Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/examples/spiking-network/Cargo.toml

@@ -0,0 +1,68 @@
+[package]
+name = "spiking-network"
+version = "0.1.0"
+edition = "2021"
+rust-version = "1.77"
+license = "MIT"
+authors = ["Ruvector Team"]
+description = "Event-driven spiking neural network for ASIC-optimized neuromorphic computing"
+readme = "docs/README.md"
+
+[dependencies]
+# Core ruvector dependencies
+ruvector-core = { path = "../../crates/ruvector-core", default-features = false }
+ruvector-gnn = { path = "../../crates/ruvector-gnn", default-features = false }
+
+# Math and numerics
+ndarray = { version = "0.16", features = ["serde"] }
+rand = "0.8"
+rand_distr = "0.4"
+
+# Serialization
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+
+# Error handling
+thiserror = "2.0"
+anyhow = "1.0"
+
+# Performance
+rayon = "1.10"
+parking_lot = "0.12"
+dashmap = "6.1"
+
+# Collections for sparse operations
+indexmap = { version = "2.0", features = ["serde"] }
+smallvec = { version = "1.11", features = ["serde"] }
+
+# Bitsets for spike encoding
+bitvec = { version = "1.0", features = ["serde"] }
+
+# Priority queue for event scheduling
+priority-queue = "2.0"
+
+[dev-dependencies]
+criterion = { version = "0.5", features = ["html_reports"] }
+proptest = "1.5"
+
+[features]
+default = ["simd"]
+simd = []
+wasm = []
+visualization = []
+
+[[bench]]
+name = "spiking_bench"
+harness = false
+
+[[example]]
+name = "edge_detection"
+path = "src/examples/edge_detection.rs"
+
+[[example]]
+name = "pattern_recognition"
+path = "src/examples/pattern_recognition.rs"
+
+[[example]]
+name = "asic_simulation"
+path = "src/examples/asic_simulation.rs"

