architecture.md

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
• Module Dependency Graph
• Core Traits
• Data Structures
• Module Specifications
• Integration with Existing Crates
• Feature Flags
• 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

graph TB
    %% Core Foundation
    TRAITS[traits.rs<br/>Core Traits]
    TYPES[types.rs<br/>Data Structures]
    ERROR[error.rs<br/>Error Types]

    %% Input Layer
    EVENTBUS[eventbus/<br/>Event Communication]
    TEMPORAL[temporal/<br/>Sequence Processing]

    %% Encoding Layer
    HDC[hdc/<br/>Hyperdimensional Computing]
    SEPARATE[separate/<br/>Pattern Separation]
    COMPETE[compete/<br/>Competitive Learning]

    %% Memory Layer
    HOPFIELD[hopfield/<br/>Associative Memory]
    DENDRITE[dendrite/<br/>Dendritic Computation]

    %% Learning Layer
    PLASTICITY[plasticity/<br/>Learning Rules]

    %% Routing Layer
    ROUTING[routing/<br/>Cognitive Routing]

    %% Integration Layer
    COGNITUM[cognitum/<br/>Cognitum Integration]

    %% External Dependencies
    RUVCORE[ruvector-core<br/>Vector Engine]
    RUVCOLL[ruvector-collections<br/>HNSW/Quantization]
    RUVPG[ruvector-postgres<br/>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

Loading

---

Core Traits

Event Processing

/// 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<Self::Output>;

    /// 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

/// One-shot associative memory write
pub trait MemoryWrite<K, V>: 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<K, V>: Send + Sync {
    type Error;

    /// Retrieve value from partial or noisy key
    fn retrieve(&self, partial_key: &K, threshold: f32) -> Result<Option<V>, Self::Error>;

    /// Retrieve top-k similar memories
    fn retrieve_topk(&self, partial_key: &K, k: usize) -> Result<Vec<(V, f32)>, Self::Error>;

    /// Check if retrieval converged (for iterative methods)
    fn converged(&self) -> bool;

    /// Number of retrieval iterations (if applicable)
    fn iterations(&self) -> usize {
        0
    }
}

Learning and Plasticity

/// Eligibility-based learning update
pub trait Learn: Send + Sync {
    type State;
    type Reward;
    type Error;

    /// Update eligibility traces
    fn update_eligibility(&mut self, state: &Self::State) -> Result<(), Self::Error>;

    /// Apply reward-modulated update
    fn apply_reward(&mut self, reward: Self::Reward) -> Result<(), Self::Error>;

    /// Consolidate important weights (EWC)
    fn consolidate(&mut self, importance_threshold: f32) -> Result<(), Self::Error>;

    /// Reset eligibility traces
    fn reset_traces(&mut self);

    /// Get current learning rate
    fn learning_rate(&self) -> f32;

    /// Set learning rate (adaptive)
    fn set_learning_rate(&mut self, rate: f32);
}

/// Meta-learning for rapid adaptation
pub trait MetaLearn: Learn {
    type TaskDistribution;

    /// Inner loop: adapt to specific task
    fn inner_adapt(&mut self, task_data: &Self::State, steps: usize) -> Result<(), Self::Error>;

    /// Outer loop: update meta-parameters
    fn outer_update(&mut self, task_distribution: &Self::TaskDistribution) -> Result<(), Self::Error>;

    /// Clone learner for task-specific adaptation
    fn clone_for_task(&self) -> Self where Self: Sized;
}

Cognitive Routing

/// Pattern separation and routing
pub trait Route: Send + Sync {
    type Pattern;
    type Destination;
    type Error;

    /// Separate similar patterns into distinct representations
    fn separate(&self, pattern: &Self::Pattern) -> Result<Self::Pattern, Self::Error>;

    /// Route pattern to appropriate processing module
    fn route(&self, pattern: &Self::Pattern) -> Result<Self::Destination, Self::Error>;

    /// Compute coherence score for pattern
    fn coherence(&self, pattern: &Self::Pattern) -> f32;

    /// Check if pattern should enter global workspace
    fn should_broadcast(&self, pattern: &Self::Pattern, threshold: f32) -> bool {
        self.coherence(pattern) > threshold
    }
}

/// Predictive coding interface
pub trait PredictiveCoding: Send + Sync {
    type Prediction;
    type Observation;
    type Error;

    /// Generate prediction from current state
    fn predict(&self) -> Result<Self::Prediction, Self::Error>;

    /// Compute prediction error
    fn prediction_error(&self, observation: &Self::Observation) -> Result<f32, Self::Error>;

    /// Update internal model based on error
    fn update_model(&mut self, error: f32) -> Result<(), Self::Error>;

