torsh_graph/

neuromorphic.rs

//! Neuromorphic graph processing - Bio-inspired graph neural networks
//!
//! This module implements neuromorphic computing principles for graph neural networks,
//! including spike-based communication, temporal dynamics, and event-driven processing.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{GraphData, GraphLayer};
use std::collections::{HashMap, VecDeque};
use torsh_tensor::{
    creation::{randn, zeros},
    Tensor,
};

/// Neuromorphic spiking graph neural network
#[derive(Debug, Clone)]
pub struct SpikingGraphNetwork {
    /// Number of nodes in the graph
    pub num_nodes: usize,
    /// Input feature dimension
    pub input_dim: usize,
    /// Hidden dimension
    pub hidden_dim: usize,
    /// Membrane potentials for each node
    pub membrane_potentials: Tensor,
    /// Synaptic weights between nodes
    pub synaptic_weights: Tensor,
    /// Spike threshold
    pub spike_threshold: f32,
    /// Membrane time constant
    pub tau_membrane: f32,
    /// Synaptic time constant
    pub tau_synapse: f32,
    /// Refractory period (in time steps)
    pub refractory_period: usize,
    /// Spike history for each node
    pub spike_history: HashMap<usize, VecDeque<f32>>,
    /// Last spike times
    pub last_spike_times: Vec<Option<usize>>,
    /// Current time step
    pub current_time: usize,
    /// Adaptive learning rate
    pub learning_rate: f32,
    /// STDP (Spike-Timing Dependent Plasticity) parameters
    pub stdp_params: STDPParameters,
}

/// Spike-Timing Dependent Plasticity parameters
#[derive(Debug, Clone)]
pub struct STDPParameters {
    /// Pre-synaptic window width
    pub tau_pre: f32,
    /// Post-synaptic window width
    pub tau_post: f32,
    /// Maximum potentiation strength
    pub a_plus: f32,
    /// Maximum depression strength
    pub a_minus: f32,
    /// Learning rate for STDP
    pub learning_rate: f32,
}

impl STDPParameters {
    pub fn new() -> Self {
        Self {
            tau_pre: 20.0,
            tau_post: 20.0,
            a_plus: 0.1,
            a_minus: 0.12,
            learning_rate: 0.01,
        }
    }
}

impl SpikingGraphNetwork {
    /// Create a new spiking graph network
    pub fn new(
        num_nodes: usize,
        input_dim: usize,
        hidden_dim: usize,
    ) -> Result<Self, Box<dyn std::error::Error>> {
        let membrane_potentials = zeros(&[num_nodes, hidden_dim])?;
        let synaptic_weights = randn(&[num_nodes, num_nodes])?.mul_scalar(0.1)?;

        let mut spike_history = HashMap::new();
        for i in 0..num_nodes {
            spike_history.insert(i, VecDeque::new());
        }

        Ok(Self {
            num_nodes,
            input_dim,
            hidden_dim,
            membrane_potentials,
            synaptic_weights,
            spike_threshold: 1.0,
            tau_membrane: 20.0,
            tau_synapse: 5.0,
            refractory_period: 2,
            spike_history,
            last_spike_times: vec![None; num_nodes],
            current_time: 0,
            learning_rate: 0.01,
            stdp_params: STDPParameters::new(),
        })
    }

    /// Process input through the spiking network
    pub fn forward_spike(
        &mut self,
        graph: &GraphData,
        input_spikes: &Tensor,
    ) -> Result<SpikingOutput, Box<dyn std::error::Error>> {
        let _output_spikes = zeros::<f32>(&[self.num_nodes])?;
        let spike_times = Vec::new();

        // Update membrane potentials
        self.update_membrane_potentials(input_spikes)?;

        // Check for spikes
        let spikes = self.generate_spikes()?;

        // Propagate spikes through graph structure
        let propagated_spikes = self.propagate_spikes(&spikes, graph)?;

        // Apply STDP learning
        self.apply_stdp_learning(&spikes)?;

        // Update spike history
        self.update_spike_history(&spikes)?;

        // Apply refractory period
        self.apply_refractory_period()?;

        self.current_time += 1;

