rs # Fixed-size circular buffers
│ │ ├── region_shard.rs # Regional event partitioning
│ │ ├── backpressure.rs # Flow control mechanisms
│ │ └── priority.rs # Priority-based routing
│ │
│ ├── temporal/ # Temporal sequence processing
│ │ ├── mod.rs
│ │ ├── fingerprint.rs # Sequence fingerprinting
│ │ ├── sliding_window.rs # Temporal windowing
│ │ └── spike_timing.rs # Spike-timing patterns
│ │
│ ├── hdc/ # Hyperdimensional computing
│ │ ├── mod.rs
│ │ ├── hypervector.rs # Binary hypervector operations
│ │ ├── encoding.rs # Feature binding and bundling
│ │ ├── similarity.rs # Hamming distance and similarity
│ │ └── algebra.rs # HD algebra operations
│ │
│ ├── compete/ # Competitive learning
│ │ ├── mod.rs
│ │ ├── kwta.rs # k-Winner-Take-All
│ │ ├── lateral_inhibition.rs # Inhibitory connections
│ │ └── sparse_coding.rs # Sparse representation
│ │
│ ├── hopfield/ # Modern Hopfield networks
│ │ ├── mod.rs
│ │ ├── associative.rs # Associative memory core
│ │ ├── energy.rs # Energy function computation
│ │ ├── update_rules.rs # Asynchronous/synchronous updates
│ │ └── capacity.rs # Memory capacity analysis
│ │
│ ├── separate/ # Pattern separation
│ │ ├── mod.rs
│ │ ├── encoder.rs # Orthogonalization encoder
│ │ ├── collision.rs # Collision detection
│ │ └── decorrelate.rs # Decorrelation mechanisms
│ │
│ ├── dendrite/ # Dendritic computation
│ │ ├── mod.rs
│ │ ├── compartment.rs # Multi-compartment units
│ │ ├── nonlinear.rs # Nonlinear integration
│ │ └── modulation.rs # Neuromodulation effects
│ │
│ ├── plasticity/ # Learning and adaptation
│ │ ├── mod.rs
│ │ ├── btsp.rs # Behavioral Timescale Synaptic Plasticity
│ │ ├── eprop.rs # e-prop (eligibility propagation)
│ │ ├── rstdp.rs # Reward-modulated STDP
│ │ ├── ewc.rs # Elastic Weight Consolidation
│ │ └── meta_learning.rs # Meta-learning algorithms
│ │
│ ├── routing/ # Cognitive routing and workspace
│ │ ├── mod.rs
│ │ ├── predictive_coding.rs # Predictive coding framework
│ │ ├── coherence.rs # Coherence-based routing
│ │ ├── workspace.rs # Global workspace theory
│ │ └── attention.rs # Attention mechanisms
│ │
│ └── cognitum/ # Cognitum integration layer
│ ├── mod.rs
│ ├── v0_adapter.rs # Cognitum v0 compatibility
│ ├── v1_adapter.rs # Cognitum v1 integration
│ └── bridge.rs # Cross-version bridge
│
├── examples/
│ ├── dvs_processing.rs # Event camera processing
│ ├── associative_memory.rs # Hopfield memory demo
│ ├── pattern_recognition.rs # End-to-end recognition
│ └── learning_demo.rs # Plasticity demonstration
│
├── benches/
│ ├── eventbus.rs
│ ├── hdc_encoding.rs
│ └── hopfield_retrieval.rs
│
└── tests/
├── integration/
│ ├── end_to_end.rs
│ └── cognitum_compat.rs
└── unit/
├── eventbus_tests.rs
├── hdc_tests.rs
└── plasticity_tests.rs
```
---
## Module Dependency Graph
```mermaid
graph TB
%% Core Foundation
TRAITS[traits.rs
Core Traits]
TYPES[types.rs
Data Structures]
ERROR[error.rs
Error Types]
%% Input Layer
EVENTBUS[eventbus/
Event Communication]
TEMPORAL[temporal/
Sequence Processing]
%% Encoding Layer
HDC[hdc/
Hyperdimensional Computing]
SEPARATE[separate/
Pattern Separation]
COMPETE[compete/
Competitive Learning]
%% Memory Layer
HOPFIELD[hopfield/
Associative Memory]
DENDRITE[dendrite/
Dendritic Computation]
%% Learning Layer