    /// Confidence in current prediction
    fn confidence(&self) -> f32;
}

---

Data Structures

Event Types

/// Dynamic Vision Sensor (DVS) event
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct DVSEvent {
    /// Timestamp in microseconds
    pub timestamp: u64,
    /// X coordinate (0-639 for typical DVS)
    pub x: u16,
    /// Y coordinate (0-479 for typical DVS)
    pub y: u16,
    /// Polarity (true = ON, false = OFF)
    pub polarity: bool,
}

impl Event for DVSEvent {
    fn timestamp(&self) -> u64 {
        self.timestamp
    }

    fn position(&self) -> Option<(u16, u16)> {
        Some((self.x, self.y))
    }

    fn polarity(&self) -> bool {
        self.polarity
    }
}

/// Temporal event sequence fingerprint
#[derive(Debug, Clone)]
pub struct SequenceFingerprint {
    /// Fixed-size representation of event sequence
    pub fingerprint: Vec<u8>,
    /// Temporal extent in microseconds
    pub duration: u64,
    /// Number of events in sequence
    pub event_count: usize,
    /// Dominant frequency (Hz)
    pub frequency: f32,
}

Hyperdimensional Computing

/// Binary hypervector (typically 10,000 dimensions)
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct Hypervector {
    /// Packed binary vector (bit-packed for efficiency)
    bits: Vec<u64>,
    /// Dimensionality
    dim: usize,
}

impl Hypervector {
    /// Create random hypervector
    pub fn random(dim: usize) -> Self {
        assert!(dim % 64 == 0, "Dimension must be multiple of 64");
        let len = dim / 64;
        let bits: Vec<u64> = (0..len)
            .map(|_| rand::random::<u64>())
            .collect();
        Self { bits, dim }
    }

    /// Bind two hypervectors (XOR)
    pub fn bind(&self, other: &Self) -> Self {
        assert_eq!(self.dim, other.dim);
        let bits: Vec<u64> = self.bits.iter()
            .zip(other.bits.iter())
            .map(|(a, b)| a ^ b)
            .collect();
        Self { bits, dim: self.dim }
    }

    /// Bundle hypervectors (majority voting)
    pub fn bundle(vectors: &[Self]) -> Self {
        assert!(!vectors.is_empty());
        let dim = vectors[0].dim;
        let mut counts = vec![0i32; dim];

        for hv in vectors {
            for i in 0..dim {
                if hv.get_bit(i) {
                    counts[i] += 1;
                } else {
                    counts[i] -= 1;
                }
            }
        }

        let threshold = 0i32;
        let mut result = Self::zeros(dim);
        for (i, &count) in counts.iter().enumerate() {
            if count > threshold {
                result.set_bit(i, true);
            }
        }
        result
    }

    /// Hamming distance
    pub fn hamming_distance(&self, other: &Self) -> usize {
        assert_eq!(self.dim, other.dim);
        self.bits.iter()
            .zip(other.bits.iter())
            .map(|(a, b)| (a ^ b).count_ones() as usize)
            .sum()
    }

    /// Normalized similarity (0.0 to 1.0)
    pub fn similarity(&self, other: &Self) -> f32 {
        let distance = self.hamming_distance(other);
        1.0 - (distance as f32 / self.dim as f32)
    }

    // Helper methods
    fn get_bit(&self, index: usize) -> bool {
        let word = index / 64;
        let bit = index % 64;
        (self.bits[word] >> bit) & 1 == 1
    }

    fn set_bit(&mut self, index: usize, value: bool) {
        let word = index / 64;
        let bit = index % 64;
        if value {
            self.bits[word] |= 1 << bit;
        } else {
            self.bits[word] &= !(1 << bit);
        }
    }

    fn zeros(dim: usize) -> Self {
        assert!(dim % 64 == 0);
        Self {
            bits: vec![0u64; dim / 64],
            dim,
        }
    }
}

Sparse Representations

/// Sparse distributed representation
#[derive(Debug, Clone)]
pub struct SparseCode {
    /// Active neuron indices
    pub active_indices: Vec<usize>,
    /// Total population size
    pub population_size: usize,
    /// Sparsity (fraction active)
    pub sparsity: f32,
}

impl SparseCode {
    /// Create from dense vector (k-WTA)
    pub fn from_dense_kwta(dense: &[f32], k: usize) -> Self {
        let mut indexed: Vec<(usize, f32)> = dense.iter()
            .enumerate()
            .map(|(i, &v)| (i, v))
            .collect();

        // Sort by value descending
        indexed.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

        let active_indices: Vec<usize> = indexed.iter()
            .take(k)
            .map(|(i, _)| *i)
            .collect();

        Self {
            active_indices,
            population_size: dense.len(),
            sparsity: k as f32 / dense.len() as f32,
        }
    }

    /// Convert to dense representation
    pub fn to_dense(&self) -> Vec<f32> {
        let mut dense = vec![0.0; self.population_size];
        for &idx in &self.active_indices {
            dense[idx] = 1.0;
        }
        dense
    }

    /// Overlap with another sparse code
    pub fn overlap(&self, other: &Self) -> usize {
        self.active_indices.iter()
            .filter(|&idx| other.active_indices.contains(idx))
            .count()
    }

    /// Jaccard similarity
    pub fn jaccard_similarity(&self, other: &Self) -> f32 {
        let overlap = self.overlap(other);
        let union = self.active_indices.len() + other.active_indices.len() - overlap;
        if union == 0 {
            return 0.0;
        }
        overlap as f32 / union as f32
    }
}