vendor/ruvector/examples/spiking-network/src/encoding/mod.rs

@@ -0,0 +1,388 @@
+//! Spike encoding and decoding utilities.
+//!
+//! This module provides methods to convert between analog signals and sparse spike trains.
+//!
+//! ## Encoding Schemes
+//!
+//! - **Rate coding**: Spike frequency encodes magnitude
+//! - **Temporal coding**: Spike timing encodes information
+//! - **Population coding**: Distributed representation across neurons
+//! - **Delta modulation**: Spikes encode changes only
+//!
+//! ## ASIC Benefits
+//!
+//! Sparse spike representations dramatically reduce:
+//! - Memory bandwidth (only store/transmit active spikes)
+//! - Computation (skip silent neurons entirely)
+//! - Power consumption (event-driven processing)
+
+use bitvec::prelude::*;
+use serde::{Deserialize, Serialize};
+use smallvec::SmallVec;
+
+/// A single spike event with source and timing.
+#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
+pub struct SpikeEvent {
+    /// Source neuron index
+    pub source: u32,
+    /// Timestamp in simulation time units
+    pub time: f32,
+    /// Optional payload (for routing or plasticity)
+    pub payload: u8,
+}
+
+impl SpikeEvent {
+    /// Create a new spike event.
+    pub fn new(source: u32, time: f32) -> Self {
+        Self {
+            source,
+            time,
+            payload: 0,
+        }
+    }
+
+    /// Create spike with payload.
+    pub fn with_payload(source: u32, time: f32, payload: u8) -> Self {
+        Self { source, time, payload }
+    }
+}
+
+/// A train of spikes from a single neuron over time.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SpikeTrain {
+    /// Neuron ID
+    pub neuron_id: u32,
+    /// Spike times (sorted ascending)
+    pub times: Vec<f32>,
+}
+
+impl SpikeTrain {
+    /// Create empty spike train for a neuron.
+    pub fn new(neuron_id: u32) -> Self {
+        Self {
+            neuron_id,
+            times: Vec::new(),
+        }
+    }
+
+    /// Add a spike at given time.
+    pub fn add_spike(&mut self, time: f32) {
+        self.times.push(time);
+    }
+
+    /// Get spike count.
+    pub fn spike_count(&self) -> usize {
+        self.times.len()
+    }
+
+    /// Calculate firing rate over duration.
+    pub fn firing_rate(&self, duration: f32) -> f32 {
+        if duration <= 0.0 {
+            return 0.0;
+        }
+        self.times.len() as f32 / duration * 1000.0 // Hz
+    }
+
+    /// Get inter-spike intervals.
+    pub fn isis(&self) -> Vec<f32> {
+        if self.times.len() < 2 {
+            return Vec::new();
+        }
+        self.times.windows(2).map(|w| w[1] - w[0]).collect()
+    }
+}
+
+/// Sparse spike matrix for population activity.
+///
+/// Uses compressed representation - only stores active spikes.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SparseSpikes {
+    /// Number of neurons in population
+    pub num_neurons: u32,
+    /// Number of time bins
+    pub num_timesteps: u32,
+    /// Spike events (sorted by time)
+    pub events: Vec<SpikeEvent>,
+}
+
+impl SparseSpikes {
+    /// Create empty spike matrix.
+    pub fn new(num_neurons: u32, num_timesteps: u32) -> Self {
+        Self {
+            num_neurons,
+            num_timesteps,
+            events: Vec::new(),
+        }
+    }
+
+    /// Add a spike event.
+    pub fn add_spike(&mut self, neuron: u32, timestep: u32) {
+        if neuron < self.num_neurons && timestep < self.num_timesteps {
+            self.events.push(SpikeEvent::new(neuron, timestep as f32));
+        }
+    }
+
+    /// Get sparsity (fraction of silent entries).
+    pub fn sparsity(&self) -> f32 {
+        let total = self.num_neurons as f64 * self.num_timesteps as f64;
+        if total == 0.0 {
+            return 1.0;
+        }
+        1.0 - (self.events.len() as f64 / total) as f32
+    }
+
+    /// Get neurons that spiked at given timestep.
+    pub fn spikes_at(&self, timestep: u32) -> SmallVec<[u32; 8]> {
+        self.events
+            .iter()
+            .filter(|e| e.time as u32 == timestep)
+            .map(|e| e.source)
+            .collect()
+    }
+
+    /// Get spike count.
+    pub fn spike_count(&self) -> usize {
+        self.events.len()
+    }
+}
+
+/// Encoder for converting analog values to spike trains.
+pub struct SpikeEncoder;
+
+impl SpikeEncoder {
+    /// Rate coding: Convert value to spike probability per timestep.
+    ///
+    /// Higher values produce more frequent spikes.
+    /// Returns sparse bit vector of spike times.
+    pub fn rate_encode(value: f32, duration_ms: f32, dt: f32, max_rate_hz: f32) -> BitVec {
+        let num_steps = (duration_ms / dt) as usize;
+        let mut spikes = bitvec![0; num_steps];
+
+        // Clamp value to [0, 1]
+        let normalized = value.clamp(0.0, 1.0);
+
+        // Convert to spike probability per timestep
+        let prob_per_step = normalized * max_rate_hz * dt / 1000.0;
+
+        // Generate spikes stochastically
+        use rand::Rng;
+        let mut rng = rand::thread_rng();
+
+        for i in 0..num_steps {
+            if rng.gen::<f32>() < prob_per_step {
+                spikes.set(i, true);
+            }
+        }
+
+        spikes
+    }
+
+    /// Temporal coding: First spike time encodes value.
+    ///
+    /// Lower values spike earlier (inverse temporal coding).
+    pub fn temporal_encode(value: f32, max_latency_ms: f32) -> f32 {
+        let normalized = value.clamp(0.0, 1.0);
+        // Invert: high value = early spike
+        (1.0 - normalized) * max_latency_ms
+    }
+
+    /// Delta modulation: Spike on significant change.
+    ///
+    /// Returns (+1, 0, -1) for increase, no change, decrease.
+    pub fn delta_encode(current: f32, previous: f32, threshold: f32) -> i8 {
+        let delta = current - previous;
+        if delta > threshold {
+            1 // Positive spike
+        } else if delta < -threshold {
+            -1 // Negative spike
+        } else {
+            0 // No spike
+        }
+    }
+
+    /// Population coding: Distribute value across multiple neurons.
+    ///
+    /// Returns spike pattern across `num_neurons` with Gaussian tuning curves.
+    pub fn population_encode(value: f32, num_neurons: usize, sigma: f32) -> Vec<f32> {
+        let mut activities = vec![0.0; num_neurons];
+        let centers: Vec<f32> = (0..num_neurons)
+            .map(|i| i as f32 / (num_neurons - 1).max(1) as f32)
+            .collect();
+
+        for (i, &center) in centers.iter().enumerate() {
+            let diff = value - center;
+            activities[i] = (-diff * diff / (2.0 * sigma * sigma)).exp();
+        }
+
+        activities
+    }
+
+    /// Convert image patch to spike-based representation.
+    ///
+    /// Uses difference-of-Gaussians for edge detection,
+    /// then temporal coding for spike generation.
+    pub fn encode_image_patch(
+        patch: &[f32],
+        width: usize,
+        height: usize,
+    ) -> SparseSpikes {
+        let mut spikes = SparseSpikes::new((width * height) as u32, 100);
+
+        // Simple intensity-based encoding
+        for (i, &pixel) in patch.iter().enumerate() {
+            if i >= width * height {
+                break;
+            }
+            // Higher intensity = earlier spike
+            let spike_time = ((1.0 - pixel.clamp(0.0, 1.0)) * 99.0) as u32;
+            if pixel > 0.1 {
+                // Threshold
+                spikes.add_spike(i as u32, spike_time);
+            }
+        }
+
+        spikes
+    }
+}
+
+/// Decoder for converting spike trains back to analog values.
+pub struct SpikeDecoder;
+
+impl SpikeDecoder {
+    /// Decode rate-coded spikes to value.
+    pub fn rate_decode(spikes: &BitVec, dt: f32, max_rate_hz: f32) -> f32 {
+        let spike_count = spikes.count_ones();
+        let duration_ms = spikes.len() as f32 * dt;
+        let rate_hz = spike_count as f32 / duration_ms * 1000.0;
+        (rate_hz / max_rate_hz).clamp(0.0, 1.0)
+    }
+
+    /// Decode temporally-coded spike time to value.
+    pub fn temporal_decode(spike_time: f32, max_latency_ms: f32) -> f32 {
+        1.0 - (spike_time / max_latency_ms).clamp(0.0, 1.0)
+    }
+
+    /// Decode population activity to value using center-of-mass.
+    pub fn population_decode(activities: &[f32]) -> f32 {
+        let num_neurons = activities.len();
+        if num_neurons == 0 {
+            return 0.5;
+        }
+
+        let mut weighted_sum = 0.0;
+        let mut total_weight = 0.0;
+
+        for (i, &activity) in activities.iter().enumerate() {
+            let center = i as f32 / (num_neurons - 1).max(1) as f32;
+            weighted_sum += center * activity;
+            total_weight += activity;
+        }
+
+        if total_weight > 0.0 {
+            weighted_sum / total_weight
+        } else {
+            0.5
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_spike_event_creation() {
+        let event = SpikeEvent::new(42, 10.5);
+        assert_eq!(event.source, 42);
+        assert_eq!(event.time, 10.5);
+        assert_eq!(event.payload, 0);
+    }
+
+    #[test]
+    fn test_spike_train() {
+        let mut train = SpikeTrain::new(0);
+        train.add_spike(10.0);
+        train.add_spike(30.0);
+        train.add_spike(50.0);
+
+        assert_eq!(train.spike_count(), 3);
+        assert!((train.firing_rate(100.0) - 30.0).abs() < 0.1);
+
+        let isis = train.isis();
+        assert_eq!(isis, vec![20.0, 20.0]);
+    }
+
+    #[test]
+    fn test_sparse_spikes() {
+        let mut spikes = SparseSpikes::new(100, 1000);
+        spikes.add_spike(0, 10);
+        spikes.add_spike(50, 10);
+        spikes.add_spike(99, 500);
+
+        assert_eq!(spikes.spike_count(), 3);
+        assert!(spikes.sparsity() > 0.99); // Very sparse
+
+        let at_10 = spikes.spikes_at(10);
+        assert_eq!(at_10.len(), 2);
+    }
+
+    #[test]
+    fn test_rate_encoding() {
+        // High value should produce more spikes
+        let high_spikes = SpikeEncoder::rate_encode(0.9, 100.0, 1.0, 100.0);
+        let low_spikes = SpikeEncoder::rate_encode(0.1, 100.0, 1.0, 100.0);
+
+        // Statistical test - not deterministic
+        assert!(high_spikes.count_ones() > low_spikes.count_ones() / 2);
+    }
+
+    #[test]
+    fn test_temporal_encoding() {
+        let early = SpikeEncoder::temporal_encode(0.9, 100.0);
+        let late = SpikeEncoder::temporal_encode(0.1, 100.0);
+
+        assert!(early < late); // High value = early spike
+    }
+
+    #[test]
+    fn test_delta_encoding() {
+        assert_eq!(SpikeEncoder::delta_encode(1.0, 0.0, 0.5), 1);
+        assert_eq!(SpikeEncoder::delta_encode(0.0, 1.0, 0.5), -1);
+        assert_eq!(SpikeEncoder::delta_encode(0.5, 0.5, 0.5), 0);
+    }
+
+    #[test]
+    fn test_population_encoding() {
+        let activities = SpikeEncoder::population_encode(0.5, 10, 0.2);
+        assert_eq!(activities.len(), 10);
+
+        // Middle neuron should have highest activity
+        let max_idx = activities
+            .iter()
+            .enumerate()
+            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
+            .map(|(i, _)| i)
+            .unwrap();
+        assert_eq!(max_idx, 4); // Close to middle
+    }
+
+    #[test]
+    fn test_rate_decode() {
+        let mut spikes = bitvec![0; 100];
+        // 10 spikes in 100 timesteps at dt=1ms = 100 Hz
+        for i in (0..100).step_by(10) {
+            spikes.set(i, true);
+        }
+
+        let decoded = SpikeDecoder::rate_decode(&spikes, 1.0, 100.0);
+        assert!((decoded - 1.0).abs() < 0.1);
+    }
+
+    #[test]
+    fn test_population_decode() {
+        // Peak at middle
+        let activities = vec![0.0, 0.1, 0.5, 1.0, 0.5, 0.1, 0.0];
+        let decoded = SpikeDecoder::population_decode(&activities);
+        assert!((decoded - 0.5).abs() < 0.1);
+    }
+}

vendor/ruvector/examples/spiking-network/src/error.rs

@@ -0,0 +1,46 @@
+//! Error types for the spiking neural network library.
+
+use thiserror::Error;
+
+/// Result type alias for spiking network operations.
+pub type Result<T> = std::result::Result<T, SpikingError>;
+
+/// Errors that can occur in spiking neural network operations.
+#[derive(Error, Debug)]
+pub enum SpikingError {
+    /// Invalid neuron parameters
+    #[error("Invalid neuron parameters: {0}")]
+    InvalidParams(String),
+
+    /// Network topology error
+    #[error("Network topology error: {0}")]
+    TopologyError(String),
+
+    /// Spike encoding error
+    #[error("Spike encoding error: {0}")]
+    EncodingError(String),
+
+    /// Router error
+    #[error("Router error: {0}")]
+    RouterError(String),
+
+    /// Learning error
+    #[error("Learning error: {0}")]
+    LearningError(String),
+
+    /// Resource exhaustion
+    #[error("Resource exhaustion: {0}")]
+    ResourceExhausted(String),
+
+    /// Invalid operation
+    #[error("Invalid operation: {0}")]
+    InvalidOperation(String),
+
+    /// IO error wrapper
+    #[error("IO error: {0}")]
+    IoError(#[from] std::io::Error),
+
+    /// Serialization error
+    #[error("Serialization error: {0}")]
+    SerializationError(String),
+}

