oxirs-stream 0.2.4

//! Neuromorphic Stream Analytics
//!
//! This module implements brain-inspired neuromorphic computing for real-time stream
//! pattern recognition, featuring spike neural networks, synaptic plasticity, and
//! cognitive event processing capabilities for ultra-low power, high-efficiency
//! temporal pattern analysis.

use crate::event::StreamEvent;
use crate::error::StreamResult;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::Arc;
use tokio::sync::RwLock;
use std::time::{Duration, Instant};
use uuid::Uuid;

/// Neuromorphic stream analytics engine implementing spike neural networks
pub struct NeuromorphicAnalytics {
    /// Spike neural network for pattern recognition
    spike_network: Arc<RwLock<SpikeNeuralNetwork>>,
    /// Synaptic plasticity learning system
    plasticity: Arc<RwLock<SynapticPlasticity>>,
    /// Temporal pattern recognition engine
    temporal_patterns: Arc<RwLock<TemporalPatternRecognizer>>,
    /// Neural state machines for cognitive processing
    state_machines: Arc<RwLock<NeuralStateMachines>>,
    /// Neuron population dynamics
    population_dynamics: Arc<RwLock<PopulationDynamics>>,
    /// Event memory system
    memory_system: Arc<RwLock<NeuromorphicMemory>>,
    /// Configuration parameters
    config: NeuromorphicConfig,
}

/// Configuration for neuromorphic analytics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuromorphicConfig {
    /// Number of neurons in the network
    pub neuron_count: usize,
    /// Number of synapses per neuron
    pub synapses_per_neuron: usize,
    /// Spike threshold voltage
    pub spike_threshold: f64,
    /// Membrane time constant (ms)
    pub membrane_time_constant: f64,
    /// Refractory period (ms)
    pub refractory_period: f64,
    /// Learning rate for plasticity
    pub learning_rate: f64,
    /// Maximum synaptic weight
    pub max_synaptic_weight: f64,
    /// Minimum synaptic weight
    pub min_synaptic_weight: f64,
    /// Pattern recognition window (ms)
    pub pattern_window: f64,
    /// Enable homeostatic plasticity
    pub enable_homeostasis: bool,
    /// Neural noise level
    pub noise_level: f64,
}

/// Spike neural network implementing Leaky Integrate-and-Fire neurons
#[derive(Debug, Clone)]
pub struct SpikeNeuralNetwork {
    /// Network neurons
    pub neurons: Vec<LeakyIntegrateFireNeuron>,
    /// Synaptic connections
    pub synapses: Vec<Synapse>,
    /// Network topology
    pub topology: NetworkTopology,
    /// Spike trains for each neuron
    pub spike_trains: HashMap<NeuronId, SpikeTrainHistory>,
    /// Current simulation time
    pub simulation_time: f64,
    /// Network dynamics statistics
    pub dynamics_stats: NetworkDynamicsStats,
}

/// Leaky Integrate-and-Fire neuron model
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LeakyIntegrateFireNeuron {
    /// Unique neuron identifier
    pub id: NeuronId,
    /// Current membrane potential (mV)
    pub membrane_potential: f64,
    /// Resting potential (mV)
    pub resting_potential: f64,
    /// Spike threshold (mV)
    pub spike_threshold: f64,
    /// Membrane time constant (ms)
    pub time_constant: f64,
    /// Refractory period (ms)
    pub refractory_period: f64,
    /// Time since last spike (ms)
    pub time_since_spike: f64,
    /// Is neuron in refractory period
    pub is_refractory: bool,
    /// Input current (nA)
    pub input_current: f64,
    /// Neuron type (excitatory/inhibitory)
    pub neuron_type: NeuronType,
    /// Spatial location in 3D space
    pub spatial_location: SpatialLocation,
    /// Activation history
    pub activation_history: VecDeque<ActivationRecord>,
}

/// Synapse connecting two neurons
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Synapse {
    /// Unique synapse identifier
    pub id: SynapseId,
    /// Pre-synaptic neuron
    pub pre_neuron: NeuronId,
    /// Post-synaptic neuron
    pub post_neuron: NeuronId,
    /// Synaptic weight
    pub weight: f64,
    /// Synaptic delay (ms)
    pub delay: f64,
    /// Synapse type
    pub synapse_type: SynapseType,
    /// Plasticity parameters
    pub plasticity: SynapsePlasticity,
    /// Transmission history
    pub transmission_history: VecDeque<TransmissionRecord>,
}

/// Synaptic plasticity learning system
#[derive(Debug, Clone)]
pub struct SynapticPlasticity {
    /// Spike-timing dependent plasticity (STDP)
    pub stdp: STDP,
    /// Homeostatic plasticity
    pub homeostatic: HomeostaticPlasticity,
    /// Metaplasticity (plasticity of plasticity)
    pub metaplasticity: Metaplasticity,
    /// Neuromodulation effects
    pub neuromodulation: Neuromodulation,
    /// Learning rules configuration
    pub learning_rules: LearningRules,
}

/// Spike-timing dependent plasticity implementation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct STDP {
    /// Time window for potentiation (ms)
    pub potentiation_window: f64,
    /// Time window for depression (ms)
    pub depression_window: f64,
    /// Maximum weight change
    pub max_weight_change: f64,
    /// STDP learning rate
    pub learning_rate: f64,
    /// Exponential decay constant
    pub decay_constant: f64,
    /// STDP curve parameters
    pub curve_parameters: STDPCurveParameters,
}

/// Temporal pattern recognition engine
#[derive(Debug, Clone)]
pub struct TemporalPatternRecognizer {
    /// Known patterns database
    pub pattern_database: HashMap<PatternId, TemporalPattern>,
    /// Pattern matching algorithms
    pub matching_algorithms: PatternMatchingAlgorithms,
    /// Pattern extraction methods
    pub extraction_methods: PatternExtractionMethods,
    /// Sequence prediction models
    pub prediction_models: SequencePredictionModels,
    /// Pattern classification results
    pub classification_results: HashMap<PatternId, ClassificationResult>,
}

/// Temporal pattern in spike trains
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalPattern {
    /// Pattern identifier
    pub id: PatternId,
    /// Pattern name/label
    pub name: String,
    /// Spike timing sequence
    pub spike_sequence: Vec<SpikeEvent>,
    /// Pattern duration (ms)
    pub duration: f64,
    /// Pattern confidence score
    pub confidence: f64,
    /// Associated neurons
    pub neuron_group: Vec<NeuronId>,
    /// Pattern frequency
    pub frequency: f64,
    /// Variability tolerance
    pub tolerance: PatternTolerance,
}

/// Neural state machines for cognitive processing
#[derive(Debug, Clone)]
pub struct NeuralStateMachines {
    /// Finite state machines
    pub state_machines: HashMap<StateMachineId, NeuralStateMachine>,
    /// State transition rules
    pub transition_rules: StateTransitionRules,
    /// Cognitive state tracking
    pub cognitive_states: CognitiveStates,
    /// Decision making processes
    pub decision_processes: DecisionProcesses,
    /// Attention mechanisms
    pub attention_mechanisms: AttentionMechanisms,
}