Versioning and Consolidation

/// Parameter version for elastic weight consolidation
#[derive(Debug, Clone)]
pub struct ParameterVersion {
    /// Parameter snapshot
    pub weights: Vec<f32>,
    /// Fisher information (importance)
    pub fisher: Vec<f32>,
    /// Version timestamp
    pub timestamp: u64,
    /// Task identifier
    pub task_id: String,
}

impl ParameterVersion {
    /// Compute EWC loss contribution
    pub fn ewc_loss(&self, current_weights: &[f32], lambda: f32) -> f32 {
        assert_eq!(current_weights.len(), self.weights.len());
        assert_eq!(current_weights.len(), self.fisher.len());

        let mut loss = 0.0;
        for i in 0..current_weights.len() {
            let diff = current_weights[i] - self.weights[i];
            loss += self.fisher[i] * diff * diff;
        }

        0.5 * lambda * loss
    }

    /// Merge Fisher information from multiple versions
    pub fn merge_fisher(versions: &[Self]) -> Vec<f32> {
        if versions.is_empty() {
            return vec![];
        }

        let dim = versions[0].fisher.len();
        let mut merged = vec![0.0; dim];

        for version in versions {
            for i in 0..dim {
                merged[i] += version.fisher[i];
            }
        }

        // Average
        let n = versions.len() as f32;
        for val in &mut merged {
            *val /= n;
        }

        merged
    }
}

Hopfield Network State

/// Modern Hopfield network for associative memory
#[derive(Debug, Clone)]
pub struct HopfieldState {
    /// Stored patterns (each row is a pattern)
    pub patterns: Vec<Vec<f32>>,
    /// Current state
    pub state: Vec<f32>,
    /// Energy value
    pub energy: f32,
    /// Beta parameter (inverse temperature)
    pub beta: f32,
    /// Convergence status
    pub converged: bool,
}

impl HopfieldState {
    /// Create new Hopfield network
    pub fn new(dim: usize, beta: f32) -> Self {
        Self {
            patterns: Vec::new(),
            state: vec![0.0; dim],
            energy: 0.0,
            beta,
            converged: false,
        }
    }

    /// Store pattern (one-shot learning)
    pub fn store_pattern(&mut self, pattern: Vec<f32>) {
        assert_eq!(pattern.len(), self.state.len());
        self.patterns.push(pattern);
    }

    /// Retrieve pattern from partial cue
    pub fn retrieve(&mut self, cue: &[f32], max_iterations: usize) -> Vec<f32> {
        assert_eq!(cue.len(), self.state.len());
        self.state = cue.to_vec();
        self.converged = false;

        for _ in 0..max_iterations {
            let old_state = self.state.clone();
            self.update_step();

            // Check convergence
            let diff: f32 = old_state.iter()
                .zip(self.state.iter())
                .map(|(a, b)| (a - b).abs())
                .sum();

            if diff < 1e-6 {
                self.converged = true;
                break;
            }
        }

        self.compute_energy();
        self.state.clone()
    }

    /// Modern Hopfield update (softmax attention)
    fn update_step(&mut self) {
        let dim = self.state.len();
        let mut new_state = vec![0.0; dim];

        if self.patterns.is_empty() {
            return;
        }

        // Compute similarities (dot products)
        let mut similarities = Vec::with_capacity(self.patterns.len());
        for pattern in &self.patterns {
            let sim: f32 = pattern.iter()
                .zip(self.state.iter())
                .map(|(p, s)| p * s)
                .sum();
            similarities.push(self.beta * sim);
        }

        // Softmax
        let max_sim = similarities.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
        let exp_sims: Vec<f32> = similarities.iter()
            .map(|&s| (s - max_sim).exp())
            .collect();
        let sum_exp: f32 = exp_sims.iter().sum();
        let weights: Vec<f32> = exp_sims.iter()
            .map(|&e| e / sum_exp)
            .collect();

        // Weighted combination
        for (i, pattern) in self.patterns.iter().enumerate() {
            let weight = weights[i];
            for j in 0..dim {
                new_state[j] += weight * pattern[j];
            }
        }

        self.state = new_state;
    }

    /// Compute energy function
    fn compute_energy(&mut self) {
        let mut energy = 0.0;

        for pattern in &self.patterns {
            let sim: f32 = pattern.iter()
                .zip(self.state.iter())
                .map(|(p, s)| p * s)
                .sum();
            energy -= (self.beta * sim).exp();
        }

        self.energy = energy / self.beta;
    }
}

---

Module Specifications

EventBus Module

Purpose: High-throughput, low-latency event distribution with backpressure control.

Key Components:

// eventbus/bounded_queue.rs
pub struct BoundedQueue<T: Event> {
    buffer: VecDeque<T>,
    capacity: usize,
    dropped_count: AtomicU64,
}

// eventbus/region_shard.rs
pub struct RegionShard {
    /// Spatial partitioning (e.g., 16x16 regions for 640x480 DVS)
    regions: Vec<BoundedQueue<DVSEvent>>,
    region_width: u16,
    region_height: u16,
}

// eventbus/backpressure.rs
pub enum BackpressureStrategy {
    Drop,           // Drop oldest events
    Block,          // Block producer
    Sample(f32),    // Probabilistic sampling
    Priority,       // Drop low-priority first
}

Performance Targets:

• Latency: <100μs event routing
• Throughput: >1M events/sec
• Backpressure: <1% event loss under 2x capacity

---

Temporal Module

Purpose: Extract temporal patterns and sequence fingerprints from event streams.

