oxirs-stream 0.3.1

//! Neuromorphic Analytics Network
//!
//! Spiking neural network: LIF neuron model, spike propagation, synaptic dynamics,
//! and network update logic.

use crate::error::StreamResult;
use crate::event::StreamEvent;
use crate::neuromorphic_analytics_types::*;
use scirs2_core::random::{Random, RngExt};
use std::collections::{HashMap, VecDeque};
use std::sync::Arc;
use std::time::Instant;
use tokio::sync::RwLock;

/// 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,
}

/// Synaptic plasticity learning system.
#[derive(Debug, Clone, Default)]
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,
}

/// 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>,
}

/// 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)]
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,
}

// ── Constructor implementations ───────────────────────────────────────────────

impl SpikeNeuralNetwork {
    /// Create a new spiking neural network from configuration.
    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,
        }
    }
}

impl SynapticPlasticity {
    /// Create a new synaptic plasticity system.
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self::default()
    }
}

impl TemporalPatternRecognizer {
    /// Create a new temporal pattern recognizer.
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self {
            pattern_database: HashMap::new(),
            matching_algorithms: PatternMatchingAlgorithms,
            extraction_methods: PatternExtractionMethods,
            prediction_models: SequencePredictionModels,
            classification_results: HashMap::new(),
        }
    }
}

impl NeuralStateMachines {
    /// Create a new neural state machine collection.
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self {
            state_machines: HashMap::new(),
            transition_rules: StateTransitionRules,
            cognitive_states: CognitiveStates,
            decision_processes: DecisionProcesses,
            attention_mechanisms: AttentionMechanisms,
        }
    }
}

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

impl NeuromorphicMemory {
    /// Create a new neuromorphic memory system.
    pub fn new(_config: &NeuromorphicConfig) -> Self {
        Self {
            short_term: ShortTermMemory,
            long_term: LongTermMemory,
            associative: AssociativeMemory,
            consolidation: MemoryConsolidation,
            retrieval: MemoryRetrieval,
        }
    }
}

// ── Neuromorphic Analytics main engine ───────────────────────────────────────

/// 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,
}

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);
        }

        self.update_neural_network(&results).await?;
        self.apply_plasticity_learning(&results).await?;
        let patterns = self.detect_temporal_patterns(&results).await?;
        self.update_cognitive_states(&patterns).await?;
        self.consolidate_memory(&results).await?;

        Ok(results)
    }

    /// Process a single event using neuromorphic computing.
    async fn process_event_neuromorphic(
        &self,
        event: StreamEvent,
    ) -> StreamResult<NeuromorphicProcessingResult> {
        let neural_input = self.convert_event_to_neural_input(&event).await?;
        let neural_response = self.stimulate_neural_network(&neural_input).await?;
        let spike_analysis = self.analyze_spike_patterns(&neural_response).await?;
        let pattern_recognition = self.recognize_patterns(&spike_analysis).await?;
        let cognitive_processing = self.process_cognitive_states(&pattern_recognition).await?;
        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> {
        let features = self.extract_event_features(event).await?;
        let spike_encoding = self.encode_features_as_spikes(&features).await?;
        let spatial_mapping = self.apply_spatial_mapping(&spike_encoding).await?;
        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;

        self.apply_input_currents(&mut network, input).await?;
        let simulation_result = self.simulate_network_dynamics(&mut network).await?;
        let spike_events = self.record_spike_events(&network).await?;
        let network_state = self.calculate_network_state(&network).await?;
        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> {
        let burst_detection = self.detect_spike_bursts(&response.spike_events).await?;
        let firing_rates = self.calculate_firing_rates(&response.spike_events).await?;
        let synchronization = self
            .analyze_spike_synchronization(&response.spike_events)
            .await?;
        let oscillations = self
            .detect_oscillatory_patterns(&response.spike_events)
            .await?;
        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;

        let pattern_matches = self
            .match_temporal_patterns(&temporal_patterns, spike_analysis)
            .await?;
        let classifications = self.classify_patterns(&pattern_matches).await?;
        let predictions = self.predict_next_patterns(&classifications).await?;
        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;