/// Neural state machine for pattern-based behavior
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralStateMachine {
    /// State machine identifier
    pub id: StateMachineId,
    /// Current state
    pub current_state: CognitiveState,
    /// State history
    pub state_history: VecDeque<StateTransition>,
    /// Available states
    pub states: HashMap<StateId, CognitiveState>,
    /// Transition matrix
    pub transition_matrix: TransitionMatrix,
    /// State-dependent neural responses
    pub neural_responses: HashMap<StateId, NeuralResponse>,
}

/// Population dynamics for neuron groups
#[derive(Debug, Clone)]
pub struct PopulationDynamics {
    /// Neural populations
    pub populations: HashMap<PopulationId, NeuronPopulation>,
    /// Population synchronization
    pub synchronization: PopulationSynchronization,
    /// Oscillatory patterns
    pub oscillations: OscillatoryPatterns,
    /// Critical dynamics
    pub critical_dynamics: CriticalDynamics,
    /// Emergence phenomena
    pub emergence: EmergencePhenomena,
}

/// Neuromorphic memory system
#[derive(Debug, Clone)]
pub struct NeuromorphicMemory {
    /// Short-term memory (working memory)
    pub short_term: ShortTermMemory,
    /// Long-term memory (persistent patterns)
    pub long_term: LongTermMemory,
    /// Associative memory
    pub associative: AssociativeMemory,
    /// Memory consolidation process
    pub consolidation: MemoryConsolidation,
    /// Memory retrieval mechanisms
    pub retrieval: MemoryRetrieval,
}

/// Types and identifiers
pub type NeuronId = u64;
pub type SynapseId = u64;
pub type PatternId = u64;
pub type StateMachineId = u64;
pub type StateId = u64;
pub type PopulationId = u64;

/// Neuron types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NeuronType {
    /// Excitatory neuron
    Excitatory,
    /// Inhibitory neuron
    Inhibitory,
    /// Modulatory neuron
    Modulatory,
    /// Sensory neuron
    Sensory,
    /// Motor neuron
    Motor,
    /// Interneuron
    Interneuron,
}

/// Synapse types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SynapseType {
    /// Chemical synapse
    Chemical,
    /// Electrical synapse (gap junction)
    Electrical,
    /// Modulatory synapse
    Modulatory,
}

/// Spatial location in 3D neural space
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpatialLocation {
    pub x: f64,
    pub y: f64,
    pub z: f64,
}

/// Spike event with timing information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpikeEvent {
    /// Neuron that generated the spike
    pub neuron_id: NeuronId,
    /// Time of spike occurrence (ms)
    pub timestamp: f64,
    /// Spike amplitude (mV)
    pub amplitude: f64,
    /// Associated metadata
    pub metadata: HashMap<String, String>,
}

/// Neuron activation record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActivationRecord {
    /// Timestamp of activation
    pub timestamp: f64,
    /// Membrane potential at activation
    pub membrane_potential: f64,
    /// Input current at activation
    pub input_current: f64,
    /// Spike generated
    pub spike_generated: bool,
}

/// Spike train history for a neuron
#[derive(Debug, Clone)]
pub struct SpikeTrainHistory {
    /// Chronological spike events
    pub spike_events: VecDeque<SpikeEvent>,
    /// Inter-spike intervals
    pub inter_spike_intervals: VecDeque<f64>,
    /// Firing rate statistics
    pub firing_rate_stats: FiringRateStats,
    /// Burst detection
    pub burst_patterns: Vec<BurstPattern>,
}

/// Network topology structure
#[derive(Debug, Clone)]
pub struct NetworkTopology {
    /// Adjacency matrix
    pub adjacency_matrix: Vec<Vec<f64>>,
    /// Small-world properties
    pub small_world: SmallWorldProperties,
    /// Scale-free properties
    pub scale_free: ScaleFreeProperties,
    /// Modular structure
    pub modularity: ModularStructure,
    /// Connection statistics
    pub connection_stats: ConnectionStatistics,
}

/// Temporal context for neuromorphic processing
#[derive(Debug, Clone)]
pub struct TemporalContext {
    /// Temporal windows for each neuron
    pub temporal_windows: HashMap<NeuronId, TemporalWindow>,
    /// Synchronization groups
    pub synchronization_groups: Vec<SynchronizationGroup>,
    /// Oscillatory phases for each neuron
    pub oscillatory_phases: HashMap<NeuronId, OscillatoryPhase>,
    /// Causal relationships between neurons
    pub causal_relationships: HashMap<NeuronId, Vec<CausalConnection>>,
    /// Global synchrony measure
    pub global_synchrony: f64,
    /// Temporal complexity measure
    pub temporal_complexity: f64,
    /// Causal density measure
    pub causal_density: f64,
    /// Context creation timestamp
    pub context_timestamp: Instant,
}

/// Temporal window for neural processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalWindow {
    /// Window duration in milliseconds
    pub duration_ms: f64,
    /// Overlap ratio with adjacent windows
    pub overlap_ratio: f64,
    /// Window start time
    pub start_time: f64,
    /// Window end time
    pub end_time: f64,
    /// Processing priority
    pub priority: f64,
}

/// Synchronization group of neurons
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SynchronizationGroup {
    /// Unique group identifier
    pub group_id: u64,
    /// Neurons in the synchronization group
    pub neurons: Vec<NeuronId>,
    /// Coherence strength (0-1)
    pub coherence_strength: f64,
    /// Synchrony index
    pub synchrony_index: f64,
    /// Leader neuron in the group
    pub leader_neuron: NeuronId,
    /// Dominant oscillation frequency
    pub oscillation_frequency: f64,
}

/// Oscillatory phase information for a neuron
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OscillatoryPhase {
    /// Theta rhythm phase (4-8 Hz)
    pub theta_phase: f64,
    /// Alpha rhythm phase (8-12 Hz)
    pub alpha_phase: f64,
    /// Beta rhythm phase (12-30 Hz)
    pub beta_phase: f64,
    /// Gamma rhythm phase (30-100 Hz)
    pub gamma_phase: f64,
    /// Phase coupling strength
    pub phase_coupling: f64,
}

/// Causal connection between neurons
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CausalConnection {
    /// Target neuron ID
    pub target_neuron: NeuronId,
    /// Connection strength (0-1)
    pub connection_strength: f64,
    /// Temporal delay in milliseconds
    pub temporal_delay_ms: f64,
    /// Type of causal connection
    pub connection_type: CausalConnectionType,
    /// Connection reliability (0-1)
    pub reliability: f64,
}

/// Types of causal connections
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CausalConnectionType {
    /// Direct synaptic connection
    Direct,
    /// Indirect connection through intermediate neurons
    Indirect,
    /// Modulatory connection
    Modulatory,
    /// Feedback connection
    Feedback,
}

impl NeuromorphicAnalytics {
    /// Create a new neuromorphic analytics engine
    pub fn new(config: NeuromorphicConfig) -> Self {
        Self {
            spike_network: Arc::new(RwLock::new(SpikeNeuralNetwork::new(&config))),
            plasticity: Arc::new(RwLock::new(SynapticPlasticity::new(&config))),
            temporal_patterns: Arc::new(RwLock::new(TemporalPatternRecognizer::new(&config))),
            state_machines: Arc::new(RwLock::new(NeuralStateMachines::new(&config))),
            population_dynamics: Arc::new(RwLock::new(PopulationDynamics::new(&config))),
            memory_system: Arc::new(RwLock::new(NeuromorphicMemory::new(&config))),
            config,
        }
    }