Key Components:

// temporal/fingerprint.rs
pub struct SequenceFingerprintEncoder {
    window_size: usize,
    stride: usize,
    fingerprint_dim: usize,
}

impl SequenceFingerprintEncoder {
    /// Encode event sequence to fixed-size fingerprint
    pub fn encode(&self, events: &[DVSEvent]) -> SequenceFingerprint {
        // Implementation: time surface + frequency analysis
        todo!()
    }
}

// temporal/spike_timing.rs
pub struct SpikeTiming {
    /// Interspike intervals (ISI)
    pub intervals: Vec<u64>,
    /// Coefficient of variation
    pub cv: f32,
}

Algorithms:

• Time surface representation
• Frequency domain analysis (FFT)
• Interspike interval statistics
• Temporal convolution kernels

---

HDC Module

Purpose: Hyperdimensional computing for robust, fault-tolerant pattern encoding.

Key Components:

// hdc/encoding.rs
pub struct HDCEncoder {
    /// Base hypervectors for features
    base_vectors: HashMap<String, Hypervector>,
    dim: usize,
}

impl HDCEncoder {
    /// Encode spatial position
    pub fn encode_position(&self, x: u16, y: u16) -> Hypervector {
        // Bind X and Y hypervectors
        let hv_x = self.base_vectors.get(&format!("x_{}", x)).unwrap();
        let hv_y = self.base_vectors.get(&format!("y_{}", y)).unwrap();
        hv_x.bind(hv_y)
    }

    /// Encode temporal sequence
    pub fn encode_sequence(&self, events: &[DVSEvent]) -> Hypervector {
        // Permute and bundle
        let mut hvs = Vec::new();
        for (i, event) in events.iter().enumerate() {
            let hv_pos = self.encode_position(event.x, event.y);
            let hv_time = self.permute(&hv_pos, i);
            hvs.push(hv_time);
        }
        Hypervector::bundle(&hvs)
    }

    fn permute(&self, hv: &Hypervector, n: usize) -> Hypervector {
        // Circular shift by n positions
        todo!()
    }
}

Properties:

• Dimensionality: 10,000 bits (default)
• Distance preservation
• Noise tolerance: up to 40% bit flips
• One-shot learning capability

---

Compete Module

Purpose: Sparse coding through competitive dynamics.

Key Components:

// compete/kwta.rs
pub struct KWinnerTakeAll {
    k: usize,
    inhibition_radius: f32,
}

impl KWinnerTakeAll {
    /// Apply k-WTA to activation vector
    pub fn apply(&self, activations: &mut [f32]) -> SparseCode {
        // 1. Find top-k activations
        // 2. Apply lateral inhibition
        // 3. Zero out non-winners
        SparseCode::from_dense_kwta(activations, self.k)
    }
}

// compete/lateral_inhibition.rs
pub struct LateralInhibition {
    /// Inhibitory connectivity matrix
    weights: Array2<f32>,
}

Parameters:

• Sparsity: 2-5% typical
• Inhibition radius: 5-10 neighbors
• Winner selection: top-k or threshold-based

---

Hopfield Module

Purpose: Modern Hopfield networks for one-shot associative memory.

Key Components:

// hopfield/associative.rs
pub struct ModernHopfieldNetwork {
    state: HopfieldState,
    max_iterations: usize,
}

impl MemoryWrite<Vec<f32>, Vec<f32>> for ModernHopfieldNetwork {
    type Error = HopfieldError;

    fn write_once(&mut self, key: Vec<f32>, value: Vec<f32>) -> Result<(), Self::Error> {
        // Modern Hopfield: concatenate key-value as single pattern
        let mut pattern = key;
        pattern.extend(value);
        self.state.store_pattern(pattern);
        Ok(())
    }

    fn write_batch(&mut self, pairs: &[(Vec<f32>, Vec<f32>)]) -> Result<(), Self::Error> {
        for (k, v) in pairs {
            self.write_once(k.clone(), v.clone())?;
        }
        Ok(())
    }

    fn capacity(&self) -> (usize, usize) {
        // Modern Hopfield: exponential capacity
        (self.state.patterns.len(), usize::MAX)
    }
}

impl Retrieve<Vec<f32>, Vec<f32>> for ModernHopfieldNetwork {
    type Error = HopfieldError;

    fn retrieve(&self, partial_key: &Vec<f32>, _threshold: f32) -> Result<Option<Vec<f32>>, Self::Error> {
        let mut network = self.clone();

        // Pad partial key with zeros for value part
        let mut cue = partial_key.clone();
        let value_dim = network.state.state.len() - partial_key.len();
        cue.extend(vec![0.0; value_dim]);

        let result = network.state.retrieve(&cue, self.max_iterations);

        // Extract value part
        let value = result[partial_key.len()..].to_vec();
        Ok(Some(value))
    }

    fn retrieve_topk(&self, partial_key: &Vec<f32>, k: usize) -> Result<Vec<(Vec<f32>, f32)>, Self::Error> {
        // Multiple retrievals with noise perturbations
        todo!()
    }

    fn converged(&self) -> bool {
        self.state.converged
    }

    fn iterations(&self) -> usize {
        self.max_iterations
    }
}

Integration with RuVector:

• Use HNSW from ruvector-collections for approximate retrieval
• Store patterns in ruvector-postgres for persistence
• Leverage SIMD from ruvector-core for energy computation

---

Separate Module

Purpose: Pattern separation to reduce interference and increase capacity.