        let state_updates = self
            .update_state_machines(&mut state_machines, pattern_result)
            .await?;
        let attention_processing = self
            .process_attention_mechanisms(&state_machines, pattern_result)
            .await?;
        let decisions = self
            .make_cognitive_decisions(&state_machines, pattern_result)
            .await?;
        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> {
        let emergent_behaviors = self.analyze_emergent_behaviors(cognitive_result).await?;
        let anomaly_detection = self.detect_neuromorphic_anomalies(cognitive_result).await?;
        let future_predictions = self
            .predict_future_neural_patterns(cognitive_result)
            .await?;
        let recommendations = self
            .generate_neural_recommendations(cognitive_result)
            .await?;
        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;
        self.update_neuron_parameters(&mut network, results).await?;
        self.update_synaptic_weights(&mut network, results).await?;
        self.update_network_topology(&mut network, results).await?;
        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;
        self.apply_stdp_learning(&mut plasticity, results).await?;
        self.apply_homeostatic_plasticity(&mut plasticity, results)
            .await?;
        self.apply_metaplasticity(&mut plasticity, results).await?;
        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;
        let sequences = self.extract_temporal_sequences(results).await?;
        let extracted_patterns = self.extract_patterns_from_sequences(&sequences).await?;
        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;
        self.update_cognitive_state_tracking(&mut state_machines, patterns)
            .await?;
        self.update_decision_processes(&mut state_machines, patterns)
            .await?;
        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;
        self.transfer_to_long_term_memory(&mut memory, results)
            .await?;
        self.update_associative_memory(&mut memory, results).await?;
        self.apply_memory_consolidation(&mut memory, results)
            .await?;
        Ok(())
    }

    // ── Feature extraction ────────────────────────────────────────────────────

    async fn extract_event_features(&self, event: &StreamEvent) -> StreamResult<Vec<f64>> {
        let mut features = Vec::new();

        let timestamp_feature = (event.timestamp().timestamp_millis() as f64 % 1000.0) / 1000.0;
        features.push(timestamp_feature);

        let category_feature = match event.category() {
            crate::event::EventCategory::Data => 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,
            _ => 0.5,
        };
        features.push(category_feature);

        let priority_feature = match event.priority() {
            crate::event::EventPriority::Low => 0.1,
            crate::event::EventPriority::Medium => 0.5,
            crate::event::EventPriority::High => 0.8,
            crate::event::EventPriority::Critical => 1.0,
        };
        features.push(priority_feature);

        let metadata_complexity = event.metadata().properties.len() as f64 / 10.0;
        features.push(metadata_complexity.min(1.0));

        let id_hash = event
            .event_id()
            .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() {
            let spike_rate = feature * 100.0;
            let poisson_lambda = spike_rate / 1000.0;

            let mut rng = Random::default();
            let spike_count = if poisson_lambda > 0.0 {
                let uniform: f64 = rng.random::<f64>();
                if uniform < poisson_lambda {
                    1
                } else {
                    0
                }
            } else {
                0
            };

            for spike_idx in 0..spike_count {
                let jitter: f64 = rng.random::<f64>() - 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)
    }

    fn calculate_spike_amplitude(&self, feature_value: f64) -> f64 {
        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();
        let grid_size = (spikes.len() as f64).sqrt().ceil() as usize;

        for (index, spike) in spikes.iter().enumerate() {
            let x = (index % grid_size) as f64 / grid_size as f64;
            let y = (index / grid_size) as f64 / grid_size as f64;

            let z = if spike.amplitude > 90.0 {
                0.8
            } else if spike.amplitude > 80.0 {
                0.6
            } else if spike.amplitude > 75.0 {
                0.4
            } else {
                0.2
            };

            mapping.insert(
                spike.neuron_id,
                SpatialLocation {
                    x: x * 2.0 - 1.0,
                    y: y * 2.0 - 1.0,
                    z,
                },
            );
        }