vendor/ruvector/examples/spiking-network/src/lib.rs

@@ -0,0 +1,71 @@
+//! # Spiking Neural Network Library
+//!
+//! Event-driven spiking neural network implementation optimized for ASIC deployment.
+//!
+//! ## Philosophy
+//!
+//! Spiking neural networks do not compute in the traditional sense. They fire only when
+//! something meaningful happens. Everything is event-driven. This single shift changes
+//! the entire energy and timing model of your ASIC.
+//!
+//! A conventional network evaluates every neuron every cycle. It burns power on
+//! multiplications even when nothing is changing. A spiking model skips all of that.
+//! Neurons stay silent until a threshold is crossed. You only compute on change.
+//!
+//! ## Architecture Benefits
+//!
+//! - **Sparse computation**: Only active neurons consume resources
+//! - **Event-driven**: No wasted cycles on unchanged state
+//! - **Local connectivity**: Minimizes routing complexity
+//! - **Tiny events**: Each spike is just a few bits
+//! - **Microsecond latency**: Local lookups instead of matrix multiplies
+//!
+//! ## Usage
+//!
+//! ```rust,ignore
+//! use spiking_network::{
+//!     neuron::{LIFNeuron, NeuronParams},
+//!     network::SpikingNetwork,
+//!     encoding::SpikeEncoder,
+//! };
+//!
+//! // Create a network with 1000 neurons
+//! let mut network = SpikingNetwork::new(1000);
+//!
+//! // Encode input as sparse spikes
+//! let spikes = SpikeEncoder::rate_encode(&input_data, 0.1);
+//!
+//! // Process - only fires on meaningful events
+//! let output = network.process(&spikes);
+//! ```
+
+#![warn(missing_docs)]
+#![deny(unsafe_op_in_unsafe_fn)]
+
+pub mod encoding;
+pub mod error;
+pub mod learning;
+pub mod network;
+pub mod neuron;
+pub mod router;
+
+// Re-exports for convenience
+pub use encoding::{SpikeEncoder, SpikeEvent, SpikeTrain};
+pub use error::{Result, SpikingError};
+pub use learning::{STDPConfig, STDPLearning};
+pub use network::{NetworkConfig, NetworkStats, SpikingNetwork};
+pub use neuron::{IzhikevichNeuron, LIFNeuron, NeuronParams, SpikingNeuron};
+pub use router::{AsicRouter, RouterConfig, SpikePacket};
+
+/// Library version
+pub const VERSION: &str = env!("CARGO_PKG_VERSION");
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_version() {
+        assert!(!VERSION.is_empty());
+    }
+}