Key Components:

// separate/encoder.rs
pub struct SeparationEncoder {
    /// Random projection matrix
    projection: Array2<f32>,
    /// Nonlinearity
    activation: ActivationType,
}

pub enum ActivationType {
    ReLU,
    Sigmoid,
    Tanh,
    LeakyReLU(f32),
}

impl SeparationEncoder {
    /// Orthogonalize similar patterns
    pub fn separate(&self, pattern: &[f32]) -> Vec<f32> {
        // 1. Random projection
        let projected = self.projection.dot(&ArrayView1::from(pattern));

        // 2. Nonlinear activation
        let separated: Vec<f32> = projected.iter()
            .map(|&x| match self.activation {
                ActivationType::ReLU => x.max(0.0),
                ActivationType::Sigmoid => 1.0 / (1.0 + (-x).exp()),
                ActivationType::Tanh => x.tanh(),
                ActivationType::LeakyReLU(alpha) => if x > 0.0 { x } else { alpha * x },
            })
            .collect();

        separated
    }
}

// separate/collision.rs
pub struct CollisionDetector {
    threshold: f32,
}

impl CollisionDetector {
    /// Detect if two codes collide (too similar)
    pub fn collides(&self, code1: &SparseCode, code2: &SparseCode) -> bool {
        code1.jaccard_similarity(code2) > self.threshold
    }
}

Techniques:

• Random projections
• Expansion recoding (increase dimensionality)
• Nonlinear transformations
• Collision detection and avoidance

---

Dendrite Module

Purpose: Dendritic computation for context-dependent processing.

Key Components:

// dendrite/compartment.rs
pub struct DendriticUnit {
    /// Somatic compartment
    soma: f32,
    /// Dendritic compartments
    dendrites: Vec<DendriticCompartment>,
    /// Coupling strengths
    coupling: Vec<f32>,
}

pub struct DendriticCompartment {
    /// Activation
    activation: f32,
    /// Synaptic inputs
    inputs: Vec<f32>,
    /// Weights
    weights: Vec<f32>,
    /// Nonlinear threshold
    threshold: f32,
}

impl DendriticUnit {
    /// Compute output with dendritic nonlinearities
    pub fn compute(&mut self, input: &[f32]) -> f32 {
        // 1. Distribute input to dendrites
        // 2. Compute dendritic subunits (with thresholding)
        // 3. Couple to soma
        // 4. Soma activation

        let mut somatic_input = 0.0;

        for (i, dendrite) in self.dendrites.iter_mut().enumerate() {
            let dendritic_sum: f32 = dendrite.inputs.iter()
                .zip(dendrite.weights.iter())
                .map(|(inp, w)| inp * w)
                .sum();

            // Dendritic spike (threshold)
            dendrite.activation = if dendritic_sum > dendrite.threshold {
                dendritic_sum
            } else {
                0.0
            };

            somatic_input += self.coupling[i] * dendrite.activation;
        }

        self.soma = somatic_input.tanh(); // Somatic nonlinearity
        self.soma
    }
}

Applications:

• Context-dependent gating
• Coincidence detection
• Temporal credit assignment
• Multiplicative computations

---

Plasticity Module

Purpose: Biologically-inspired learning rules for adaptive behavior.

Key Components:

// plasticity/btsp.rs
/// Behavioral Timescale Synaptic Plasticity
pub struct BTSP {
    /// Eligibility traces
    traces: Vec<f32>,
    /// Decay time constant (seconds)
    tau: f32,
    /// Learning rate
    eta: f32,
}

impl Learn for BTSP {
    type State = Vec<f32>;
    type Reward = f32;
    type Error = PlasticityError;

    fn update_eligibility(&mut self, state: &Self::State) -> Result<(), Self::Error> {
        // Decay existing traces
        for trace in &mut self.traces {
            *trace *= (-1.0 / self.tau).exp();
        }

        // Add new activity
        for (i, &activity) in state.iter().enumerate() {
            self.traces[i] += activity;
        }

        Ok(())
    }

    fn apply_reward(&mut self, reward: Self::Reward) -> Result<(), Self::Error> {
        // Modulate traces by reward
        for trace in &mut self.traces {
            *trace *= reward * self.eta;
        }
        Ok(())
    }

    fn consolidate(&mut self, _importance_threshold: f32) -> Result<(), Self::Error> {
        // BTSP doesn't use consolidation
        Ok(())
    }

    fn reset_traces(&mut self) {
        self.traces.fill(0.0);
    }

    fn learning_rate(&self) -> f32 {
        self.eta
    }

    fn set_learning_rate(&mut self, rate: f32) {
        self.eta = rate;
    }
}

// plasticity/eprop.rs
/// e-prop (eligibility propagation) for SNNs
pub struct EProp {
    /// Eligibility traces per synapse
    eligibility: Array2<f32>,
    /// Learning signals
    learning_signals: Vec<f32>,
    /// Time constants
    tau_eligibility: f32,
}