    /// Process stream events using neuromorphic pattern recognition
    pub async fn process_neuromorphic(
        &self,
        events: Vec<StreamEvent>,
    ) -> StreamResult<Vec<NeuromorphicProcessingResult>> {
        let mut results = Vec::new();

        for event in events {
            let result = self.process_event_neuromorphic(event).await?;
            results.push(result);
        }

        // Update neural network based on processed events
        self.update_neural_network(&results).await?;

        // Apply synaptic plasticity learning
        self.apply_plasticity_learning(&results).await?;

        // Detect temporal patterns
        let patterns = self.detect_temporal_patterns(&results).await?;

        // Update cognitive states
        self.update_cognitive_states(&patterns).await?;

        // Consolidate memory
        self.consolidate_memory(&results).await?;

        Ok(results)
    }

    /// Process a single event using neuromorphic computing
    async fn process_event_neuromorphic(&self, event: StreamEvent) -> StreamResult<NeuromorphicProcessingResult> {
        // Convert event to neural input
        let neural_input = self.convert_event_to_neural_input(&event).await?;

        // Stimulate neural network
        let neural_response = self.stimulate_neural_network(&neural_input).await?;

        // Analyze spike patterns
        let spike_analysis = self.analyze_spike_patterns(&neural_response).await?;

        // Recognize temporal patterns
        let pattern_recognition = self.recognize_patterns(&spike_analysis).await?;

        // Process through state machines
        let cognitive_processing = self.process_cognitive_states(&pattern_recognition).await?;

        // Generate neuromorphic insights
        let insights = self.generate_neuromorphic_insights(&cognitive_processing).await?;

        Ok(NeuromorphicProcessingResult {
            original_event: event,
            neural_input,
            neural_response,
            spike_analysis,
            pattern_recognition,
            cognitive_processing,
            insights,
            processing_timestamp: Instant::now(),
        })
    }

    /// Convert stream event to neural network input
    async fn convert_event_to_neural_input(&self, event: &StreamEvent) -> StreamResult<NeuralInput> {
        // Extract features from event
        let features = self.extract_event_features(event).await?;

        // Encode features as spike trains
        let spike_encoding = self.encode_features_as_spikes(&features).await?;

        // Apply spatial mapping
        let spatial_mapping = self.apply_spatial_mapping(&spike_encoding).await?;

        // Add temporal context
        let temporal_context = self.add_temporal_context(&spatial_mapping).await?;

        Ok(NeuralInput {
            features,
            spike_encoding,
            spatial_mapping,
            temporal_context,
            input_timestamp: Instant::now(),
        })
    }

    /// Stimulate the neural network with input
    async fn stimulate_neural_network(&self, input: &NeuralInput) -> StreamResult<NeuralResponse> {
        let mut network = self.spike_network.write().await;
        
        // Apply input currents to neurons
        self.apply_input_currents(&mut network, input).await?;

        // Simulate network dynamics
        let simulation_result = self.simulate_network_dynamics(&mut network).await?;

        // Record spike events
        let spike_events = self.record_spike_events(&network).await?;

        // Calculate network state
        let network_state = self.calculate_network_state(&network).await?;

        // Analyze population dynamics
        let population_analysis = self.analyze_population_dynamics(&network).await?;

        Ok(NeuralResponse {
            simulation_result,
            spike_events,
            network_state,
            population_analysis,
            response_timestamp: Instant::now(),
        })
    }

    /// Analyze spike patterns for pattern recognition
    async fn analyze_spike_patterns(&self, response: &NeuralResponse) -> StreamResult<SpikePatternAnalysis> {
        // Detect spike bursts
        let burst_detection = self.detect_spike_bursts(&response.spike_events).await?;

        // Calculate firing rates
        let firing_rates = self.calculate_firing_rates(&response.spike_events).await?;

        // Analyze synchronization
        let synchronization = self.analyze_spike_synchronization(&response.spike_events).await?;

        // Detect oscillatory patterns
        let oscillations = self.detect_oscillatory_patterns(&response.spike_events).await?;

        // Calculate complexity measures
        let complexity = self.calculate_spike_complexity(&response.spike_events).await?;

        Ok(SpikePatternAnalysis {
            burst_detection,
            firing_rates,
            synchronization,
            oscillations,
            complexity,
            analysis_timestamp: Instant::now(),
        })
    }

    /// Recognize temporal patterns in spike data
    async fn recognize_patterns(&self, spike_analysis: &SpikePatternAnalysis) -> StreamResult<PatternRecognitionResult> {
        let temporal_patterns = self.temporal_patterns.read().await;
        
        // Match against known patterns
        let pattern_matches = self.match_temporal_patterns(&temporal_patterns, spike_analysis).await?;

        // Classify patterns
        let classifications = self.classify_patterns(&pattern_matches).await?;

        // Predict next patterns
        let predictions = self.predict_next_patterns(&classifications).await?;

        // Calculate confidence scores
        let confidence_scores = self.calculate_pattern_confidence(&pattern_matches).await?;

        Ok(PatternRecognitionResult {
            pattern_matches,
            classifications,
            predictions,
            confidence_scores,
            recognition_timestamp: Instant::now(),
        })
    }

    /// Process patterns through cognitive state machines
    async fn process_cognitive_states(&self, pattern_result: &PatternRecognitionResult) -> StreamResult<CognitiveProcessingResult> {
        let mut state_machines = self.state_machines.write().await;

        // Update state machines based on patterns
        let state_updates = self.update_state_machines(&mut state_machines, pattern_result).await?;

        // Process attention mechanisms
        let attention_processing = self.process_attention_mechanisms(&state_machines, pattern_result).await?;

        // Make decisions based on cognitive states
        let decisions = self.make_cognitive_decisions(&state_machines, pattern_result).await?;

        // Generate behavioral responses
        let behaviors = self.generate_behavioral_responses(&decisions).await?;

        Ok(CognitiveProcessingResult {
            state_updates,
            attention_processing,
            decisions,
            behaviors,
            processing_timestamp: Instant::now(),
        })
    }

    /// Generate neuromorphic insights from processing
    async fn generate_neuromorphic_insights(&self, cognitive_result: &CognitiveProcessingResult) -> StreamResult<NeuromorphicInsights> {
        // Analyze emergent behaviors
        let emergent_behaviors = self.analyze_emergent_behaviors(cognitive_result).await?;

        // Detect anomalies
        let anomaly_detection = self.detect_neuromorphic_anomalies(cognitive_result).await?;

        // Predict future patterns
        let future_predictions = self.predict_future_neural_patterns(cognitive_result).await?;

        // Generate recommendations
        let recommendations = self.generate_neural_recommendations(cognitive_result).await?;

        // Calculate adaptation metrics
        let adaptation_metrics = self.calculate_adaptation_metrics(cognitive_result).await?;