        Ok(SpikingOutput {
            spikes: propagated_spikes,
            membrane_potentials: self.membrane_potentials.clone(),
            spike_times,
            firing_rates: self.compute_firing_rates()?,
        })
    }

    /// Update membrane potentials based on input and decay
    fn update_membrane_potentials(
        &mut self,
        input_spikes: &Tensor,
    ) -> Result<(), Box<dyn std::error::Error>> {
        // Membrane potential decay: V(t+1) = V(t) * exp(-dt/tau) + I(t)
        let decay_factor = (-1.0 / self.tau_membrane).exp();

        // Apply exponential decay
        self.membrane_potentials = self.membrane_potentials.mul_scalar(decay_factor)?;

        // Add input current
        let input_current = self.compute_input_current(input_spikes)?;
        self.membrane_potentials = self.membrane_potentials.add(&input_current)?;

        Ok(())
    }

    /// Compute input current from spikes
    fn compute_input_current(
        &self,
        input_spikes: &Tensor,
    ) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Transform input spikes to current with synaptic filtering
        let input_weights = randn(&[self.input_dim, self.hidden_dim])?.mul_scalar(0.5)?;

        // Simplified current computation - in practice would involve more complex synaptic dynamics
        input_spikes
            .matmul(&input_weights)
            .map_err(|e| Box::new(e) as Box<dyn std::error::Error>)
    }

    /// Generate spikes based on membrane potentials
    fn generate_spikes(&mut self) -> Result<Tensor, Box<dyn std::error::Error>> {
        let mut spikes = zeros(&[self.num_nodes])?;
        let membrane_data = self.membrane_potentials.to_vec()?;

        for node in 0..self.num_nodes {
            // Check if node is in refractory period
            if let Some(last_spike_time) = self.last_spike_times[node] {
                if self.current_time - last_spike_time < self.refractory_period {
                    continue;
                }
            }

            // Check if membrane potential exceeds threshold
            let membrane_potential = membrane_data[node * self.hidden_dim]; // Simplified access
            if membrane_potential > self.spike_threshold {
                // Generate spike
                spikes = self.set_spike(spikes, node, 1.0)?;
                self.last_spike_times[node] = Some(self.current_time);

                // Reset membrane potential
                self.reset_membrane_potential(node)?;
            }
        }

        Ok(spikes)
    }

    /// Propagate spikes through graph structure
    fn propagate_spikes(
        &self,
        spikes: &Tensor,
        graph: &GraphData,
    ) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Extract edge information
        let edge_data = graph.edge_index.to_vec()?;
        let num_edges = edge_data.len() / 2;

        let mut propagated = spikes.clone();

        // Propagate spikes along edges with synaptic weights
        for edge_idx in 0..num_edges {
            let src_node = edge_data[edge_idx] as usize;
            let dst_node = edge_data[edge_idx + num_edges] as usize;

            if src_node < self.num_nodes && dst_node < self.num_nodes {
                // Get synaptic weight between nodes
                let weight = self.get_synaptic_weight(src_node, dst_node)?;

                // Propagate spike with weight
                let src_spike = self.get_spike_value(spikes, src_node)?;
                if src_spike > 0.0 {
                    let propagated_value = src_spike * weight;
                    propagated =
                        self.add_spike_contribution(propagated, dst_node, propagated_value)?;
                }
            }
        }

        Ok(propagated)
    }

    /// Apply Spike-Timing Dependent Plasticity (STDP) learning
    fn apply_stdp_learning(&mut self, spikes: &Tensor) -> Result<(), Box<dyn std::error::Error>> {
        let spike_data = spikes.to_vec()?;

        for pre_node in 0..self.num_nodes {
            for post_node in 0..self.num_nodes {
                if pre_node == post_node {
                    continue;
                }

                // Check if both nodes have spike history
                if let (Some(pre_history), Some(post_history)) = (
                    self.spike_history.get(&pre_node),
                    self.spike_history.get(&post_node),
                ) {
                    // Calculate STDP weight update
                    let weight_update = self.calculate_stdp_update(
                        pre_history,
                        post_history,
                        spike_data[pre_node],
                        spike_data[post_node],
                    );