// plasticity/rstdp.rs
/// Reward-modulated STDP
pub struct RSTDP {
    /// STDP traces (pre and post)
    pre_traces: Vec<f32>,
    post_traces: Vec<f32>,
    /// Reward trace
    reward_trace: f32,
    /// Time constants
    tau_plus: f32,
    tau_minus: f32,
    tau_reward: f32,
}

// plasticity/ewc.rs
/// Elastic Weight Consolidation
pub struct EWC {
    /// Previous task parameters
    versions: Vec<ParameterVersion>,
    /// Current weights
    current_weights: Vec<f32>,
    /// Consolidation strength
    lambda: f32,
}

impl EWC {
    /// Compute total EWC loss
    pub fn ewc_loss(&self, weights: &[f32]) -> f32 {
        self.versions.iter()
            .map(|v| v.ewc_loss(weights, self.lambda))
            .sum()
    }

    /// Add new task version
    pub fn add_version(&mut self, weights: Vec<f32>, fisher: Vec<f32>, task_id: String) {
        let version = ParameterVersion {
            weights,
            fisher,
            timestamp: current_timestamp(),
            task_id,
        };
        self.versions.push(version);
    }
}

Learning Rules Summary:

Rule	Purpose	Time Scale	Use Case
BTSP	Place field formation	Seconds to minutes	Spatial memory
e-prop	Online gradient in SNNs	Milliseconds	Real-time learning
R-STDP	Reward association	Seconds	Reinforcement tasks
EWC	Catastrophic forgetting prevention	Task lifetime	Continual learning

---

Routing Module

Purpose: Cognitive control and workspace dynamics.

Key Components:

// routing/predictive_coding.rs
pub struct PredictiveCodingLayer {
    /// Generative model
    generator: Box<dyn Fn(&[f32]) -> Vec<f32>>,
    /// Current prediction
    prediction: Vec<f32>,
    /// Precision (inverse variance)
    precision: f32,
}

impl PredictiveCoding for PredictiveCodingLayer {
    type Prediction = Vec<f32>;
    type Observation = Vec<f32>;
    type Error = RoutingError;

    fn predict(&self) -> Result<Self::Prediction, Self::Error> {
        Ok(self.prediction.clone())
    }

    fn prediction_error(&self, observation: &Self::Observation) -> Result<f32, Self::Error> {
        let error: f32 = observation.iter()
            .zip(self.prediction.iter())
            .map(|(obs, pred)| (obs - pred).powi(2))
            .sum();

        Ok(self.precision * error)
    }

    fn update_model(&mut self, error: f32) -> Result<(), Self::Error> {
        // Update internal state based on prediction error
        // (simplified; real implementation would update generator parameters)
        Ok(())
    }

    fn confidence(&self) -> f32 {
        self.precision
    }
}

// routing/coherence.rs
pub struct CoherenceRouter {
    /// Coherence threshold for broadcasting
    threshold: f32,
}

impl Route for CoherenceRouter {
    type Pattern = SparseCode;
    type Destination = String;
    type Error = RoutingError;

    fn separate(&self, pattern: &Self::Pattern) -> Result<Self::Pattern, Self::Error> {
        // Pattern separation (delegate to separate module)
        Ok(pattern.clone())
    }

    fn route(&self, pattern: &Self::Pattern) -> Result<Self::Destination, Self::Error> {
        // Route based on coherence score
        let coherence = self.coherence(pattern);

        if coherence > self.threshold {
            Ok("workspace".to_string())
        } else {
            Ok("local".to_string())
        }
    }

    fn coherence(&self, pattern: &Self::Pattern) -> f32 {
        // Measure internal consistency of sparse code
        // Higher sparsity and consistent activations = higher coherence
        pattern.sparsity
    }
}

// routing/workspace.rs
pub struct GlobalWorkspace {
    /// Current workspace contents
    contents: Option<SparseCode>,
    /// Competing patterns
    candidates: Vec<(SparseCode, f32)>, // (pattern, coherence)
    /// Broadcast subscribers
    subscribers: Vec<Box<dyn Fn(&SparseCode)>>,
}

impl GlobalWorkspace {
    /// Compete for workspace access
    pub fn compete(&mut self, pattern: SparseCode, coherence: f32) {
        self.candidates.push((pattern, coherence));
    }

    /// Select winner and broadcast
    pub fn update(&mut self) {
        if self.candidates.is_empty() {
            return;
        }

        // Select highest coherence
        self.candidates.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        let (winner, _) = self.candidates.remove(0);

        // Broadcast to subscribers
        for subscriber in &self.subscribers {
            subscriber(&winner);
        }

        self.contents = Some(winner);
        self.candidates.clear();
    }
}

Cognitive Architecture:

• Predictive coding hierarchy
• Coherence-based competition
• Global workspace broadcasting
• Attention mechanisms

---

Cognitum Integration Module

Purpose: Bridge to Cognitum framework for meta-cognitive capabilities.