vendor/ruvector/examples/spiking-network/src/network/mod.rs

@@ -0,0 +1,544 @@
+//! Spiking neural network implementation.
+//!
+//! This module provides the core network structure with event-driven processing.
+//!
+//! ## Event-Driven Architecture
+//!
+//! Unlike conventional ANNs that evaluate every neuron every cycle, this network:
+//! - Processes only when spikes arrive
+//! - Skips silent neurons entirely
+//! - Routes tiny spike events (few bits each)
+//! - Maintains microsecond-scale latency
+//!
+//! ## Network Topologies
+//!
+//! Supports ASIC-friendly connectivity patterns:
+//! - Local 2D grids (minimal routing)
+//! - Small-world networks (efficient paths)
+//! - Hierarchical layers (feedforward)
+//! - Custom sparse connectivity
+
+mod synapse;
+mod topology;
+
+pub use synapse::{Synapse, SynapseType};
+pub use topology::{ConnectionPattern, LocalConnectivity, TopologyConfig};
+
+use crate::encoding::{SparseSpikes, SpikeEvent};
+use crate::error::{Result, SpikingError};
+use crate::neuron::{LIFNeuron, SpikingNeuron};
+use indexmap::IndexMap;
+use parking_lot::RwLock;
+use priority_queue::PriorityQueue;
+use rayon::prelude::*;
+use serde::{Deserialize, Serialize};
+use smallvec::SmallVec;
+use std::cmp::Reverse;
+use std::sync::Arc;
+
+/// Network configuration.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct NetworkConfig {
+    /// Number of neurons
+    pub num_neurons: usize,
+    /// Simulation timestep (ms)
+    pub dt: f32,
+    /// Enable parallel processing
+    pub parallel: bool,
+    /// Maximum synapses per neuron (for ASIC budgeting)
+    pub max_synapses_per_neuron: usize,
+    /// Topology configuration
+    pub topology: TopologyConfig,
+}
+
+impl Default for NetworkConfig {
+    fn default() -> Self {
+        Self {
+            num_neurons: 1000,
+            dt: 1.0,
+            parallel: true,
+            max_synapses_per_neuron: 100,
+            topology: TopologyConfig::default(),
+        }
+    }
+}
+
+/// Statistics from network simulation.
+#[derive(Debug, Clone, Default, Serialize, Deserialize)]
+pub struct NetworkStats {
+    /// Total spikes generated
+    pub total_spikes: usize,
+    /// Spikes per timestep
+    pub spikes_per_step: Vec<usize>,
+    /// Active neuron count per timestep
+    pub active_neurons_per_step: Vec<usize>,
+    /// Energy consumed (picojoules)
+    pub energy_consumed: f64,
+    /// Simulation time (ms)
+    pub simulation_time: f32,
+    /// Average firing rate (Hz)
+    pub avg_firing_rate: f32,
+    /// Network sparsity (fraction of silent neurons)
+    pub sparsity: f32,
+}
+
+impl NetworkStats {
+    /// Calculate statistics after simulation.
+    pub fn finalize(&mut self) {
+        if self.spikes_per_step.is_empty() {
+            return;
+        }
+
+        self.total_spikes = self.spikes_per_step.iter().sum();
+
+        let num_neurons = if !self.active_neurons_per_step.is_empty() {
+            self.active_neurons_per_step.iter().max().copied().unwrap_or(1)
+        } else {
+            1
+        };
+
+        // Calculate average firing rate
+        if self.simulation_time > 0.0 && num_neurons > 0 {
+            self.avg_firing_rate = (self.total_spikes as f32)
+                / (num_neurons as f32)
+                / self.simulation_time
+                * 1000.0;
+        }
+
+        // Calculate sparsity
+        let total_possible: usize = self.active_neurons_per_step.iter().sum();
+        if total_possible > 0 {
+            self.sparsity = 1.0 - (self.total_spikes as f32 / total_possible as f32);
+        }
+    }
+}
+
+/// Event in the priority queue for event-driven simulation.
+#[derive(Debug, Clone)]
+struct NetworkEvent {
+    /// Target neuron index
+    target: usize,
+    /// Event time
+    time: f32,
+    /// Input current to deliver
+    current: f32,
+}
+
+impl PartialEq for NetworkEvent {
+    fn eq(&self, other: &Self) -> bool {
+        self.time == other.time && self.target == other.target
+    }
+}
+
+impl Eq for NetworkEvent {}
+
+impl std::hash::Hash for NetworkEvent {
+    fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
+        self.target.hash(state);
+        self.time.to_bits().hash(state);
+    }
+}
+
+/// Spiking neural network with event-driven processing.
+pub struct SpikingNetwork {
+    /// Network configuration
+    config: NetworkConfig,
+    /// Neurons (using LIF as default)
+    neurons: Vec<LIFNeuron>,
+    /// Outgoing connections: source -> [(target, synapse)]
+    connections: Vec<SmallVec<[(usize, Synapse); 16]>>,
+    /// Event queue for spike scheduling
+    event_queue: PriorityQueue<NetworkEvent, Reverse<i64>>,
+    /// Current simulation time
+    current_time: f32,
+    /// Statistics collector
+    stats: NetworkStats,
+    /// Output spikes (for external readout)
+    output_spikes: Arc<RwLock<Vec<SpikeEvent>>>,
+}
+
+impl SpikingNetwork {
+    /// Create a new spiking network.
+    pub fn new(config: NetworkConfig) -> Result<Self> {
+        if config.num_neurons == 0 {
+            return Err(SpikingError::InvalidParams("num_neurons must be > 0".into()));
+        }
+
+        // Initialize neurons
+        let neurons: Vec<LIFNeuron> = (0..config.num_neurons)
+            .map(|_| LIFNeuron::with_defaults())
+            .collect();
+
+        // Initialize connection storage
+        let connections = vec![SmallVec::new(); config.num_neurons];
+
+        Ok(Self {
+            config,
+            neurons,
+            connections,
+            event_queue: PriorityQueue::new(),
+            current_time: 0.0,
+            stats: NetworkStats::default(),
+            output_spikes: Arc::new(RwLock::new(Vec::new())),
+        })
+    }
+
+    /// Create network with given number of neurons and default config.
+    pub fn with_neurons(num_neurons: usize) -> Result<Self> {
+        Self::new(NetworkConfig {
+            num_neurons,
+            ..Default::default()
+        })
+    }
+
+    /// Add a synaptic connection.
+    pub fn connect(&mut self, source: usize, target: usize, synapse: Synapse) -> Result<()> {
+        if source >= self.config.num_neurons || target >= self.config.num_neurons {
+            return Err(SpikingError::TopologyError("Invalid neuron indices".into()));
+        }
+
+        if self.connections[source].len() >= self.config.max_synapses_per_neuron {
+            return Err(SpikingError::ResourceExhausted(format!(
+                "Max synapses ({}) reached for neuron {}",
+                self.config.max_synapses_per_neuron, source
+            )));
+        }
+
+        self.connections[source].push((target, synapse));
+        Ok(())
+    }
+
+    /// Build network topology from configuration.
+    pub fn build_topology(&mut self) -> Result<()> {
+        let pattern = self.config.topology.pattern.clone();
+        let num_neurons = self.config.num_neurons;
+
+        match pattern {
+            ConnectionPattern::AllToAll { probability } => {
+                self.build_random_connections(probability)?;
+            }
+            ConnectionPattern::LocalGrid { width, radius } => {
+                self.build_local_grid(width, radius)?;
+            }
+            ConnectionPattern::SmallWorld {
+                k,
+                rewire_prob,
+            } => {
+                self.build_small_world(k, rewire_prob)?;
+            }
+            ConnectionPattern::Feedforward { layer_sizes } => {
+                self.build_feedforward(&layer_sizes)?;
+            }
+            ConnectionPattern::Custom => {
+                // Connections added manually
+            }
+        }
+
+        Ok(())
+    }
+
+    fn build_random_connections(&mut self, probability: f32) -> Result<()> {
+        use rand::Rng;
+        let mut rng = rand::thread_rng();
+        let n = self.config.num_neurons;
+
+        for src in 0..n {
+            for tgt in 0..n {
+                if src != tgt && rng.gen::<f32>() < probability {
+                    let weight = rng.gen_range(0.1..1.0);
+                    let synapse = Synapse::excitatory(weight);
+                    let _ = self.connect(src, tgt, synapse);
+                }
+            }
+        }
+        Ok(())
+    }
+
+    fn build_local_grid(&mut self, width: usize, radius: usize) -> Result<()> {
+        use rand::Rng;
+        let mut rng = rand::thread_rng();
+        let height = self.config.num_neurons / width;
+
+        for y in 0..height {
+            for x in 0..width {
+                let src = y * width + x;
+
+                // Connect to neighbors within radius
+                for dy in -(radius as i32)..=(radius as i32) {
+                    for dx in -(radius as i32)..=(radius as i32) {
+                        if dx == 0 && dy == 0 {