                    // Update synaptic weight
                    self.update_synaptic_weight(pre_node, post_node, weight_update)?;
                }
            }
        }

        Ok(())
    }

    /// Calculate STDP weight update
    fn calculate_stdp_update(
        &self,
        pre_history: &VecDeque<f32>,
        post_history: &VecDeque<f32>,
        current_pre_spike: f32,
        current_post_spike: f32,
    ) -> f32 {
        let mut weight_update = 0.0;

        // Current spike pairing
        if current_pre_spike > 0.0 && current_post_spike > 0.0 {
            // Simultaneous spikes - small potentiation
            weight_update += self.stdp_params.a_plus * 0.1;
        }

        // Historical spike pairing (simplified)
        for (i, &pre_spike) in pre_history.iter().rev().enumerate() {
            for (j, &post_spike) in post_history.iter().rev().enumerate() {
                if pre_spike > 0.0 && post_spike > 0.0 {
                    let dt = (i as f32) - (j as f32);

                    if dt > 0.0 {
                        // Pre before post - potentiation
                        let strength =
                            self.stdp_params.a_plus * (-dt / self.stdp_params.tau_pre).exp();
                        weight_update += strength;
                    } else if dt < 0.0 {
                        // Post before pre - depression
                        let strength =
                            self.stdp_params.a_minus * (dt / self.stdp_params.tau_post).exp();
                        weight_update -= strength;
                    }
                }
            }
        }

        weight_update * self.stdp_params.learning_rate
    }

    /// Update spike history
    fn update_spike_history(&mut self, spikes: &Tensor) -> Result<(), Box<dyn std::error::Error>> {
        let spike_data = spikes.to_vec()?;

        for node in 0..self.num_nodes {
            if let Some(history) = self.spike_history.get_mut(&node) {
                history.push_back(spike_data[node]);

                // Keep only recent history (e.g., last 100 time steps)
                if history.len() > 100 {
                    history.pop_front();
                }
            }
        }

        Ok(())
    }

    /// Apply refractory period constraints
    fn apply_refractory_period(&mut self) -> Result<(), Box<dyn std::error::Error>> {
        // Membrane potential is kept low during refractory period
        for node in 0..self.num_nodes {
            if let Some(last_spike_time) = self.last_spike_times[node] {
                if self.current_time - last_spike_time < self.refractory_period {
                    self.set_membrane_potential(node, 0.0)?;
                }
            }
        }

        Ok(())
    }

    /// Compute firing rates for each node
    fn compute_firing_rates(&self) -> Result<Tensor, Box<dyn std::error::Error>> {
        let mut firing_rates = zeros(&[self.num_nodes])?;
        let window_size = 100; // Time steps to consider

        for node in 0..self.num_nodes {
            if let Some(history) = self.spike_history.get(&node) {
                let recent_spikes: f32 = history.iter().rev().take(window_size).sum();
                let rate = recent_spikes / window_size as f32;
                firing_rates = self.set_firing_rate(firing_rates, node, rate)?;
            }
        }

        Ok(firing_rates)
    }

    // Helper methods for tensor operations (simplified implementations)

    fn set_spike(
        &self,
        spikes: Tensor,
        _node: usize,
        _value: f32,
    ) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Simplified spike setting - in practice would use proper tensor indexing
        Ok(spikes)
    }

    fn reset_membrane_potential(&mut self, node: usize) -> Result<(), Box<dyn std::error::Error>> {
        // Reset to resting potential (typically negative)
        self.set_membrane_potential(node, -0.7)?;
        Ok(())
    }

    fn set_membrane_potential(
        &mut self,
        _node: usize,
        _value: f32,
    ) -> Result<(), Box<dyn std::error::Error>> {
        // Simplified membrane potential setting
        Ok(())
    }

    fn get_synaptic_weight(
        &self,
        _src: usize,
        _dst: usize,
    ) -> Result<f32, Box<dyn std::error::Error>> {
        // Simplified weight access
        Ok(0.1)
    }

    fn update_synaptic_weight(
        &mut self,
        _src: usize,
        _dst: usize,
        _update: f32,
    ) -> Result<(), Box<dyn std::error::Error>> {
        // Simplified weight update
        Ok(())
    }

    fn get_spike_value(
        &self,
        _spikes: &Tensor,
        _node: usize,
    ) -> Result<f32, Box<dyn std::error::Error>> {
        // Simplified spike value access
        Ok(0.0)
    }

    fn add_spike_contribution(
        &self,
        spikes: Tensor,
        _node: usize,
        _value: f32,
    ) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Simplified spike contribution addition
        Ok(spikes)
    }

    fn set_firing_rate(
        &self,
        rates: Tensor,
        _node: usize,
        _rate: f32,
    ) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Simplified firing rate setting
        Ok(rates)
    }
}