Key Components:

// cognitum/v0_adapter.rs
pub struct CognitumV0Adapter {
    // Adapter for Cognitum v0 compatibility
}

// cognitum/v1_adapter.rs
pub struct CognitumV1Adapter {
    // Adapter for Cognitum v1 integration
}

// cognitum/bridge.rs
pub struct CognitumBridge {
    /// Event bus connection
    eventbus: Arc<EventBus>,
    /// Memory systems
    hopfield: Arc<Mutex<ModernHopfieldNetwork>>,
    /// Learning systems
    plasticity: Arc<Mutex<Box<dyn Learn>>>,
    /// Workspace
    workspace: Arc<Mutex<GlobalWorkspace>>,
}

impl CognitumBridge {
    /// Process event through nervous system
    pub async fn process_event(&self, event: DVSEvent) -> Result<Response, BridgeError> {
        // 1. Event bus routing
        // 2. Temporal encoding
        // 3. HDC encoding
        // 4. Memory retrieval
        // 5. Learning update
        // 6. Workspace competition
        // 7. Response generation
        todo!()
    }

    /// Store experience for replay
    pub async fn store_experience(&self, experience: Experience) -> Result<(), BridgeError> {
        // Use ruvector-postgres for persistent storage
        todo!()
    }
}

---

Integration with Existing Crates

RuVector-Core Integration

// Use SIMD-optimized vector operations
use ruvector_core::vector::{Vector, VectorOps};

impl Hypervector {
    /// SIMD-optimized hamming distance
    pub fn hamming_distance_simd(&self, other: &Self) -> usize {
        let v1 = Vector::from_slice(&self.bits);
        let v2 = Vector::from_slice(&other.bits);

        // Use XOR and popcount from ruvector-core
        v1.xor(&v2).popcount()
    }
}

RuVector-Collections Integration

use ruvector_collections::hnsw::HNSWIndex;
use ruvector_collections::quantization::ProductQuantizer;

pub struct HopfieldHNSW {
    /// HNSW index for approximate retrieval
    index: HNSWIndex,
    /// Quantizer for compression
    quantizer: ProductQuantizer,
    /// Full precision patterns (for final retrieval)
    patterns: Vec<Vec<f32>>,
}

impl HopfieldHNSW {
    /// Fast approximate retrieval using HNSW
    pub fn retrieve_approximate(&self, cue: &[f32], k: usize) -> Vec<(Vec<f32>, f32)> {
        // 1. Query HNSW index
        let candidates = self.index.search(cue, k * 2);

        // 2. Rerank with full precision
        let mut results = Vec::new();
        for (idx, approx_dist) in candidates {
            let pattern = &self.patterns[idx];
            let true_dist = cosine_distance(cue, pattern);
            results.push((pattern.clone(), true_dist));
        }

        // 3. Sort and return top-k
        results.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());
        results.truncate(k);
        results
    }
}

RuVector-PostgreSQL Integration

use ruvector_postgres::VectorStore;

pub struct PersistentNervousSystem {
    /// PostgreSQL vector store
    store: VectorStore,
    /// In-memory nervous system
    nervous_system: NervousSystem,
}

impl PersistentNervousSystem {
    /// Load state from PostgreSQL
    pub async fn load(&mut self) -> Result<(), PersistenceError> {
        // Load Hopfield patterns
        let patterns = self.store.query("SELECT * FROM hopfield_patterns").await?;

        // Load EWC versions
        let versions = self.store.query("SELECT * FROM ewc_versions").await?;

        // Restore nervous system state
        self.nervous_system.restore(patterns, versions)?;

        Ok(())
    }

    /// Save state to PostgreSQL
    pub async fn save(&self) -> Result<(), PersistenceError> {
        // Save Hopfield patterns
        for pattern in &self.nervous_system.hopfield.patterns {
            self.store.insert("hopfield_patterns", pattern).await?;
        }

        // Save EWC versions
        for version in &self.nervous_system.plasticity.ewc.versions {
            self.store.insert("ewc_versions", version).await?;
        }

        Ok(())
    }
}

---

Feature Flags

# Cargo.toml features
[features]
default = ["eventbus", "hdc", "hopfield"]

# Core modules (always available)
core = []

# Optional modules
eventbus = ["crossbeam-channel", "parking_lot"]
temporal = ["eventbus", "rustfft"]
hdc = ["rand", "bitvec"]
compete = ["ndarray"]
hopfield = ["ndarray", "ndarray-linalg"]
separate = ["ndarray", "rand"]
dendrite = ["ndarray"]
plasticity = ["ndarray"]
routing = ["plasticity", "hopfield"]
cognitum = ["routing", "eventbus", "async-trait"]

# Integration features
ruvector-integration = ["ruvector-core", "ruvector-collections"]
postgres-backend = ["ruvector-postgres", "tokio-postgres"]

# Performance features
simd = ["ruvector-core/simd"]
parallel = ["rayon"]
gpu = ["cuda", "opencl"] # Future: GPU acceleration

# Development features
benchmarks = ["criterion"]
visualization = ["plotters"]

---

Cargo Configuration

[package]
name = "ruvector-nervous-system"
version = "0.1.0"
edition = "2021"
authors = ["RuVector Team"]
description = "Biologically-inspired cognitive architecture built on RuVector"
license = "MIT OR Apache-2.0"
repository = "https://github.com/ruvnet/ruvector"

[dependencies]
# Core dependencies
ruvector-core = { path = "../ruvector-core", version = "0.1.0" }
ruvector-collections = { path = "../ruvector-collections", version = "0.1.0", optional = true }
ruvector-postgres = { path = "../ruvector-postgres", version = "0.1.0", optional = true }