+                            continue;
+                        }
+
+                        let nx = (x as i32 + dx).rem_euclid(width as i32) as usize;
+                        let ny = (y as i32 + dy).rem_euclid(height as i32) as usize;
+                        let tgt = ny * width + nx;
+
+                        if tgt < self.config.num_neurons {
+                            let distance = ((dx * dx + dy * dy) as f32).sqrt();
+                            let weight = 1.0 / distance * rng.gen_range(0.5..1.0);
+                            let synapse = Synapse::excitatory(weight);
+                            let _ = self.connect(src, tgt, synapse);
+                        }
+                    }
+                }
+            }
+        }
+        Ok(())
+    }
+
+    fn build_small_world(&mut self, k: usize, rewire_prob: f32) -> Result<()> {
+        use rand::Rng;
+        let mut rng = rand::thread_rng();
+        let n = self.config.num_neurons;
+
+        // Start with ring lattice
+        for i in 0..n {
+            for j in 1..=k / 2 {
+                let neighbor = (i + j) % n;
+                let weight = rng.gen_range(0.5..1.0);
+                let synapse = Synapse::excitatory(weight);
+                let _ = self.connect(i, neighbor, synapse);
+            }
+        }
+
+        // Rewire with probability
+        for i in 0..n {
+            if rng.gen::<f32>() < rewire_prob {
+                let new_target = rng.gen_range(0..n);
+                if new_target != i {
+                    let weight = rng.gen_range(0.5..1.0);
+                    let synapse = Synapse::excitatory(weight);
+                    let _ = self.connect(i, new_target, synapse);
+                }
+            }
+        }
+        Ok(())
+    }
+
+    fn build_feedforward(&mut self, layer_sizes: &[usize]) -> Result<()> {
+        use rand::Rng;
+        let mut rng = rand::thread_rng();
+        let mut offset = 0;
+
+        for i in 0..layer_sizes.len() - 1 {
+            let src_size = layer_sizes[i];
+            let tgt_size = layer_sizes[i + 1];
+            let tgt_offset = offset + src_size;
+
+            for src in 0..src_size {
+                for tgt in 0..tgt_size {
+                    let weight = rng.gen_range(0.1..1.0);
+                    let synapse = Synapse::excitatory(weight);
+                    let _ = self.connect(offset + src, tgt_offset + tgt, synapse);
+                }
+            }
+
+            offset = tgt_offset;
+        }
+        Ok(())
+    }
+
+    /// Inject external input spikes.
+    pub fn inject_spikes(&mut self, spikes: &SparseSpikes) {
+        for event in &spikes.events {
+            if (event.source as usize) < self.config.num_neurons {
+                self.schedule_event(
+                    event.source as usize,
+                    event.time,
+                    1.0, // Unit current for input spikes
+                );
+            }
+        }
+    }
+
+    /// Schedule an event for future processing.
+    fn schedule_event(&mut self, target: usize, time: f32, current: f32) {
+        let event = NetworkEvent {
+            target,
+            time,
+            current,
+        };
+        // Priority is negative time (earlier = higher priority)
+        let priority = Reverse((time * 1000.0) as i64);
+        self.event_queue.push(event, priority);
+    }
+
+    /// Process one timestep using event-driven simulation.
+    pub fn step(&mut self) -> usize {
+        let dt = self.config.dt;
+        let next_time = self.current_time + dt;
+        let mut spikes_this_step = 0;
+
+        // Process events up to next_time
+        while let Some((event, _)) = self.event_queue.peek() {
+            if event.time > next_time {
+                break;
+            }
+
+            let event = self.event_queue.pop().unwrap().0;
+            self.neurons[event.target].receive_input(event.current);
+        }
+
+        // Update all neurons and collect spikes
+        let spike_indices: Vec<usize> = if self.config.parallel {
+            self.neurons
+                .par_iter_mut()
+                .enumerate()
+                .filter_map(|(i, neuron)| {
+                    if neuron.update(dt) {
+                        Some(i)
+                    } else {
+                        None
+                    }
+                })
+                .collect()
+        } else {
+            self.neurons
+                .iter_mut()
+                .enumerate()
+                .filter_map(|(i, neuron)| {
+                    if neuron.update(dt) {
+                        Some(i)
+                    } else {
+                        None
+                    }
+                })
+                .collect()
+        };
+
+        // Propagate spikes
+        for &src in &spike_indices {
+            spikes_this_step += 1;
+
+            // Record output spike
+            {
+                let mut outputs = self.output_spikes.write();
+                outputs.push(SpikeEvent::new(src as u32, self.current_time));
+            }
+
+            // Schedule postsynaptic events
+            for &(target, ref synapse) in &self.connections[src] {
+                let arrival_time = self.current_time + synapse.delay;
+                let current = synapse.weight * synapse.sign();
+                self.schedule_event(target, arrival_time, current);
+            }
+        }
+
+        // Update statistics
+        self.stats.spikes_per_step.push(spikes_this_step);
+        self.stats.active_neurons_per_step.push(self.config.num_neurons);
+        self.stats.energy_consumed += self.estimate_step_energy(spikes_this_step);
+
+        self.current_time = next_time;
+        spikes_this_step
+    }
+
+    /// Run simulation for given duration.
+    pub fn run(&mut self, duration_ms: f32) -> NetworkStats {
+        let num_steps = (duration_ms / self.config.dt) as usize;
+
+        for _ in 0..num_steps {
+            self.step();
+        }
+
+        self.stats.simulation_time = duration_ms;
+        self.stats.finalize();
+        self.stats.clone()
+    }
+
+    /// Get output spikes.
+    pub fn output_spikes(&self) -> Vec<SpikeEvent> {
+        self.output_spikes.read().clone()
+    }
+
+    /// Clear output spike buffer.
+    pub fn clear_outputs(&mut self) {
+        self.output_spikes.write().clear();
+    }
+
+    /// Reset network to initial state.
+    pub fn reset(&mut self) {
+        for neuron in &mut self.neurons {
+            neuron.reset();
+        }
+        self.event_queue.clear();
+        self.current_time = 0.0;
+        self.stats = NetworkStats::default();
+        self.output_spikes.write().clear();
+    }
+
+    /// Get current time.
+    pub fn current_time(&self) -> f32 {
+        self.current_time
+    }
+
+    /// Get number of neurons.
+    pub fn num_neurons(&self) -> usize {
+        self.config.num_neurons
+    }
+
+    /// Get total number of synapses.
+    pub fn num_synapses(&self) -> usize {
+        self.connections.iter().map(|c| c.len()).sum()
+    }
+
+    /// Estimate energy for one step.
+    fn estimate_step_energy(&self, num_spikes: usize) -> f64 {
+        // Base energy for updates
+        let update_energy = self.config.num_neurons as f64 * 3.0; // pJ per neuron
+
+        // Spike energy
+        let spike_energy = num_spikes as f64 * 10.0; // pJ per spike
+
+        update_energy + spike_energy
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_network_creation() {
+        let network = SpikingNetwork::with_neurons(100).unwrap();
+        assert_eq!(network.num_neurons(), 100);
+    }
+
+    #[test]
+    fn test_basic_connection() {
+        let mut network = SpikingNetwork::with_neurons(10).unwrap();
+        let synapse = Synapse::excitatory(0.5);
+        network.connect(0, 1, synapse).unwrap();
+        assert_eq!(network.num_synapses(), 1);
+    }
+
+    #[test]
+    fn test_simulation_step() {
+        let mut network = SpikingNetwork::with_neurons(10).unwrap();
+
+        // Add some connections
+        for i in 0..9 {
+            network.connect(i, i + 1, Synapse::excitatory(0.5)).unwrap();
+        }
+
+        // Inject strong input to first neuron
+        let mut spikes = SparseSpikes::new(10, 1);
+        spikes.add_spike(0, 0);
+        network.inject_spikes(&spikes);
+
+        // Run a few steps
+        let stats = network.run(100.0);
+        assert!(stats.total_spikes > 0);
+    }
+
+    #[test]
+    fn test_sparsity_tracking() {
+        let mut network = SpikingNetwork::with_neurons(100).unwrap();
+        network.build_topology().unwrap();
+
+        let stats = network.run(100.0);
+        // Without input, should be very sparse
+        assert!(stats.sparsity > 0.9);
+    }
+}