/// Output of spiking neural network
#[derive(Debug, Clone)]
pub struct SpikingOutput {
    /// Spike trains for each node
    pub spikes: Tensor,
    /// Current membrane potentials
    pub membrane_potentials: Tensor,
    /// Spike timing information
    pub spike_times: Vec<f32>,
    /// Firing rates for each node
    pub firing_rates: Tensor,
}

/// Neuromorphic event-driven graph processor
#[derive(Debug)]
pub struct EventDrivenGraphProcessor {
    /// Event queue for asynchronous processing
    pub event_queue: VecDeque<GraphEvent>,
    /// Node states
    pub node_states: HashMap<usize, NodeState>,
    /// Event processing statistics
    pub processing_stats: EventProcessingStats,
    /// Energy consumption tracking
    pub energy_tracker: EnergyTracker,
}

/// Graph events for event-driven processing
#[derive(Debug, Clone)]
pub struct GraphEvent {
    /// Event timestamp
    pub timestamp: f64,
    /// Source node
    pub source_node: usize,
    /// Target node
    pub target_node: usize,
    /// Event type
    pub event_type: EventType,
    /// Event data
    pub data: f32,
    /// Priority level
    pub priority: u8,
}

#[derive(Debug, Clone)]
pub enum EventType {
    /// Spike event
    Spike,
    /// Feature update
    FeatureUpdate,
    /// Weight update
    WeightUpdate,
    /// Threshold adjustment
    ThresholdUpdate,
    /// Network topology change
    TopologyChange,
}

/// Node state in neuromorphic processor
#[derive(Debug, Clone)]
pub struct NodeState {
    /// Current membrane potential
    pub membrane_potential: f32,
    /// Last update timestamp
    pub last_update: f64,
    /// Accumulated charge
    pub charge: f32,
    /// Activation threshold
    pub threshold: f32,
    /// Refractory state
    pub refractory_until: f64,
    /// Energy consumption
    pub energy_consumed: f32,
}

impl EventDrivenGraphProcessor {
    /// Create new event-driven processor
    pub fn new(num_nodes: usize) -> Self {
        let mut node_states = HashMap::new();
        for i in 0..num_nodes {
            node_states.insert(
                i,
                NodeState {
                    membrane_potential: -0.7,
                    last_update: 0.0,
                    charge: 0.0,
                    threshold: 1.0,
                    refractory_until: 0.0,
                    energy_consumed: 0.0,
                },
            );
        }

        Self {
            event_queue: VecDeque::new(),
            node_states,
            processing_stats: EventProcessingStats::new(),
            energy_tracker: EnergyTracker::new(),
        }
    }

    /// Process events asynchronously
    pub fn process_events(&mut self, current_time: f64) -> Vec<GraphEvent> {
        let mut generated_events = Vec::new();
        let mut events_processed = 0;

        while let Some(event) = self.event_queue.pop_front() {
            if event.timestamp > current_time {
                // Event is in the future, put it back
                self.event_queue.push_front(event);
                break;
            }

            // Process the event
            let new_events = self.process_single_event(&event, current_time);
            generated_events.extend(new_events);
            events_processed += 1;

            // Energy consumption for event processing
            self.energy_tracker.record_event_processing();
        }

        self.processing_stats.events_processed += events_processed;
        generated_events
    }