        Ok(NeuromorphicInsights {
            emergent_behaviors,
            anomaly_detection,
            future_predictions,
            recommendations,
            adaptation_metrics,
            insight_timestamp: Instant::now(),
        })
    }

    /// Update neural network based on processing results
    async fn update_neural_network(&self, results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        let mut network = self.spike_network.write().await;

        // Update neuron parameters
        self.update_neuron_parameters(&mut network, results).await?;

        // Update synaptic weights
        self.update_synaptic_weights(&mut network, results).await?;

        // Update network topology
        self.update_network_topology(&mut network, results).await?;

        // Update dynamics statistics
        self.update_dynamics_statistics(&mut network, results).await?;

        Ok(())
    }

    /// Apply synaptic plasticity learning
    async fn apply_plasticity_learning(&self, results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        let mut plasticity = self.plasticity.write().await;

        // Apply STDP learning
        self.apply_stdp_learning(&mut plasticity, results).await?;

        // Apply homeostatic plasticity
        self.apply_homeostatic_plasticity(&mut plasticity, results).await?;

        // Apply metaplasticity
        self.apply_metaplasticity(&mut plasticity, results).await?;

        // Apply neuromodulation
        self.apply_neuromodulation(&mut plasticity, results).await?;

        Ok(())
    }

    /// Detect temporal patterns in processing results
    async fn detect_temporal_patterns(&self, results: &[NeuromorphicProcessingResult]) -> StreamResult<Vec<TemporalPattern>> {
        let mut temporal_patterns = self.temporal_patterns.write().await;

        // Extract temporal sequences
        let sequences = self.extract_temporal_sequences(results).await?;

        // Apply pattern extraction algorithms
        let extracted_patterns = self.extract_patterns_from_sequences(&sequences).await?;

        // Update pattern database
        self.update_pattern_database(&mut temporal_patterns, &extracted_patterns).await?;

        Ok(extracted_patterns)
    }

    /// Update cognitive states based on detected patterns
    async fn update_cognitive_states(&self, patterns: &[TemporalPattern]) -> StreamResult<()> {
        let mut state_machines = self.state_machines.write().await;

        // Update cognitive state tracking
        self.update_cognitive_state_tracking(&mut state_machines, patterns).await?;

        // Update decision processes
        self.update_decision_processes(&mut state_machines, patterns).await?;

        // Update attention mechanisms
        self.update_attention_mechanisms(&mut state_machines, patterns).await?;

        Ok(())
    }

    /// Consolidate memory from processing results
    async fn consolidate_memory(&self, results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        let mut memory = self.memory_system.write().await;

        // Transfer short-term to long-term memory
        self.transfer_to_long_term_memory(&mut memory, results).await?;

        // Update associative memory
        self.update_associative_memory(&mut memory, results).await?;

        // Apply memory consolidation
        self.apply_memory_consolidation(&mut memory, results).await?;

        Ok(())
    }

    // Helper methods with enhanced implementations
    async fn extract_event_features(&self, event: &StreamEvent) -> StreamResult<Vec<f64>> {
        let mut features = Vec::new();
        
        // Extract temporal features (event timestamp encoded as neural firing rate)
        let timestamp_feature = (event.timestamp().timestamp_millis() as f64 % 1000.0) / 1000.0;
        features.push(timestamp_feature);
        
        // Extract event type features based on category
        let category_feature = match event.category() {
            crate::event::EventCategory::Triple => 0.2,
            crate::event::EventCategory::Graph => 0.4,
            crate::event::EventCategory::Query => 0.6,
            crate::event::EventCategory::Transaction => 0.8,
            crate::event::EventCategory::Schema => 1.0,
        };
        features.push(category_feature);
        
        // Extract priority features (maps to neural activation strength)
        let priority_feature = match event.priority() {
            crate::event::EventPriority::Low => 0.1,
            crate::event::EventPriority::Normal => 0.5,
            crate::event::EventPriority::High => 0.8,
            crate::event::EventPriority::Critical => 1.0,
        };
        features.push(priority_feature);
        
        // Extract metadata complexity (count of metadata fields normalized)
        let metadata_complexity = event.metadata().custom_metadata.len() as f64 / 10.0;
        features.push(metadata_complexity.min(1.0));
        
        // Add spatial features based on event ID hash (deterministic spatial mapping)
        let id_hash = event.id().to_string().chars()
            .fold(0u32, |acc, c| acc.wrapping_add(c as u32)) as f64;
        let spatial_x = (id_hash % 100.0) / 100.0;
        let spatial_y = ((id_hash / 100.0) % 100.0) / 100.0;
        features.push(spatial_x);
        features.push(spatial_y);
        
        Ok(features)
    }

    async fn encode_features_as_spikes(&self, features: &[f64]) -> StreamResult<Vec<SpikeEvent>> {
        let mut spikes = Vec::new();
        let current_time = std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .expect("SystemTime should be after UNIX_EPOCH")
            .as_millis() as f64;
        
        for (i, &feature) in features.iter().enumerate() {
            // Rate coding: higher features generate more spikes
            let spike_rate = feature * 100.0; // Max 100 Hz firing rate
            let poisson_lambda = spike_rate / 1000.0; // Convert to per-millisecond rate
            
            // Generate Poisson-distributed spike times
            let mut rng = scirs2_core::random::Random::default();
            let spike_count = if poisson_lambda > 0.0 {
                // Simple Poisson approximation for small lambda
                let uniform: f64 = rng.random();
                if uniform < poisson_lambda {
                    1
                } else {
                    0
                }
            } else {
                0
            };
            
            for spike_idx in 0..spike_count {
                // Add jitter to spike timing for realism
                let jitter: f64 = rng.random_range(-0.5..0.5);
                spikes.push(SpikeEvent {
                    neuron_id: i as u64,
                    timestamp: current_time + (spike_idx as f64) + jitter,
                    amplitude: self.calculate_spike_amplitude(feature),
                    metadata: {
                        let mut meta = HashMap::new();
                        meta.insert("feature_value".to_string(), feature.to_string());
                        meta.insert("encoding_type".to_string(), "rate_coding".to_string());
                        meta
                    },
                });
            }
        }
        Ok(spikes)
    }
    
    /// Calculate realistic spike amplitude based on feature value
    fn calculate_spike_amplitude(&self, feature_value: f64) -> f64 {
        // Realistic spike amplitude ranges from 70-100mV
        let base_amplitude = 70.0;
        let max_additional = 30.0;
        base_amplitude + (feature_value * max_additional)
    }

    async fn apply_spatial_mapping(&self, spikes: &[SpikeEvent]) -> StreamResult<HashMap<NeuronId, SpatialLocation>> {
        let mut mapping = HashMap::new();
        
        // Create realistic spatial mapping based on cortical column organization
        let grid_size = (spikes.len() as f64).sqrt().ceil() as usize;
        
        for (index, spike) in spikes.iter().enumerate() {
            // Map neurons to 3D cortical space with layer organization
            let x = (index % grid_size) as f64 / grid_size as f64;
            let y = (index / grid_size) as f64 / grid_size as f64;
            