vendor/ruvector/examples/spiking-network/src/neuron/izhikevich.rs

@@ -0,0 +1,413 @@
+//! Izhikevich neuron model.
+//!
+//! The Izhikevich model captures rich spiking dynamics with just two variables:
+//! - Membrane potential (fast)
+//! - Recovery variable (slow)
+//!
+//! This allows simulation of 20+ different firing patterns observed in cortical neurons,
+//! while remaining computationally efficient.
+//!
+//! ## Firing Patterns
+//!
+//! - Regular spiking (RS) - most common excitatory
+//! - Intrinsically bursting (IB) - burst then regular
+//! - Chattering (CH) - fast rhythmic bursting
+//! - Fast spiking (FS) - inhibitory interneurons
+//! - Low-threshold spiking (LTS) - inhibitory
+//!
+//! ## ASIC Considerations
+//!
+//! - 2 multiply-accumulates per timestep
+//! - 1 multiplication for recovery
+//! - ~150-200 gates in digital implementation
+
+use super::{NeuronParams, NeuronState, SpikingNeuron, EnergyModel};
+use serde::{Deserialize, Serialize};
+
+/// Pre-defined Izhikevich neuron types with biological parameters.
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
+pub enum IzhikevichType {
+    /// Regular spiking - most common excitatory cortical neuron
+    RegularSpiking,
+    /// Intrinsically bursting - initial burst then regular spikes
+    IntrinsicallyBursting,
+    /// Chattering - fast rhythmic bursting
+    Chattering,
+    /// Fast spiking - typical inhibitory interneuron
+    FastSpiking,
+    /// Low-threshold spiking - inhibitory with rebound
+    LowThresholdSpiking,
+    /// Thalamo-cortical - two firing modes
+    ThalamoCortical,
+    /// Resonator - subthreshold oscillations
+    Resonator,
+}
+
+impl IzhikevichType {
+    /// Get parameters for this neuron type.
+    pub fn params(self) -> IzhikevichParams {
+        match self {
+            Self::RegularSpiking => IzhikevichParams {
+                a: 0.02,
+                b: 0.2,
+                c: -65.0,
+                d: 8.0,
+                threshold: 30.0,
+                refractory: 0.0, // Implicit in dynamics
+            },
+            Self::IntrinsicallyBursting => IzhikevichParams {
+                a: 0.02,
+                b: 0.2,
+                c: -55.0,
+                d: 4.0,
+                threshold: 30.0,
+                refractory: 0.0,
+            },
+            Self::Chattering => IzhikevichParams {
+                a: 0.02,
+                b: 0.2,
+                c: -50.0,
+                d: 2.0,
+                threshold: 30.0,
+                refractory: 0.0,
+            },
+            Self::FastSpiking => IzhikevichParams {
+                a: 0.1,
+                b: 0.2,
+                c: -65.0,
+                d: 2.0,
+                threshold: 30.0,
+                refractory: 0.0,
+            },
+            Self::LowThresholdSpiking => IzhikevichParams {
+                a: 0.02,
+                b: 0.25,
+                c: -65.0,
+                d: 2.0,
+                threshold: 30.0,
+                refractory: 0.0,
+            },
+            Self::ThalamoCortical => IzhikevichParams {
+                a: 0.02,
+                b: 0.25,
+                c: -65.0,
+                d: 0.05,
+                threshold: 30.0,
+                refractory: 0.0,
+            },
+            Self::Resonator => IzhikevichParams {
+                a: 0.1,
+                b: 0.26,
+                c: -65.0,
+                d: 2.0,
+                threshold: 30.0,
+                refractory: 0.0,
+            },
+        }
+    }
+}
+
+/// Parameters for Izhikevich neuron model.
+///
+/// The model equations are:
+/// ```text
+/// dv/dt = 0.04*v² + 5*v + 140 - u + I
+/// du/dt = a*(b*v - u)
+/// if v >= 30 mV: v = c, u = u + d
+/// ```
+#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
+pub struct IzhikevichParams {
+    /// Time scale of recovery variable (smaller = slower recovery)
+    pub a: f32,
+    /// Sensitivity of recovery to subthreshold membrane potential
+    pub b: f32,
+    /// After-spike reset value of membrane potential (mV)
+    pub c: f32,
+    /// After-spike reset increment of recovery variable
+    pub d: f32,
+    /// Spike threshold (mV) - typically 30
+    pub threshold: f32,
+    /// Explicit refractory period (ms) - usually 0 for Izhikevich
+    pub refractory: f32,
+}
+
+impl Default for IzhikevichParams {
+    fn default() -> Self {
+        IzhikevichType::RegularSpiking.params()
+    }
+}
+
+impl NeuronParams for IzhikevichParams {
+    fn threshold(&self) -> f32 {
+        self.threshold
+    }
+
+    fn reset_potential(&self) -> f32 {
+        self.c
+    }
+
+    fn resting_potential(&self) -> f32 {
+        // Resting potential is approximately -65 to -70 mV
+        -65.0
+    }
+
+    fn refractory_period(&self) -> f32 {
+        self.refractory
+    }
+
+    fn validate(&self) -> Option<String> {
+        if self.a <= 0.0 || self.a > 1.0 {
+            return Some("a should be in (0, 1]".into());
+        }
+        if self.threshold < 0.0 {
+            return Some("threshold should be positive".into());
+        }
+        None
+    }
+}
+
+/// Izhikevich neuron model.
+///
+/// Provides rich spiking dynamics while remaining computationally efficient.
+/// The two-variable model captures most qualitative behaviors of biological neurons.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct IzhikevichNeuron {
+    /// Model parameters
+    params: IzhikevichParams,
+    /// Membrane potential (mV)
+    v: f32,
+    /// Recovery variable (dimensionless)
+    u: f32,
+    /// Accumulated input current
+    input_current: f32,
+    /// Time since last spike
+    time_since_spike: Option<f32>,
+    /// Refractory countdown (if explicit refractory used)
+    refractory_remaining: f32,
+}
+
+impl IzhikevichNeuron {
+    /// Create neuron from predefined type.
+    pub fn from_type(neuron_type: IzhikevichType) -> Self {
+        Self::new(neuron_type.params())
+    }
+
+    /// Create regular spiking neuron (most common).
+    pub fn regular_spiking() -> Self {
+        Self::from_type(IzhikevichType::RegularSpiking)
+    }
+
+    /// Create fast spiking neuron (inhibitory).
+    pub fn fast_spiking() -> Self {
+        Self::from_type(IzhikevichType::FastSpiking)
+    }
+
+    /// Get recovery variable.
+    pub fn recovery(&self) -> f32 {
+        self.u
+    }
+}
+
+impl SpikingNeuron for IzhikevichNeuron {
+    type Params = IzhikevichParams;
+
+    fn new(params: IzhikevichParams) -> Self {
+        // Initialize at resting state
+        let v = params.c;
+        let u = params.b * v;
+        Self {
+            params,
+            v,
+            u,
+            input_current: 0.0,
+            time_since_spike: None,
+            refractory_remaining: 0.0,
+        }
+    }
+
+    fn state(&self) -> NeuronState {
+        NeuronState {
+            membrane_potential: self.v,
+            time_since_spike: self.time_since_spike,
+            is_refractory: self.refractory_remaining > 0.0,
+            input_current: self.input_current,
+        }
+    }
+
+    fn params(&self) -> &Self::Params {
+        &self.params
+    }
+
+    fn receive_input(&mut self, current: f32) {
+        self.input_current += current;
+    }
+
+    fn update(&mut self, dt: f32) -> bool {
+        // Update time since spike
+        if let Some(ref mut t) = self.time_since_spike {
+            *t += dt;
+        }
+
+        // Handle explicit refractory if set
+        if self.refractory_remaining > 0.0 {
+            self.refractory_remaining -= dt;
+            self.input_current = 0.0;
+            return false;
+        }
+
+        // Izhikevich dynamics with Euler integration
+        // For numerical stability, use two half-steps for v
+        let i = self.input_current;
+
+        // Half step 1
+        let dv1 = 0.04 * self.v * self.v + 5.0 * self.v + 140.0 - self.u + i;
+        self.v += dv1 * dt * 0.5;
+
+        // Half step 2
+        let dv2 = 0.04 * self.v * self.v + 5.0 * self.v + 140.0 - self.u + i;
+        self.v += dv2 * dt * 0.5;
+
+        // Recovery variable
+        let du = self.params.a * (self.params.b * self.v - self.u);
+        self.u += du * dt;
+
+        // Clear input
+        self.input_current = 0.0;
+
+        // Spike check
+        if self.v >= self.params.threshold {
+            // Spike!
+            self.v = self.params.c;
+            self.u += self.params.d;
+            self.time_since_spike = Some(0.0);
+            self.refractory_remaining = self.params.refractory;
+            true
+        } else {
+            false
+        }
+    }
+
+    fn reset(&mut self) {
+        self.v = self.params.c;
+        self.u = self.params.b * self.v;
+        self.input_current = 0.0;
+        self.time_since_spike = None;
+        self.refractory_remaining = 0.0;
+    }
+
+    fn is_refractory(&self) -> bool {
+        self.refractory_remaining > 0.0
+    }
+
+    fn membrane_potential(&self) -> f32 {
+        self.v
+    }
+
+    fn time_since_spike(&self) -> Option<f32> {
+        self.time_since_spike
+    }
+}
+
+impl EnergyModel for IzhikevichNeuron {
+    fn update_energy(&self) -> f32 {
+        // Estimate: 3 multiplies, 6 adds, 1 comparison
+        // More complex than LIF
+        5.0 // picojoules
+    }
+
+    fn spike_energy(&self) -> f32 {
+        10.0 // picojoules
+    }
+
+    fn silicon_area(&self) -> f32 {
+        // ~150-200 gates at 28nm
+        17.5 // square micrometers
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_izhikevich_types() {
+        // Test all predefined types can be created
+        let types = [
+            IzhikevichType::RegularSpiking,
+            IzhikevichType::IntrinsicallyBursting,
+            IzhikevichType::Chattering,
+            IzhikevichType::FastSpiking,
+            IzhikevichType::LowThresholdSpiking,
+            IzhikevichType::ThalamoCortical,
+            IzhikevichType::Resonator,
+        ];
+
+        for neuron_type in types {
+            let neuron = IzhikevichNeuron::from_type(neuron_type);
+            assert!(neuron.params().validate().is_none());
+        }
+    }
+
+    #[test]
+    fn test_regular_spiking_behavior() {
+        let mut neuron = IzhikevichNeuron::regular_spiking();
+        let mut spike_count = 0;
+
+        // Inject constant current and count spikes
+        for _ in 0..1000 {
+            neuron.receive_input(10.0);
+            if neuron.update(1.0) {
+                spike_count += 1;
+            }
+        }
+
+        // Should spike regularly
+        assert!(spike_count > 10, "Regular spiking neuron should fire regularly");
+        assert!(spike_count < 200, "Should not fire too fast");
+    }
+
+    #[test]
+    fn test_fast_spiking_behavior() {
+        let mut fs = IzhikevichNeuron::fast_spiking();
+        let mut rs = IzhikevichNeuron::regular_spiking();
+
+        let mut fs_spikes = 0;
+        let mut rs_spikes = 0;
+
+        // Same input to both
+        for _ in 0..1000 {
+            fs.receive_input(14.0);
+            rs.receive_input(14.0);
+
+            if fs.update(1.0) { fs_spikes += 1; }
+            if rs.update(1.0) { rs_spikes += 1; }
+        }
+
+        // Fast spiking should fire more often
+        assert!(fs_spikes > rs_spikes, "Fast spiking should fire more than regular");
+    }
+
+    #[test]
+    fn test_recovery_dynamics() {
+        let mut neuron = IzhikevichNeuron::regular_spiking();
+        let initial_u = neuron.recovery();
+
+        // After spike, recovery should increase
+        neuron.v = 35.0; // Above threshold
+        neuron.update(1.0);
+
+        assert!(neuron.recovery() > initial_u, "Recovery should increase after spike");
+    }
+
+    #[test]
+    fn test_subthreshold_dynamics() {
+        let mut neuron = IzhikevichNeuron::regular_spiking();
+
+        // Weak input should not cause immediate spike
+        neuron.receive_input(2.0);
+        assert!(!neuron.update(1.0));
+
+        // Voltage should rise but not spike
+        assert!(neuron.membrane_potential() > neuron.params.c);
+    }
+}