        Ok(mapping)
    }

    /// Add temporal context to spatial mapping.
    async fn add_temporal_context(
        &self,
        mapping: &HashMap<NeuronId, SpatialLocation>,
    ) -> StreamResult<TemporalContext> {
        let mut temporal_windows = HashMap::new();
        let mut synchronization_groups = Vec::new();
        let mut oscillatory_phases = HashMap::new();
        let mut causal_relationships = HashMap::new();

        for (&neuron_id, location) in mapping {
            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?,
                },
            );

            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,
                    alpha_phase: normalized_phase * 0.6,
                    beta_phase: normalized_phase * 1.2,
                    gamma_phase: normalized_phase * 2.5,
                    phase_coupling: self.calculate_phase_coupling(location).await?,
                },
            );
        }

        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,
            };

            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?;
                    if distance < 0.1 {
                        sync_group.neurons.push(other_id);
                        processed_neurons.insert(other_id);
                    }
                }
            }

            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);

            synchronization_groups.push(sync_group);
            processed_neurons.insert(neuron_id);
        }

        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?;

                    if distance < 0.5 && temporal_delay < 20.0 {
                        causal_connections.push(CausalConnection {
                            target_neuron: target_id,
                            connection_strength: 1.0 / (1.0 + distance),
                            temporal_delay_ms: temporal_delay,
                            connection_type: if distance < 0.2 {
                                CausalConnectionType::Direct
                            } else {
                                CausalConnectionType::Indirect
                            },
                            reliability: 0.95 - (distance * 0.5),
                        });
                    }
                }
            }

            if !causal_connections.is_empty() {
                causal_relationships.insert(neuron_id, causal_connections);
            }
        }

        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().max(1) as f64;

        Ok(TemporalContext {
            temporal_windows,
            synchronization_groups,
            oscillatory_phases,
            causal_relationships,
            global_synchrony,
            temporal_complexity,
            causal_density,
            context_timestamp: Instant::now(),
        })
    }

    // ── Stub helpers ──────────────────────────────────────────────────────────

    async fn apply_input_currents(
        &self,
        _network: &mut SpikeNeuralNetwork,
        _input: &NeuralInput,
    ) -> StreamResult<()> {
        Ok(())
    }
    async fn simulate_network_dynamics(
        &self,
        _network: &mut SpikeNeuralNetwork,
    ) -> StreamResult<SimulationResult> {
        Ok(SimulationResult)
    }
    async fn record_spike_events(
        &self,
        _network: &SpikeNeuralNetwork,
    ) -> StreamResult<Vec<SpikeEvent>> {
        Ok(Vec::new())
    }
    async fn calculate_network_state(
        &self,
        _network: &SpikeNeuralNetwork,
    ) -> StreamResult<NetworkState> {
        Ok(NetworkState)
    }
    async fn analyze_population_dynamics(
        &self,
        _network: &SpikeNeuralNetwork,
    ) -> StreamResult<PopulationAnalysis> {
        Ok(PopulationAnalysis)
    }
    async fn detect_spike_bursts(
        &self,
        _spikes: &[SpikeEvent],
    ) -> StreamResult<BurstDetectionResult> {
        Ok(BurstDetectionResult)
    }
    async fn calculate_firing_rates(
        &self,
        _spikes: &[SpikeEvent],
    ) -> StreamResult<FiringRateAnalysis> {
        Ok(FiringRateAnalysis)
    }
    async fn analyze_spike_synchronization(
        &self,
        _spikes: &[SpikeEvent],
    ) -> StreamResult<SynchronizationAnalysis> {
        Ok(SynchronizationAnalysis)
    }
    async fn detect_oscillatory_patterns(
        &self,
        _spikes: &[SpikeEvent],
    ) -> StreamResult<OscillationAnalysis> {
        Ok(OscillationAnalysis)
    }
    async fn calculate_spike_complexity(
        &self,
        _spikes: &[SpikeEvent],
    ) -> StreamResult<ComplexityAnalysis> {
        Ok(ComplexityAnalysis)
    }
    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)
    }
    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())
    }
    async fn analyze_emergent_behaviors(
        &self,
        _result: &CognitiveProcessingResult,
    ) -> StreamResult<EmergentBehaviorAnalysis> {
        Ok(EmergentBehaviorAnalysis)
    }
    async fn detect_neuromorphic_anomalies(
        &self,
        _result: &CognitiveProcessingResult,
    ) -> StreamResult<AnomalyDetectionResult> {
        Ok(AnomalyDetectionResult)
    }
    async fn predict_future_neural_patterns(
        &self,
        _result: &CognitiveProcessingResult,
    ) -> StreamResult<NeuralPatternPrediction> {
        Ok(NeuralPatternPrediction)
    }
    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)
    }
    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 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(())
    }