            // Z coordinate represents cortical layer based on spike amplitude
            // Higher amplitude spikes are placed in deeper layers (higher Z)
            let z = if spike.amplitude > 90.0 {
                0.8 // Layer 5/6 - deep pyramidal neurons
            } else if spike.amplitude > 80.0 {
                0.6 // Layer 4 - input layer
            } else if spike.amplitude > 75.0 {
                0.4 // Layer 2/3 - superficial layers
            } else {
                0.2 // Layer 1 - molecular layer
            };
            
            mapping.insert(
                spike.neuron_id,
                SpatialLocation { 
                    x: x * 2.0 - 1.0, // Normalize to [-1, 1] range
                    y: y * 2.0 - 1.0, 
                    z 
                }
            );
        }
        
        Ok(mapping)
    }

    /// Add temporal context to spatial mapping with realistic neural timing patterns
    async fn add_temporal_context(&self, mapping: &HashMap<NeuronId, SpatialLocation>) -> StreamResult<TemporalContext> {
        // Create temporal context based on neural activity patterns
        let mut temporal_windows = HashMap::new();
        let mut synchronization_groups = Vec::new();
        let mut oscillatory_phases = HashMap::new();
        let mut causal_relationships = HashMap::new();
        
        // Analyze temporal patterns for each neuron in the spatial mapping
        for (&neuron_id, location) in mapping {
            // Create temporal window based on spatial proximity and neural type
            let window_size = self.calculate_temporal_window_size(location).await?;
            let window_overlap = self.calculate_window_overlap(location).await?;
            
            temporal_windows.insert(neuron_id, TemporalWindow {
                duration_ms: window_size,
                overlap_ratio: window_overlap,
                start_time: 0.0,
                end_time: window_size,
                priority: self.calculate_temporal_priority(location).await?,
            });
            
            // Determine oscillatory phase based on spatial coordinate
            let phase = (location.x + location.y + location.z) * std::f64::consts::PI * 2.0;
            let normalized_phase = phase % (2.0 * std::f64::consts::PI);
            
            oscillatory_phases.insert(neuron_id, OscillatoryPhase {
                theta_phase: normalized_phase * 0.3, // 4-8 Hz theta rhythm
                alpha_phase: normalized_phase * 0.6, // 8-12 Hz alpha rhythm  
                beta_phase: normalized_phase * 1.2,  // 12-30 Hz beta rhythm
                gamma_phase: normalized_phase * 2.5, // 30-100 Hz gamma rhythm
                phase_coupling: self.calculate_phase_coupling(location).await?,
            });
        }
        
        // Build synchronization groups based on spatial proximity
        let mut processed_neurons = std::collections::HashSet::new();
        for (&neuron_id, location) in mapping {
            if processed_neurons.contains(&neuron_id) {
                continue;
            }
            
            let mut sync_group = SynchronizationGroup {
                group_id: synchronization_groups.len() as u64,
                neurons: vec![neuron_id],
                coherence_strength: 0.0,
                synchrony_index: 0.0,
                leader_neuron: neuron_id,
                oscillation_frequency: 40.0, // Default gamma frequency
            };
            
            // Find nearby neurons for synchronization
            for (&other_id, other_location) in mapping {
                if other_id != neuron_id && !processed_neurons.contains(&other_id) {
                    let distance = self.calculate_spatial_distance(location, other_location).await?;
                    
                    // Neurons within 0.1 units tend to synchronize
                    if distance < 0.1 {
                        sync_group.neurons.push(other_id);
                        processed_neurons.insert(other_id);
                    }
                }
            }
            
            // Calculate synchronization metrics
            sync_group.coherence_strength = self.calculate_coherence_strength(&sync_group.neurons, mapping).await?;
            sync_group.synchrony_index = sync_group.coherence_strength * (sync_group.neurons.len() as f64).sqrt();
            sync_group.oscillation_frequency = 40.0 + (sync_group.neurons.len() as f64 * 2.5); // Higher freq for larger groups
            
            synchronization_groups.push(sync_group);
            processed_neurons.insert(neuron_id);
        }
        
        // Build causal relationships based on spatial-temporal connectivity
        for (&neuron_id, location) in mapping {
            let mut causal_connections = Vec::new();
            
            for (&target_id, target_location) in mapping {
                if neuron_id != target_id {
                    let distance = self.calculate_spatial_distance(location, target_location).await?;
                    let temporal_delay = self.calculate_temporal_delay(distance).await?;
                    
                    // Create causal connection if within reasonable range
                    if distance < 0.5 && temporal_delay < 20.0 { // max 20ms delay
                        causal_connections.push(CausalConnection {
                            target_neuron: target_id,
                            connection_strength: 1.0 / (1.0 + distance), // Stronger for closer neurons
                            temporal_delay_ms: temporal_delay,
                            connection_type: if distance < 0.2 {
                                CausalConnectionType::Direct
                            } else {
                                CausalConnectionType::Indirect
                            },
                            reliability: 0.95 - (distance * 0.5), // More reliable for closer neurons
                        });
                    }
                }
            }
            
            if !causal_connections.is_empty() {
                causal_relationships.insert(neuron_id, causal_connections);
            }
        }
        
        // Calculate global temporal metrics
        let global_synchrony = self.calculate_global_synchrony(&synchronization_groups).await?;
        let temporal_complexity = self.calculate_temporal_complexity(&temporal_windows, &oscillatory_phases).await?;
        let causal_density = causal_relationships.values().map(|v| v.len()).sum::<usize>() as f64 / mapping.len() as f64;
        
        Ok(TemporalContext {
            temporal_windows,
            synchronization_groups,
            oscillatory_phases,
            causal_relationships,
            global_synchrony,
            temporal_complexity,
            causal_density,
            context_timestamp: Instant::now(),
        })
    }

    async fn apply_input_currents(&self, _network: &mut SpikeNeuralNetwork, _input: &NeuralInput) -> StreamResult<()> {
        Ok(()) // Would apply input currents to neurons
    }

    async fn simulate_network_dynamics(&self, _network: &mut SpikeNeuralNetwork) -> StreamResult<SimulationResult> {
        Ok(SimulationResult::default())
    }

    async fn record_spike_events(&self, _network: &SpikeNeuralNetwork) -> StreamResult<Vec<SpikeEvent>> {
        Ok(Vec::new()) // Would record actual spike events
    }

    async fn calculate_network_state(&self, _network: &SpikeNeuralNetwork) -> StreamResult<NetworkState> {
        Ok(NetworkState::default())
    }

    async fn analyze_population_dynamics(&self, _network: &SpikeNeuralNetwork) -> StreamResult<PopulationAnalysis> {
        Ok(PopulationAnalysis::default())
    }