vendor/ruvector/examples/spiking-network/src/neuron/lif.rs

@@ -0,0 +1,316 @@
+//! Leaky Integrate-and-Fire (LIF) neuron model.
+//!
+//! The LIF model is the workhorse of neuromorphic computing:
+//! - Simple dynamics: membrane voltage leaks toward rest
+//! - Spikes when threshold crossed
+//! - Resets and enters refractory period
+//!
+//! ## ASIC Benefits
+//!
+//! - Single multiply-accumulate per timestep
+//! - No division (pre-computed decay factor)
+//! - 2-3 comparisons per update
+//! - ~100 gates in digital implementation
+
+use super::{NeuronParams, NeuronState, SpikingNeuron, EnergyModel};
+use serde::{Deserialize, Serialize};
+
+/// Parameters for LIF neuron.
+#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
+pub struct LIFParams {
+    /// Membrane time constant (ms) - controls leak rate
+    pub tau_m: f32,
+    /// Spike threshold (mV)
+    pub threshold: f32,
+    /// Reset potential after spike (mV)
+    pub reset: f32,
+    /// Resting membrane potential (mV)
+    pub resting: f32,
+    /// Refractory period (ms)
+    pub refractory: f32,
+    /// Membrane resistance (MOhm) - scales input current
+    pub resistance: f32,
+}
+
+impl Default for LIFParams {
+    fn default() -> Self {
+        Self {
+            tau_m: 20.0,
+            threshold: -50.0,
+            reset: -70.0,
+            resting: -65.0,
+            refractory: 2.0,
+            resistance: 10.0, // 10 MOhm typical for cortical neurons
+        }
+    }
+}
+
+impl NeuronParams for LIFParams {
+    fn threshold(&self) -> f32 {
+        self.threshold
+    }
+
+    fn reset_potential(&self) -> f32 {
+        self.reset
+    }
+
+    fn resting_potential(&self) -> f32 {
+        self.resting
+    }
+
+    fn refractory_period(&self) -> f32 {
+        self.refractory
+    }
+
+    fn validate(&self) -> Option<String> {
+        if self.tau_m <= 0.0 {
+            return Some("tau_m must be positive".into());
+        }
+        if self.threshold <= self.reset {
+            return Some("threshold must be greater than reset".into());
+        }
+        if self.refractory < 0.0 {
+            return Some("refractory period cannot be negative".into());
+        }
+        if self.resistance <= 0.0 {
+            return Some("resistance must be positive".into());
+        }
+        None
+    }
+}
+
+/// Leaky Integrate-and-Fire neuron.
+///
+/// Implements the differential equation:
+/// ```text
+/// τ_m * dV/dt = -(V - V_rest) + R * I
+/// ```
+///
+/// With spike condition: V ≥ V_threshold
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct LIFNeuron {
+    /// Neuron parameters
+    params: LIFParams,
+    /// Current membrane potential (mV)
+    membrane_potential: f32,
+    /// Time remaining in refractory period (ms)
+    refractory_remaining: f32,
+    /// Accumulated input current for this timestep
+    input_current: f32,
+    /// Time since last spike (ms)
+    time_since_spike: Option<f32>,
+    /// Pre-computed decay factor for efficiency
+    decay_factor: f32,
+}
+
+impl LIFNeuron {
+    /// Create LIF neuron with default parameters.
+    pub fn with_defaults() -> Self {
+        Self::new(LIFParams::default())
+    }
+
+    /// Pre-compute decay factor for given timestep.
+    ///
+    /// This avoids division in the hot path.
+    /// decay = exp(-dt / tau_m) ≈ 1 - dt/tau_m for small dt
+    fn compute_decay(&self, dt: f32) -> f32 {
+        // Use linear approximation for ASIC compatibility
+        // Error < 1% for dt < 2ms with tau_m = 20ms
+        1.0 - dt / self.params.tau_m
+    }
+
+    /// Get the pre-computed decay factor.
+    pub fn decay_factor(&self) -> f32 {
+        self.decay_factor
+    }
+}
+
+impl SpikingNeuron for LIFNeuron {
+    type Params = LIFParams;
+
+    fn new(params: LIFParams) -> Self {
+        let decay_factor = 1.0 - 1.0 / params.tau_m; // For dt=1ms default
+        Self {
+            params,
+            membrane_potential: params.resting,
+            refractory_remaining: 0.0,
+            input_current: 0.0,
+            time_since_spike: None,
+            decay_factor,
+        }
+    }
+
+    fn state(&self) -> NeuronState {
+        NeuronState {
+            membrane_potential: self.membrane_potential,
+            time_since_spike: self.time_since_spike,
+            is_refractory: self.refractory_remaining > 0.0,
+            input_current: self.input_current,
+        }
+    }
+
+    fn params(&self) -> &Self::Params {
+        &self.params
+    }
+
+    fn receive_input(&mut self, current: f32) {
+        // Accumulate input - this is the sparse event
+        self.input_current += current;
+    }
+
+    fn update(&mut self, dt: f32) -> bool {
+        // Update time since spike
+        if let Some(ref mut t) = self.time_since_spike {
+            *t += dt;
+        }
+
+        // Handle refractory period
+        if self.refractory_remaining > 0.0 {
+            self.refractory_remaining -= dt;
+            self.input_current = 0.0; // Clear accumulated input
+            return false;
+        }
+
+        // Compute decay factor for this timestep
+        let decay = self.compute_decay(dt);
+
+        // LIF dynamics: V = decay * V + (1-decay) * V_rest + R * I * dt / tau_m
+        // Simplified: V = decay * (V - V_rest) + V_rest + R * I * dt / tau_m
+        let v_diff = self.membrane_potential - self.params.resting;
+        let input_term = self.params.resistance * self.input_current * dt / self.params.tau_m;
+
+        self.membrane_potential = decay * v_diff + self.params.resting + input_term;
+
+        // Clear input for next timestep
+        self.input_current = 0.0;
+
+        // Check for spike
+        if self.membrane_potential >= self.params.threshold {
+            // Spike!
+            self.membrane_potential = self.params.reset;
+            self.refractory_remaining = self.params.refractory;
+            self.time_since_spike = Some(0.0);
+            true
+        } else {
+            false
+        }
+    }
+
+    fn reset(&mut self) {
+        self.membrane_potential = self.params.resting;
+        self.refractory_remaining = 0.0;
+        self.input_current = 0.0;
+        self.time_since_spike = None;
+    }
+
+    fn is_refractory(&self) -> bool {
+        self.refractory_remaining > 0.0
+    }
+
+    fn membrane_potential(&self) -> f32 {
+        self.membrane_potential
+    }
+
+    fn time_since_spike(&self) -> Option<f32> {
+        self.time_since_spike
+    }
+}
+
+impl EnergyModel for LIFNeuron {
+    fn update_energy(&self) -> f32 {
+        // Estimate: 1 multiply, 3 adds, 2 comparisons
+        // At 28nm: ~0.5 pJ per operation
+        3.0 // picojoules
+    }
+
+    fn spike_energy(&self) -> f32 {
+        // Spike packet generation and routing
+        10.0 // picojoules
+    }
+
+    fn silicon_area(&self) -> f32 {
+        // ~100 gates at 28nm ≈ 0.1 μm² per gate
+        10.0 // square micrometers
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_lif_default_creation() {
+        let neuron = LIFNeuron::with_defaults();
+        assert_eq!(neuron.membrane_potential(), -65.0);
+        assert!(!neuron.is_refractory());
+    }
+
+    #[test]
+    fn test_lif_spike_generation() {
+        let mut neuron = LIFNeuron::with_defaults();
+
+        // Inject strong current
+        for _ in 0..100 {
+            neuron.receive_input(5.0); // Strong input
+            if neuron.update(1.0) {
+                // Spiked!
+                assert!(neuron.is_refractory());
+                assert_eq!(neuron.membrane_potential(), neuron.params.reset);
+                return;
+            }
+        }
+        panic!("Neuron should have spiked with strong input");
+    }
+
+    #[test]
+    fn test_lif_refractory_period() {
+        let params = LIFParams {
+            refractory: 5.0,
+            ..Default::default()
+        };
+        let mut neuron = LIFNeuron::new(params);
+
+        // Force a spike
+        neuron.membrane_potential = params.threshold + 1.0;
+        neuron.update(1.0);
+
+        // Should be refractory
+        assert!(neuron.is_refractory());
+
+        // Should not spike during refractory
+        neuron.receive_input(100.0);
+        assert!(!neuron.update(1.0));
+
+        // After refractory period
+        for _ in 0..5 {
+            neuron.update(1.0);
+        }
+        assert!(!neuron.is_refractory());
+    }
+
+    #[test]
+    fn test_lif_leak_to_rest() {
+        let mut neuron = LIFNeuron::with_defaults();
+        neuron.membrane_potential = -55.0; // Above resting
+
+        // Without input, should decay toward resting
+        for _ in 0..100 {
+            neuron.update(1.0);
+        }
+
+        // Should be close to resting potential
+        assert!((neuron.membrane_potential() - (-65.0)).abs() < 1.0);
+    }
+
+    #[test]
+    fn test_params_validation() {
+        let invalid = LIFParams {
+            tau_m: -1.0,
+            ..Default::default()
+        };
+        assert!(invalid.validate().is_some());
+
+        let valid = LIFParams::default();
+        assert!(valid.validate().is_none());
+    }
+}