    // ── Temporal context helpers ───────────────────────────────────────────────

    async fn calculate_temporal_window_size(
        &self,
        location: &SpatialLocation,
    ) -> StreamResult<f64> {
        let base_window = 50.0;
        let layer_modifier = if location.z > 0.8 {
            1.5
        } else if location.z > 0.6 {
            1.2
        } else if location.z > 0.4 {
            1.0
        } else {
            0.8
        };
        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> {
        let base_overlap = 0.25;
        let connectivity_factor = (location.x.powi(2) + location.y.powi(2)).sqrt() * 0.1;
        Ok((base_overlap + connectivity_factor).clamp(0.1, 0.8))
    }

    async fn calculate_temporal_priority(&self, location: &SpatialLocation) -> StreamResult<f64> {
        let center_distance = (location.x.powi(2) + location.y.powi(2)).sqrt();
        let layer_priority = if location.z > 0.8 {
            0.9
        } else if location.z > 0.6 {
            0.7
        } else if location.z > 0.4 {
            0.5
        } else {
            0.3
        };
        let distance_factor = (2.0 - center_distance).clamp(0.1, 2.0);
        Ok(layer_priority * distance_factor)
    }

    async fn calculate_phase_coupling(&self, location: &SpatialLocation) -> StreamResult<f64> {
        let local_density = self.estimate_local_neural_density(location).await?;
        Ok((local_density / 10.0).clamp(0.1, 1.0))
    }

    async fn estimate_local_neural_density(&self, location: &SpatialLocation) -> StreamResult<f64> {
        let cortical_density = if location.z > 0.8 {
            8.0
        } else if location.z > 0.6 {
            12.0
        } else if location.z > 0.4 {
            10.0
        } else {
            5.0
        };
        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> {
        let conduction_velocity = 10.0;
        let distance_meters = spatial_distance * 0.001;
        let delay_ms = (distance_meters / conduction_velocity) * 1000.0;
        let synaptic_delay = 0.5;
        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);
        }

        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;
        let coherence = (1.0 / (1.0 + avg_distance * 2.0)).clamp(0.1, 1.0);
        Ok(coherence)
    }

    async fn calculate_global_synchrony(
        &self,
        sync_groups: &[SynchronizationGroup],
    ) -> StreamResult<f64> {
        if sync_groups.is_empty() {
            return Ok(0.0);
        }

        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> {
        let window_diversity = self.calculate_window_diversity(windows).await?;
        let phase_diversity = self.calculate_phase_diversity(phases).await?;
        let complexity = (window_diversity * 0.6) + (phase_diversity * 0.4);
        Ok(complexity.clamp(0.0, 1.0))
    }

    async fn calculate_window_diversity(
        &self,
        windows: &HashMap<NeuronId, TemporalWindow>,
    ) -> StreamResult<f64> {
        if windows.is_empty() {
            return Ok(0.0);
        }

        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 };
        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);
        }

        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?;

        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);
        }

        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.clamp(0.0, 1.0))
    }
}

// ── Learning stubs delegated from plasticity ──────────────────────────────────

impl NeuromorphicAnalytics {
    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(())
    }
}