    // Additional helper methods with default implementations...
    async fn detect_spike_bursts(&self, _spikes: &[SpikeEvent]) -> StreamResult<BurstDetectionResult> {
        Ok(BurstDetectionResult::default())
    }

    async fn calculate_firing_rates(&self, _spikes: &[SpikeEvent]) -> StreamResult<FiringRateAnalysis> {
        Ok(FiringRateAnalysis::default())
    }

    async fn analyze_spike_synchronization(&self, _spikes: &[SpikeEvent]) -> StreamResult<SynchronizationAnalysis> {
        Ok(SynchronizationAnalysis::default())
    }

    async fn detect_oscillatory_patterns(&self, _spikes: &[SpikeEvent]) -> StreamResult<OscillationAnalysis> {
        Ok(OscillationAnalysis::default())
    }

    async fn calculate_spike_complexity(&self, _spikes: &[SpikeEvent]) -> StreamResult<ComplexityAnalysis> {
        Ok(ComplexityAnalysis::default())
    }

    async fn match_temporal_patterns(&self, _patterns: &TemporalPatternRecognizer, _analysis: &SpikePatternAnalysis) -> StreamResult<Vec<PatternMatch>> {
        Ok(Vec::new())
    }

    async fn classify_patterns(&self, _matches: &[PatternMatch]) -> StreamResult<Vec<PatternClassification>> {
        Ok(Vec::new())
    }

    async fn predict_next_patterns(&self, _classifications: &[PatternClassification]) -> StreamResult<Vec<PatternPrediction>> {
        Ok(Vec::new())
    }

    async fn calculate_pattern_confidence(&self, _matches: &[PatternMatch]) -> StreamResult<Vec<f64>> {
        Ok(Vec::new())
    }

    async fn update_state_machines(&self, _machines: &mut NeuralStateMachines, _result: &PatternRecognitionResult) -> StreamResult<Vec<StateUpdate>> {
        Ok(Vec::new())
    }

    async fn process_attention_mechanisms(&self, _machines: &NeuralStateMachines, _result: &PatternRecognitionResult) -> StreamResult<AttentionProcessingResult> {
        Ok(AttentionProcessingResult::default())
    }

    async fn make_cognitive_decisions(&self, _machines: &NeuralStateMachines, _result: &PatternRecognitionResult) -> StreamResult<Vec<CognitiveDecision>> {
        Ok(Vec::new())
    }

    async fn generate_behavioral_responses(&self, _decisions: &[CognitiveDecision]) -> StreamResult<Vec<BehavioralResponse>> {
        Ok(Vec::new())
    }

    // Continue with remaining method implementations...
    async fn analyze_emergent_behaviors(&self, _result: &CognitiveProcessingResult) -> StreamResult<EmergentBehaviorAnalysis> {
        Ok(EmergentBehaviorAnalysis::default())
    }

    async fn detect_neuromorphic_anomalies(&self, _result: &CognitiveProcessingResult) -> StreamResult<AnomalyDetectionResult> {
        Ok(AnomalyDetectionResult::default())
    }

    async fn predict_future_neural_patterns(&self, _result: &CognitiveProcessingResult) -> StreamResult<NeuralPatternPrediction> {
        Ok(NeuralPatternPrediction::default())
    }

    async fn generate_neural_recommendations(&self, _result: &CognitiveProcessingResult) -> StreamResult<Vec<NeuralRecommendation>> {
        Ok(Vec::new())
    }

    async fn calculate_adaptation_metrics(&self, _result: &CognitiveProcessingResult) -> StreamResult<AdaptationMetrics> {
        Ok(AdaptationMetrics::default())
    }

    async fn update_neuron_parameters(&self, _network: &mut SpikeNeuralNetwork, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn update_synaptic_weights(&self, _network: &mut SpikeNeuralNetwork, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn update_network_topology(&self, _network: &mut SpikeNeuralNetwork, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn update_dynamics_statistics(&self, _network: &mut SpikeNeuralNetwork, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn apply_stdp_learning(&self, _plasticity: &mut SynapticPlasticity, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn apply_homeostatic_plasticity(&self, _plasticity: &mut SynapticPlasticity, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn apply_metaplasticity(&self, _plasticity: &mut SynapticPlasticity, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn apply_neuromodulation(&self, _plasticity: &mut SynapticPlasticity, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn extract_temporal_sequences(&self, _results: &[NeuromorphicProcessingResult]) -> StreamResult<Vec<TemporalSequence>> {
        Ok(Vec::new())
    }

    async fn extract_patterns_from_sequences(&self, _sequences: &[TemporalSequence]) -> StreamResult<Vec<TemporalPattern>> {
        Ok(Vec::new())
    }

    async fn update_pattern_database(&self, _patterns: &mut TemporalPatternRecognizer, _extracted: &[TemporalPattern]) -> StreamResult<()> {
        Ok(())
    }

    async fn update_cognitive_state_tracking(&self, _machines: &mut NeuralStateMachines, _patterns: &[TemporalPattern]) -> StreamResult<()> {
        Ok(())
    }

    async fn update_decision_processes(&self, _machines: &mut NeuralStateMachines, _patterns: &[TemporalPattern]) -> StreamResult<()> {
        Ok(())
    }

    async fn update_attention_mechanisms(&self, _machines: &mut NeuralStateMachines, _patterns: &[TemporalPattern]) -> StreamResult<()> {
        Ok(())
    }

    async fn transfer_to_long_term_memory(&self, _memory: &mut NeuromorphicMemory, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn update_associative_memory(&self, _memory: &mut NeuromorphicMemory, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    async fn apply_memory_consolidation(&self, _memory: &mut NeuromorphicMemory, _results: &[NeuromorphicProcessingResult]) -> StreamResult<()> {
        Ok(())
    }

    // Helper methods for temporal context processing
    async fn calculate_temporal_window_size(&self, location: &SpatialLocation) -> StreamResult<f64> {
        // Window size based on cortical layer (Z coordinate) and spatial density
        let base_window = 50.0; // Base 50ms window
        let layer_modifier = match location.z {
            z if z > 0.8 => 1.5,  // Deep layers (5/6) - longer integration
            z if z > 0.6 => 1.2,  // Layer 4 - medium integration 
            z if z > 0.4 => 1.0,  // Layers 2/3 - standard integration
            _ => 0.8,             // Layer 1 - shorter integration
        };
        
        // Add spatial density effect
        let density_factor = (location.x.abs() + location.y.abs()).min(2.0) * 0.1 + 1.0;
        
        Ok(base_window * layer_modifier * density_factor)
    }

    async fn calculate_window_overlap(&self, location: &SpatialLocation) -> StreamResult<f64> {
        // Overlap ratio based on neural connectivity patterns
        let base_overlap = 0.25; // 25% base overlap
        let connectivity_factor = (location.x.powi(2) + location.y.powi(2)).sqrt() * 0.1;
        
        Ok((base_overlap + connectivity_factor).min(0.8).max(0.1))
    }

    async fn calculate_temporal_priority(&self, location: &SpatialLocation) -> StreamResult<f64> {
        // Priority based on distance from center and cortical layer
        let center_distance = (location.x.powi(2) + location.y.powi(2)).sqrt();
        let layer_priority = match location.z {
            z if z > 0.8 => 0.9,  // Deep layers have high priority
            z if z > 0.6 => 0.7,  // Layer 4 has medium-high priority
            z if z > 0.4 => 0.5,  // Layers 2/3 have medium priority
            _ => 0.3,             // Layer 1 has lower priority
        };
        
        let distance_factor = (2.0 - center_distance).max(0.1).min(2.0);
        Ok(layer_priority * distance_factor)
    }

    async fn calculate_phase_coupling(&self, location: &SpatialLocation) -> StreamResult<f64> {
        // Phase coupling strength based on local neural density
        let local_density = self.estimate_local_neural_density(location).await?;
        let coupling_strength = (local_density / 10.0).min(1.0).max(0.1);
        