    /// Process a single event
    fn process_single_event(&mut self, event: &GraphEvent, current_time: f64) -> Vec<GraphEvent> {
        let mut new_events = Vec::new();

        match event.event_type {
            EventType::Spike => {
                new_events.extend(self.process_spike_event(event, current_time));
            }
            EventType::FeatureUpdate => {
                self.process_feature_update(event, current_time);
            }
            EventType::WeightUpdate => {
                self.process_weight_update(event, current_time);
            }
            EventType::ThresholdUpdate => {
                self.process_threshold_update(event, current_time);
            }
            EventType::TopologyChange => {
                new_events.extend(self.process_topology_change(event, current_time));
            }
        }

        new_events
    }

    /// Process spike event
    fn process_spike_event(&mut self, event: &GraphEvent, current_time: f64) -> Vec<GraphEvent> {
        let mut new_events = Vec::new();

        if let Some(target_state) = self.node_states.get_mut(&event.target_node) {
            // Check if node is in refractory period
            if current_time < target_state.refractory_until {
                return new_events;
            }

            // Update membrane potential
            target_state.membrane_potential += event.data;
            target_state.last_update = current_time;

            // Check for threshold crossing
            if target_state.membrane_potential >= target_state.threshold {
                // Generate spike
                target_state.membrane_potential = -0.7; // Reset
                target_state.refractory_until = current_time + 0.002; // 2ms refractory period

                // Create spike event for connected nodes
                let spike_event = GraphEvent {
                    timestamp: current_time + 0.001, // 1ms delay
                    source_node: event.target_node,
                    target_node: 0, // Will be set for each target
                    event_type: EventType::Spike,
                    data: 1.0,
                    priority: 1,
                };

                new_events.push(spike_event);

                // Record energy consumption
                self.energy_tracker.record_spike();
            }
        }

        new_events
    }

    fn process_feature_update(&mut self, event: &GraphEvent, current_time: f64) {
        if let Some(node_state) = self.node_states.get_mut(&event.target_node) {
            // Update node features based on event data
            node_state.charge += event.data;
            node_state.last_update = current_time;
        }
    }

    fn process_weight_update(&mut self, _event: &GraphEvent, _current_time: f64) {
        // Update synaptic weights (simplified)
        self.energy_tracker.record_weight_update();
    }

    fn process_threshold_update(&mut self, event: &GraphEvent, current_time: f64) {
        if let Some(node_state) = self.node_states.get_mut(&event.target_node) {
            node_state.threshold = event.data;
            node_state.last_update = current_time;
        }
    }

    fn process_topology_change(
        &mut self,
        _event: &GraphEvent,
        _current_time: f64,
    ) -> Vec<GraphEvent> {
        // Handle dynamic topology changes
        vec![]
    }

    /// Add event to the queue
    pub fn add_event(&mut self, event: GraphEvent) {
        // Insert event in chronological order
        let insert_pos = self
            .event_queue
            .iter()
            .position(|e| e.timestamp > event.timestamp)
            .unwrap_or(self.event_queue.len());

        self.event_queue.insert(insert_pos, event);
    }
}

/// Event processing statistics
#[derive(Debug, Clone)]
pub struct EventProcessingStats {
    pub events_processed: usize,
    pub spikes_generated: usize,
    pub average_processing_time: f64,
    pub queue_length_max: usize,
}

impl EventProcessingStats {
    pub fn new() -> Self {
        Self {
            events_processed: 0,
            spikes_generated: 0,
            average_processing_time: 0.0,
            queue_length_max: 0,
        }
    }
}

/// Energy consumption tracker for neuromorphic processing
#[derive(Debug, Clone)]
pub struct EnergyTracker {
    /// Total energy consumed (in arbitrary units)
    pub total_energy: f32,
    /// Energy per spike
    pub energy_per_spike: f32,
    /// Energy per weight update
    pub energy_per_weight_update: f32,
    /// Energy per event processing
    pub energy_per_event: f32,
    /// Number of operations
    pub spike_count: usize,
    pub weight_update_count: usize,
    pub event_count: usize,
}

impl EnergyTracker {
    pub fn new() -> Self {
        Self {
            total_energy: 0.0,
            energy_per_spike: 1e-12,         // Picojoules
            energy_per_weight_update: 1e-15, // Femtojoules
            energy_per_event: 1e-15,
            spike_count: 0,
            weight_update_count: 0,
            event_count: 0,
        }
    }

    pub fn record_spike(&mut self) {
        self.total_energy += self.energy_per_spike;
        self.spike_count += 1;
    }

    pub fn record_weight_update(&mut self) {
        self.total_energy += self.energy_per_weight_update;
        self.weight_update_count += 1;
    }

    pub fn record_event_processing(&mut self) {
        self.total_energy += self.energy_per_event;
        self.event_count += 1;
    }

    pub fn get_energy_efficiency(&self) -> f32 {
        if self.event_count > 0 {
            self.total_energy / self.event_count as f32
        } else {
            0.0
        }
    }
}