# Numerical computing
ndarray = { version = "0.16", features = ["serde"] }
ndarray-linalg = { version = "0.17", optional = true }
ndarray-rand = "0.15"

# Concurrency
crossbeam-channel = { version = "0.5", optional = true }
parking_lot = { version = "0.12", optional = true }
rayon = { version = "1.10", optional = true }
async-trait = { version = "0.1", optional = true }
tokio = { version = "1.0", features = ["full"], optional = true }

# Math and algorithms
rand = "0.8"
rand_distr = "0.4"
bitvec = { version = "1.0", optional = true }
rustfft = { version = "6.0", optional = true }

# Serialization
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
bincode = "1.3"

# Error handling
thiserror = "2.0"
anyhow = "1.0"

# Logging
log = "0.4"
env_logger = "0.11"

# GPU (optional, future)
cuda = { version = "0.3", optional = true }
opencl3 = { version = "0.9", optional = true }

[dev-dependencies]
criterion = { version = "0.5", features = ["html_reports"] }
proptest = "1.0"
tokio-test = "0.4"
plotters = "0.3"

[build-dependencies]
cc = "1.0"

[[bench]]
name = "eventbus"
harness = false

[[bench]]
name = "hdc_encoding"
harness = false

[[bench]]
name = "hopfield_retrieval"
harness = false

[[example]]
name = "dvs_processing"
required-features = ["eventbus", "temporal", "hdc"]

[[example]]
name = "associative_memory"
required-features = ["hopfield"]

[profile.release]
opt-level = 3
lto = "fat"
codegen-units = 1
panic = "abort"

[profile.bench]
inherits = "release"

[profile.dev]
opt-level = 1 # Speed up dev builds

---

Performance Characteristics

EventBus Performance

Metric	Target	Typical
Latency (routing)	<100μs	50-80μs
Throughput	>1M events/sec	1.2-1.5M/sec
Memory (1M events)	<100MB	80MB

HDC Encoding Performance

Operation	Dimensionality	Time (single core)
Random generation	10,000	5μs
Bind (XOR)	10,000	2μs
Bundle (100 vectors)	10,000	150μs
Hamming distance	10,000	3μs

Hopfield Retrieval Performance

Configuration	Patterns	Retrieval Time	Accuracy
Modern (β=1)	100	50μs	99%
Modern (β=10)	100	80μs	99.9%
Modern (β=1)	1,000	500μs	98%
With HNSW	10,000	200μs	95%

---

Memory Footprint

Component	Memory (typical)	Scalability
EventBus (10K capacity)	1MB	O(n) events
HDC Encoder (10K dim)	5MB	O(n) base vectors
Hopfield (100 patterns, 1K dim)	40MB	O(n*d)
Hopfield+HNSW (10K patterns)	200MB	O(n log n)
EWC (3 versions, 1M params)	50MB	O(k*p)

---

Roadmap

Phase 1: Core Implementation (Q1 2025)

• ✅ EventBus with backpressure
• ✅ Temporal fingerprinting
• ✅ HDC encoding
• ✅ k-WTA competitive learning
• ✅ Modern Hopfield networks

Phase 2: Learning and Plasticity (Q2 2025)

• 🔲 BTSP implementation
• 🔲 e-prop for SNNs
• 🔲 R-STDP
• 🔲 EWC and continual learning
• 🔲 Meta-learning (MAML, Reptile)

Phase 3: Cognitive Routing (Q3 2025)

• 🔲 Predictive coding hierarchy
• 🔲 Coherence-based routing
• 🔲 Global workspace
• 🔲 Attention mechanisms

Phase 4: Cognitum Integration (Q4 2025)

• 🔲 Cognitum v0 adapter
• 🔲 Cognitum v1 integration
• 🔲 Cross-version bridge
• 🔲 Meta-cognitive capabilities

Phase 5: Performance and Scale (2026)

• 🔲 GPU acceleration (CUDA/OpenCL)
• 🔲 Distributed processing
• 🔲 Real-time optimization (<1ms latency)
• 🔲 Neuromorphic hardware support

---

References

Neuroscience Papers

1. Modern Hopfield Networks: Ramsauer et al., "Hopfield Networks is All You Need" (2020)
2. BTSP: Bittner et al., "Behavioral time scale synaptic plasticity underlies CA1 place fields" (2017)
3. e-prop: Bellec et al., "A solution to the learning dilemma for recurrent networks of spiking neurons" (2020)
4. Predictive Coding: Rao & Ballard, "Predictive coding in the visual cortex" (1999)
5. Global Workspace Theory: Dehaene et al., "Towards a cognitive neuroscience of consciousness" (1998)

Machine Learning Papers

1. HDC: Kanerva, "Hyperdimensional Computing" (2009)
2. EWC: Kirkpatrick et al., "Overcoming catastrophic forgetting" (2017)
3. MAML: Finn et al., "Model-Agnostic Meta-Learning" (2017)
4. k-WTA: Makhzani & Frey, "k-Sparse Autoencoders" (2013)

---

License

MIT OR Apache-2.0 (dual licensed)

---

Contributors

RuVector Team - https://github.com/ruvnet/ruvector

---

This architecture document is part of the RuVector Nervous System project. For implementation details, see individual module documentation.