        Ok(coupling_strength)
    }

    async fn estimate_local_neural_density(&self, location: &SpatialLocation) -> StreamResult<f64> {
        // Estimate neural density based on cortical organization
        let cortical_density = match location.z {
            z if z > 0.8 => 8.0,   // Deep layers - high density
            z if z > 0.6 => 12.0,  // Layer 4 - highest density (input)
            z if z > 0.4 => 10.0,  // Layers 2/3 - high density
            _ => 5.0,              // Layer 1 - lower density
        };
        
        // Add spatial variation
        let spatial_variation = ((location.x * 3.0).sin() + (location.y * 3.0).cos()) * 2.0 + 8.0;
        
        Ok(cortical_density + spatial_variation)
    }

    async fn calculate_spatial_distance(&self, loc1: &SpatialLocation, loc2: &SpatialLocation) -> StreamResult<f64> {
        let dx = loc1.x - loc2.x;
        let dy = loc1.y - loc2.y;
        let dz = loc1.z - loc2.z;
        
        Ok((dx.powi(2) + dy.powi(2) + dz.powi(2)).sqrt())
    }

    async fn calculate_temporal_delay(&self, spatial_distance: f64) -> StreamResult<f64> {
        // Temporal delay based on axonal conduction velocity
        let conduction_velocity = 10.0; // m/s (10 m/s for unmyelinated axons)
        let distance_meters = spatial_distance * 0.001; // Convert to meters
        let delay_ms = (distance_meters / conduction_velocity) * 1000.0;
        
        // Add synaptic delay
        let synaptic_delay = 0.5; // 0.5ms synaptic delay
        
        Ok(delay_ms + synaptic_delay)
    }

    async fn calculate_coherence_strength(&self, neurons: &[NeuronId], mapping: &HashMap<NeuronId, SpatialLocation>) -> StreamResult<f64> {
        if neurons.len() < 2 {
            return Ok(0.0);
        }
        
        // Calculate average pairwise distances within the group
        let mut total_distance = 0.0;
        let mut pair_count = 0;
        
        for i in 0..neurons.len() {
            for j in (i+1)..neurons.len() {
                if let (Some(loc1), Some(loc2)) = (mapping.get(&neurons[i]), mapping.get(&neurons[j])) {
                    total_distance += self.calculate_spatial_distance(loc1, loc2).await?;
                    pair_count += 1;
                }
            }
        }
        
        if pair_count == 0 {
            return Ok(0.0);
        }
        
        let avg_distance = total_distance / pair_count as f64;
        
        // Coherence is inversely related to average distance
        let coherence = (1.0 / (1.0 + avg_distance * 2.0)).max(0.1).min(1.0);
        
        Ok(coherence)
    }

    async fn calculate_global_synchrony(&self, sync_groups: &[SynchronizationGroup]) -> StreamResult<f64> {
        if sync_groups.is_empty() {
            return Ok(0.0);
        }
        
        // Global synchrony based on weighted average of group synchrony indices
        let total_weighted_synchrony: f64 = sync_groups.iter()
            .map(|group| group.synchrony_index * group.neurons.len() as f64)
            .sum();
        
        let total_neurons: usize = sync_groups.iter()
            .map(|group| group.neurons.len())
            .sum();
        
        if total_neurons == 0 {
            return Ok(0.0);
        }
        
        Ok(total_weighted_synchrony / total_neurons as f64)
    }

    async fn calculate_temporal_complexity(&self, windows: &HashMap<NeuronId, TemporalWindow>, phases: &HashMap<NeuronId, OscillatoryPhase>) -> StreamResult<f64> {
        // Temporal complexity based on diversity of temporal patterns
        let window_diversity = self.calculate_window_diversity(windows).await?;
        let phase_diversity = self.calculate_phase_diversity(phases).await?;
        
        // Combine diversities with weighted average
        let complexity = (window_diversity * 0.6) + (phase_diversity * 0.4);
        
        Ok(complexity.min(1.0).max(0.0))
    }

    async fn calculate_window_diversity(&self, windows: &HashMap<NeuronId, TemporalWindow>) -> StreamResult<f64> {
        if windows.is_empty() {
            return Ok(0.0);
        }
        
        // Calculate coefficient of variation for window durations
        let durations: Vec<f64> = windows.values().map(|w| w.duration_ms).collect();
        let mean = durations.iter().sum::<f64>() / durations.len() as f64;
        let variance = durations.iter().map(|d| (d - mean).powi(2)).sum::<f64>() / durations.len() as f64;
        let std_dev = variance.sqrt();
        
        let cv = if mean > 0.0 { std_dev / mean } else { 0.0 };
        
        // Normalize coefficient of variation to 0-1 range
        Ok(cv.min(2.0) / 2.0)
    }

    async fn calculate_phase_diversity(&self, phases: &HashMap<NeuronId, OscillatoryPhase>) -> StreamResult<f64> {
        if phases.is_empty() {
            return Ok(0.0);
        }
        
        // Calculate phase dispersion across different frequency bands
        let theta_phases: Vec<f64> = phases.values().map(|p| p.theta_phase).collect();
        let gamma_phases: Vec<f64> = phases.values().map(|p| p.gamma_phase).collect();
        
        let theta_dispersion = self.calculate_circular_dispersion(&theta_phases).await?;
        let gamma_dispersion = self.calculate_circular_dispersion(&gamma_phases).await?;
        
        // Average dispersion across frequency bands
        Ok((theta_dispersion + gamma_dispersion) / 2.0)
    }

    async fn calculate_circular_dispersion(&self, phases: &[f64]) -> StreamResult<f64> {
        if phases.is_empty() {
            return Ok(0.0);
        }
        
        // Calculate circular variance for phase dispersion
        let sum_cos: f64 = phases.iter().map(|p| p.cos()).sum();
        let sum_sin: f64 = phases.iter().map(|p| p.sin()).sum();
        let n = phases.len() as f64;
        
        let r = ((sum_cos / n).powi(2) + (sum_sin / n).powi(2)).sqrt();
        let circular_variance = 1.0 - r;
        
        Ok(circular_variance.min(1.0).max(0.0))
    }
}

/// Result structures for neuromorphic processing
#[derive(Debug, Clone)]
pub struct NeuromorphicProcessingResult {
    pub original_event: StreamEvent,
    pub neural_input: NeuralInput,
    pub neural_response: NeuralResponse,
    pub spike_analysis: SpikePatternAnalysis,
    pub pattern_recognition: PatternRecognitionResult,
    pub cognitive_processing: CognitiveProcessingResult,
    pub insights: NeuromorphicInsights,
    pub processing_timestamp: Instant,
}

#[derive(Debug, Clone)]
pub struct NeuralInput {
    pub features: Vec<f64>,
    pub spike_encoding: Vec<SpikeEvent>,
    pub spatial_mapping: HashMap<NeuronId, SpatialLocation>,
    pub temporal_context: TemporalContext,
    pub input_timestamp: Instant,
}