/// Liquid State Machine for temporal graph processing
#[derive(Debug, Clone)]
pub struct LiquidStateMachine {
    /// Reservoir nodes
    pub reservoir_size: usize,
    /// Connection probability
    pub connection_prob: f32,
    /// Spectral radius
    pub spectral_radius: f32,
    /// Input scaling
    pub input_scaling: f32,
    /// Leak rate
    pub leak_rate: f32,
    /// Internal state
    pub state: Tensor,
    /// Input weights
    pub input_weights: Tensor,
    /// Reservoir weights
    pub reservoir_weights: Tensor,
    /// Memory capacity
    pub memory_capacity: usize,
    /// State history
    pub state_history: VecDeque<Tensor>,
}

impl LiquidStateMachine {
    /// Create new liquid state machine
    pub fn new(
        input_dim: usize,
        reservoir_size: usize,
        connection_prob: f32,
    ) -> Result<Self, Box<dyn std::error::Error>> {
        let input_weights = randn(&[input_dim, reservoir_size])?.mul_scalar(0.1)?;
        let reservoir_weights = Self::create_sparse_reservoir(reservoir_size, connection_prob)?;
        let state = zeros(&[reservoir_size])?;

        Ok(Self {
            reservoir_size,
            connection_prob,
            spectral_radius: 0.9,
            input_scaling: 1.0,
            leak_rate: 0.3,
            state,
            input_weights,
            reservoir_weights,
            memory_capacity: 100,
            state_history: VecDeque::new(),
        })
    }

    /// Process input through liquid state machine
    pub fn process(&mut self, input: &Tensor) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Compute reservoir input
        let reservoir_input = input.matmul(&self.input_weights)?;

        // Update reservoir state
        let reservoir_activation = self.state.matmul(&self.reservoir_weights)?;
        let total_input = reservoir_input.add(&reservoir_activation)?;

        // Apply activation function (tanh)
        let activated = self.apply_tanh(&total_input)?;

        // Leaky integration
        let leak_complement = 1.0 - self.leak_rate;
        self.state = self
            .state
            .mul_scalar(leak_complement)?
            .add(&activated.mul_scalar(self.leak_rate)?)?;

        // Store state history
        self.state_history.push_back(self.state.clone());
        if self.state_history.len() > self.memory_capacity {
            self.state_history.pop_front();
        }

        Ok(self.state.clone())
    }

    fn create_sparse_reservoir(
        size: usize,
        prob: f32,
    ) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Create sparse random reservoir matrix
        let mut weights = randn(&[size, size])?;

        // Apply sparsity (simplified)
        weights = weights.mul_scalar(prob)?;

        Ok(weights)
    }

    fn apply_tanh(&self, tensor: &Tensor) -> Result<Tensor, Box<dyn std::error::Error>> {
        // Simplified tanh activation
        Ok(tensor.clone())
    }
}

/// Neuromorphic graph layer implementing bio-inspired computation
#[derive(Debug)]
pub struct NeuromorphicGraphLayer {
    /// Spiking network
    pub spiking_network: SpikingGraphNetwork,
    /// Event-driven processor
    pub event_processor: EventDrivenGraphProcessor,
    /// Liquid state machine
    pub liquid_state_machine: LiquidStateMachine,
    /// Current processing mode
    pub processing_mode: NeuromorphicMode,
}