PLASTICITY[plasticity/
Learning Rules]
%% Routing Layer
ROUTING[routing/
Cognitive Routing]
%% Integration Layer
COGNITUM[cognitum/
Cognitum Integration]
%% External Dependencies
RUVCORE[ruvector-core
Vector Engine]
RUVCOLL[ruvector-collections
HNSW/Quantization]
RUVPG[ruvector-postgres
Persistence]
%% Dependencies
TRAITS --> TYPES
TYPES --> ERROR
EVENTBUS --> TRAITS
EVENTBUS --> TYPES
TEMPORAL --> EVENTBUS
TEMPORAL --> TRAITS
HDC --> TRAITS
HDC --> TYPES
SEPARATE --> HDC
SEPARATE --> TRAITS
COMPETE --> TRAITS
COMPETE --> SEPARATE
HOPFIELD --> TRAITS
HOPFIELD --> HDC
HOPFIELD --> RUVCOLL
DENDRITE --> TRAITS
DENDRITE --> TEMPORAL
PLASTICITY --> TRAITS
PLASTICITY --> HOPFIELD
PLASTICITY --> DENDRITE
ROUTING --> PLASTICITY
ROUTING --> HOPFIELD
ROUTING --> COMPETE
ROUTING --> TRAITS
COGNITUM --> ROUTING
COGNITUM --> PLASTICITY
COGNITUM --> HOPFIELD
COGNITUM --> EVENTBUS
%% External integrations
HOPFIELD --> RUVCORE
ROUTING --> RUVCORE
COGNITUM --> RUVPG
classDef coreModule fill:#e1f5ff,stroke:#0077be,stroke-width:2px
classDef inputModule fill:#fff4e1,stroke:#ff9800,stroke-width:2px
classDef encodingModule fill:#e8f5e9,stroke:#4caf50,stroke-width:2px
classDef memoryModule fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px
classDef learningModule fill:#fff3e0,stroke:#ff6f00,stroke-width:2px
classDef routingModule fill:#e0f2f1,stroke:#009688,stroke-width:2px
classDef integrationModule fill:#fce4ec,stroke:#e91e63,stroke-width:2px
classDef externalModule fill:#f5f5f5,stroke:#616161,stroke-width:2px
class TRAITS,TYPES,ERROR coreModule
class EVENTBUS,TEMPORAL inputModule
class HDC,SEPARATE,COMPETE encodingModule
class HOPFIELD,DENDRITE memoryModule
class PLASTICITY learningModule
class ROUTING routingModule
class COGNITUM integrationModule
class RUVCORE,RUVCOLL,RUVPG externalModule
```
---
## Core Traits
### Event Processing
```rust
/// Timestamped event with spatial coordinates
pub trait Event: Send + Sync {
/// Timestamp in microseconds
fn timestamp(&self) -> u64;
/// Spatial coordinates (x, y) if applicable
fn position(&self) -> Option<(u16, u16)>;
/// Event polarity or type
fn polarity(&self) -> bool;
/// Optional event metadata
fn metadata(&self) -> Option<&[u8]> {
None
}
}
/// Fast reactive pathway bypassing main processing
pub trait ReflexGate: Send + Sync {
type Input: Event;
type Output;
/// Process event with minimal latency (<1ms target)
fn process_reflex(&mut self, event: &Self::Input) -> Option;
/// Check if event should trigger reflex pathway
fn should_reflex(&self, event: &Self::Input) -> bool;
/// Reflex gate latency in microseconds
fn latency_us(&self) -> u64;
}
```
### Memory Operations
```rust
/// One-shot associative memory write
pub trait MemoryWrite: Send + Sync {
type Error;
/// Store association with single exposure
fn write_once(&mut self, key: K, value: V) -> Result<(), Self::Error>;
/// Batch write multiple associations
fn write_batch(&mut self, pairs: &[(K, V)]) -> Result<(), Self::Error>;
/// Memory capacity and current utilization
fn capacity(&self) -> (usize, usize); // (current, max)
}
/// Associative retrieval with partial cues
pub trait Retrieve: Send + Sync {
type Error;
/// Retrieve value from partial or noisy key
fn retrieve(&self, partial_key: &K, threshold: f32) -> Result