vendor/ruvector/examples/spiking-network/src/neuron/mod.rs

@@ -0,0 +1,41 @@
+//! Spiking neuron models.
+//!
+//! This module provides biologically-inspired neuron models optimized for
+//! event-driven computation. Neurons stay silent until a threshold is crossed,
+//! eliminating wasted cycles on unchanged state.
+//!
+//! ## Available Models
+//!
+//! - **LIF (Leaky Integrate-and-Fire)**: Simple, efficient, ASIC-friendly
+//! - **Izhikevich**: Rich dynamics, biologically plausible spiking patterns
+//!
+//! ## ASIC Considerations
+//!
+//! These models are designed for minimal silicon cost:
+//! - Fixed-point compatible arithmetic
+//! - No division operations in hot paths
+//! - Predictable memory access patterns
+//! - Branch-friendly state machines
+
+mod lif;
+mod izhikevich;
+mod traits;
+
+pub use lif::{LIFNeuron, LIFParams};
+pub use izhikevich::{IzhikevichNeuron, IzhikevichParams, IzhikevichType};
+pub use traits::{NeuronParams, SpikingNeuron, NeuronState};
+
+/// Default membrane time constant (ms)
+pub const DEFAULT_TAU_M: f32 = 20.0;
+
+/// Default spike threshold (mV)
+pub const DEFAULT_THRESHOLD: f32 = -50.0;
+
+/// Default resting potential (mV)
+pub const DEFAULT_RESTING: f32 = -65.0;
+
+/// Default reset potential (mV)
+pub const DEFAULT_RESET: f32 = -70.0;
+
+/// Default refractory period (ms)
+pub const DEFAULT_REFRACTORY: f32 = 2.0;

vendor/ruvector/examples/spiking-network/src/neuron/traits.rs

@@ -0,0 +1,108 @@
+//! Trait definitions for spiking neurons.
+
+use serde::{Deserialize, Serialize};
+
+/// State of a spiking neuron.
+#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
+pub struct NeuronState {
+    /// Membrane potential (mV)
+    pub membrane_potential: f32,
+    /// Time since last spike (ms), None if never spiked
+    pub time_since_spike: Option<f32>,
+    /// Whether the neuron is currently in refractory period
+    pub is_refractory: bool,
+    /// Accumulated input current for this timestep
+    pub input_current: f32,
+}
+
+impl Default for NeuronState {
+    fn default() -> Self {
+        Self {
+            membrane_potential: -65.0, // Resting potential
+            time_since_spike: None,
+            is_refractory: false,
+            input_current: 0.0,
+        }
+    }
+}
+
+/// Parameters that define a spiking neuron's behavior.
+pub trait NeuronParams: Clone + Send + Sync {
+    /// Get the spike threshold voltage
+    fn threshold(&self) -> f32;
+
+    /// Get the reset voltage after spike
+    fn reset_potential(&self) -> f32;
+
+    /// Get the resting membrane potential
+    fn resting_potential(&self) -> f32;
+
+    /// Get the refractory period in milliseconds
+    fn refractory_period(&self) -> f32;
+
+    /// Validate parameters, returning error message if invalid
+    fn validate(&self) -> Option<String>;
+}
+
+/// Core trait for spiking neuron models.
+///
+/// Implementing types should be efficient for ASIC deployment:
+/// - Avoid floating-point division in `update()`
+/// - Use predictable branching
+/// - Minimize memory footprint
+pub trait SpikingNeuron: Clone + Send + Sync {
+    /// Associated parameter type
+    type Params: NeuronParams;
+
+    /// Create a new neuron with given parameters
+    fn new(params: Self::Params) -> Self;
+
+    /// Get current neuron state
+    fn state(&self) -> NeuronState;
+
+    /// Get neuron parameters
+    fn params(&self) -> &Self::Params;
+
+    /// Add input current (from incoming spikes or external input)
+    ///
+    /// This is a sparse operation - only called when input arrives.
+    fn receive_input(&mut self, current: f32);
+
+    /// Update neuron state for one timestep.
+    ///
+    /// Returns `true` if the neuron fires a spike.
+    ///
+    /// # Arguments
+    /// * `dt` - Time step in milliseconds
+    ///
+    /// # ASIC Optimization
+    /// This is the hot path. Implementations should:
+    /// - Use only additions and multiplications
+    /// - Avoid conditional branches where possible
+    /// - Use fixed-point compatible operations
+    fn update(&mut self, dt: f32) -> bool;
+
+    /// Reset neuron to initial state
+    fn reset(&mut self);
+
+    /// Check if neuron is in refractory period
+    fn is_refractory(&self) -> bool;
+
+    /// Get membrane potential
+    fn membrane_potential(&self) -> f32;
+
+    /// Get time since last spike (if any)
+    fn time_since_spike(&self) -> Option<f32>;
+}
+
+/// Energy estimation for ASIC cost analysis.
+pub trait EnergyModel {
+    /// Estimate energy cost for a single update step (picojoules)
+    fn update_energy(&self) -> f32;
+
+    /// Estimate energy cost for spike emission (picojoules)
+    fn spike_energy(&self) -> f32;
+
+    /// Estimate silicon area (square micrometers)
+    fn silicon_area(&self) -> f32;
+}