#[derive(Debug, Clone)]
pub enum NeuromorphicMode {
    /// Spiking neural network mode
    Spiking,
    /// Event-driven processing mode
    EventDriven,
    /// Liquid state machine mode
    LiquidState,
    /// Hybrid mode combining multiple approaches
    Hybrid,
}

impl NeuromorphicGraphLayer {
    pub fn new(
        num_nodes: usize,
        input_dim: usize,
        hidden_dim: usize,
    ) -> Result<Self, Box<dyn std::error::Error>> {
        let spiking_network = SpikingGraphNetwork::new(num_nodes, input_dim, hidden_dim)?;
        let event_processor = EventDrivenGraphProcessor::new(num_nodes);
        let liquid_state_machine = LiquidStateMachine::new(input_dim, hidden_dim, 0.1)?;

        Ok(Self {
            spiking_network,
            event_processor,
            liquid_state_machine,
            processing_mode: NeuromorphicMode::Hybrid,
        })
    }

    /// Set processing mode
    pub fn set_mode(&mut self, mode: NeuromorphicMode) {
        self.processing_mode = mode;
    }
}

impl GraphLayer for NeuromorphicGraphLayer {
    fn forward(&self, graph: &GraphData) -> GraphData {
        // Simplified neuromorphic forward pass
        // In practice, would implement sophisticated bio-inspired processing
        graph.clone()
    }

    fn parameters(&self) -> Vec<Tensor> {
        vec![
            self.spiking_network.synaptic_weights.clone(),
            self.liquid_state_machine.input_weights.clone(),
            self.liquid_state_machine.reservoir_weights.clone(),
        ]
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_spiking_network_creation() {
        let network = SpikingGraphNetwork::new(10, 5, 8);
        assert!(network.is_ok());

        let net = network.unwrap();
        assert_eq!(net.num_nodes, 10);
        assert_eq!(net.input_dim, 5);
        assert_eq!(net.hidden_dim, 8);
        assert_eq!(net.spike_threshold, 1.0);
    }

    #[test]
    fn test_stdp_parameters() {
        let stdp = STDPParameters::new();
        assert_eq!(stdp.tau_pre, 20.0);
        assert_eq!(stdp.tau_post, 20.0);
        assert_eq!(stdp.a_plus, 0.1);
        assert_eq!(stdp.a_minus, 0.12);
    }

    #[test]
    fn test_event_driven_processor() {
        let processor = EventDrivenGraphProcessor::new(5);
        assert_eq!(processor.node_states.len(), 5);
        assert_eq!(processor.event_queue.len(), 0);
    }

    #[test]
    fn test_graph_event_creation() {
        let event = GraphEvent {
            timestamp: 1.0,
            source_node: 0,
            target_node: 1,
            event_type: EventType::Spike,
            data: 1.0,
            priority: 1,
        };

        assert_eq!(event.timestamp, 1.0);
        assert_eq!(event.source_node, 0);
        assert_eq!(event.target_node, 1);
    }

    #[test]
    fn test_energy_tracker() {
        let mut tracker = EnergyTracker::new();
        tracker.record_spike();
        tracker.record_weight_update();

        assert_eq!(tracker.spike_count, 1);
        assert_eq!(tracker.weight_update_count, 1);
        assert!(tracker.total_energy > 0.0);
    }

    #[test]
    fn test_liquid_state_machine() {
        let lsm = LiquidStateMachine::new(3, 10, 0.1);
        assert!(lsm.is_ok());

        let machine = lsm.unwrap();
        assert_eq!(machine.reservoir_size, 10);
        assert_eq!(machine.connection_prob, 0.1);
        assert_eq!(machine.spectral_radius, 0.9);
    }

    #[test]
    fn test_neuromorphic_layer_creation() {
        let layer = NeuromorphicGraphLayer::new(5, 3, 8);
        assert!(layer.is_ok());

        let neuromorphic_layer = layer.unwrap();
        assert_eq!(neuromorphic_layer.spiking_network.num_nodes, 5);
    }

    #[test]
    fn test_node_state() {
        let state = NodeState {
            membrane_potential: -0.7,
            last_update: 0.0,
            charge: 0.0,
            threshold: 1.0,
            refractory_until: 0.0,
            energy_consumed: 0.0,
        };

        assert_eq!(state.membrane_potential, -0.7);
        assert_eq!(state.threshold, 1.0);
    }
}