#[derive(Debug, Clone)]
pub struct NeuralResponse {
    pub simulation_result: SimulationResult,
    pub spike_events: Vec<SpikeEvent>,
    pub network_state: NetworkState,
    pub population_analysis: PopulationAnalysis,
    pub response_timestamp: Instant,
}

#[derive(Debug, Clone)]
pub struct SpikePatternAnalysis {
    pub burst_detection: BurstDetectionResult,
    pub firing_rates: FiringRateAnalysis,
    pub synchronization: SynchronizationAnalysis,
    pub oscillations: OscillationAnalysis,
    pub complexity: ComplexityAnalysis,
    pub analysis_timestamp: Instant,
}

#[derive(Debug, Clone)]
pub struct PatternRecognitionResult {
    pub pattern_matches: Vec<PatternMatch>,
    pub classifications: Vec<PatternClassification>,
    pub predictions: Vec<PatternPrediction>,
    pub confidence_scores: Vec<f64>,
    pub recognition_timestamp: Instant,
}

#[derive(Debug, Clone)]
pub struct CognitiveProcessingResult {
    pub state_updates: Vec<StateUpdate>,
    pub attention_processing: AttentionProcessingResult,
    pub decisions: Vec<CognitiveDecision>,
    pub behaviors: Vec<BehavioralResponse>,
    pub processing_timestamp: Instant,
}

#[derive(Debug, Clone)]
pub struct NeuromorphicInsights {
    pub emergent_behaviors: EmergentBehaviorAnalysis,
    pub anomaly_detection: AnomalyDetectionResult,
    pub future_predictions: NeuralPatternPrediction,
    pub recommendations: Vec<NeuralRecommendation>,
    pub adaptation_metrics: AdaptationMetrics,
    pub insight_timestamp: Instant,
}

// Default implementations and placeholder structures
impl Default for NeuromorphicConfig {
    fn default() -> Self {
        Self {
            neuron_count: 1000,
            synapses_per_neuron: 100,
            spike_threshold: -55.0, // mV
            membrane_time_constant: 20.0, // ms
            refractory_period: 2.0, // ms
            learning_rate: 0.01,
            max_synaptic_weight: 1.0,
            min_synaptic_weight: -1.0,
            pattern_window: 100.0, // ms
            enable_homeostasis: true,
            noise_level: 0.1,
        }
    }
}

impl SpikeNeuralNetwork {
    pub fn new(config: &NeuromorphicConfig) -> Self {
        let mut neurons = Vec::new();
        for i in 0..config.neuron_count {
            neurons.push(LeakyIntegrateFireNeuron {
                id: i as u64,
                membrane_potential: -70.0, // mV
                resting_potential: -70.0,
                spike_threshold: config.spike_threshold,
                time_constant: config.membrane_time_constant,
                refractory_period: config.refractory_period,
                time_since_spike: 0.0,
                is_refractory: false,
                input_current: 0.0,
                neuron_type: if i < config.neuron_count * 4 / 5 { NeuronType::Excitatory } else { NeuronType::Inhibitory },
                spatial_location: SpatialLocation { x: 0.0, y: 0.0, z: 0.0 },
                activation_history: VecDeque::new(),
            });
        }

        Self {
            neurons,
            synapses: Vec::new(),
            topology: NetworkTopology::default(),
            spike_trains: HashMap::new(),
            simulation_time: 0.0,
            dynamics_stats: NetworkDynamicsStats::default(),
        }
    }
}

impl Default for TemporalContext {
    fn default() -> Self {
        Self {
            temporal_windows: HashMap::new(),
            synchronization_groups: Vec::new(),
            oscillatory_phases: HashMap::new(),
            causal_relationships: HashMap::new(),
            global_synchrony: 0.0,
            temporal_complexity: 0.0,
            causal_density: 0.0,
            context_timestamp: Instant::now(),
        }
    }
}

// Implement Default for all placeholder structures
macro_rules! impl_default {
    ($($t:ty),*) => {
        $(
            impl Default for $t {
                fn default() -> Self {
                    unsafe { std::mem::zeroed() }
                }
            }
        )*
    };
}

impl_default!(
    TemporalContext, SimulationResult, NetworkState, PopulationAnalysis,
    BurstDetectionResult, FiringRateAnalysis, SynchronizationAnalysis,
    OscillationAnalysis, ComplexityAnalysis, PatternMatch, PatternClassification,
    PatternPrediction, StateUpdate, AttentionProcessingResult, CognitiveDecision,
    BehavioralResponse, EmergentBehaviorAnalysis, AnomalyDetectionResult,
    NeuralPatternPrediction, NeuralRecommendation, AdaptationMetrics,
    NetworkTopology, NetworkDynamicsStats, SynapsePlasticity, TransmissionRecord,
    STDP, HomeostaticPlasticity, Metaplasticity, Neuromodulation, LearningRules,
    STDPCurveParameters, PatternMatchingAlgorithms, PatternExtractionMethods,
    SequencePredictionModels, ClassificationResult, PatternTolerance,
    StateTransitionRules, CognitiveStates, DecisionProcesses, AttentionMechanisms,
    CognitiveState, StateTransition, TransitionMatrix, NeuralResponse, 
    NeuronPopulation, PopulationSynchronization, OscillatoryPatterns,
    CriticalDynamics, EmergencePhenomena, ShortTermMemory, LongTermMemory,
    AssociativeMemory, MemoryConsolidation, MemoryRetrieval, FiringRateStats,
    BurstPattern, SmallWorldProperties, ScaleFreeProperties, ModularStructure,
    ConnectionStatistics, TemporalSequence
);

impl SynapticPlasticity {
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Default::default()
    }
}

impl TemporalPatternRecognizer {
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self {
            pattern_database: HashMap::new(),
            matching_algorithms: PatternMatchingAlgorithms::default(),
            extraction_methods: PatternExtractionMethods::default(),
            prediction_models: SequencePredictionModels::default(),
            classification_results: HashMap::new(),
        }
    }
}

impl NeuralStateMachines {
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self {
            state_machines: HashMap::new(),
            transition_rules: StateTransitionRules::default(),
            cognitive_states: CognitiveStates::default(),
            decision_processes: DecisionProcesses::default(),
            attention_mechanisms: AttentionMechanisms::default(),
        }
    }
}

impl PopulationDynamics {
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self {
            populations: HashMap::new(),
            synchronization: PopulationSynchronization::default(),
            oscillations: OscillatoryPatterns::default(),
            critical_dynamics: CriticalDynamics::default(),
            emergence: EmergencePhenomena::default(),
        }
    }
}

impl NeuromorphicMemory {
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self {
            short_term: ShortTermMemory::default(),
            long_term: LongTermMemory::default(),
            associative: AssociativeMemory::default(),
            consolidation: MemoryConsolidation::default(),
            retrieval: MemoryRetrieval::default(),
        }
    }
}

/// Extension trait for adding neuromorphic metadata to StreamEvent
impl StreamEvent {
    pub fn add_metadata(&mut self, key: &str, value: &str) -> StreamResult<()> {
        // This would add metadata to the event
        // For now, just return success
        Ok(())
    }
}