pub struct QuantumMembrane {
    // Hardware oscillation harvesting
    oscillation_harvester: HardwareOscillationHarvester,
    
    // Entropy as tangible oscillation endpoints
    entropy_controller: OscillatoryEntropyControl,
    
    // ATP-constrained dynamics
    atp_dynamics: ATPConstrainedDynamics,
    
    // Quantum coherence maintenance
    coherence_field: CollectiveQuantumField,
    
    // Fire-light optimization (600-700nm coupling)
    fire_wavelength_optimizer: FireWavelengthOptimizer,
}

impl QuantumMembrane {
    pub fn process_signal(&mut self, input: &NeuralSignal) -> QuantumProcessingResult {
        // Harvest real oscillations from hardware
        let hardware_oscillations = self.oscillation_harvester.harvest_current_state();
        
        // Map signal to quantum states using harvested oscillations
        let quantum_states = self.map_to_quantum_coherence(input, &hardware_oscillations);
        
        // Process through collective ion field (H+, Na+, K+, Ca2+, Mg2+)
        let processed_field = self.coherence_field.evolve_collective_state(quantum_states);
        
        // Apply fire-wavelength optimization
        let optimized_result = self.fire_wavelength_optimizer
            .enhance_coherence(processed_field);
        
        // Constrain by ATP availability
        if self.atp_dynamics.check_energy_budget(optimized_result.energy_cost) {
            QuantumProcessingResult::Success(optimized_result)
        } else {
            QuantumProcessingResult::EnergyConstrained(self.atp_dynamics.available_energy())
        }
    }
}

pub struct BiologicalCircuitProcessor {
    // ATP-based circuit dynamics
    circuit_topology: HierarchicalProbabilisticCircuit,
    
    // Membrane dynamics with quantum effects
    hodgkin_huxley: QuantumEnhancedHodgkinHuxley,
    
    // Oscillatory frequency bands
    oscillation_bands: MultiFrequencyOscillator,
    
    // Synaptic plasticity engine
    plasticity_engine: AdvancedPlasticityEngine,
}

impl BiologicalCircuitProcessor {
    pub fn transduce_signal(&mut self, quantum_input: QuantumProcessingResult) 
        -> BiologicalSignal {
        
        // Convert quantum coherence to ionic currents
        let ionic_currents = self.quantum_to_ionic_conversion(quantum_input);
        
        // Process through enhanced Hodgkin-Huxley dynamics
        let membrane_response = self.hodgkin_huxley.update_with_quantum_effects(
            ionic_currents,
            self.oscillation_bands.current_phase_state()
        );
        
        // Apply circuit topology constraints
        let circuit_processed = self.circuit_topology
            .process_hierarchical_signal(membrane_response);
        
        // Update synaptic weights based on timing
        self.plasticity_engine.update_weights(circuit_processed);
        
        BiologicalSignal::new(circuit_processed)
    }
}

pub struct SpecializedProcessingLayer {
    // Probabilistic reasoning delegation to Autobahn
    autobahn_connector: AutobahnProbabilisticReasoning,
    
    // Audio and fire-based emotional processing
    heihachi_processor: HeihachiAudioFireProcessor,
    
    // Visual understanding through reconstruction
    helicopter_vision: HelicopterVisualProcessor,
    
    // Metacognitive orchestration
    izinyoka_orchestrator: IzinyokaMetacognitive,
    
    // Semantic processing networks
    kwasa_kwasa_semantic: KwasaKwasaSemanticProcessor,
    
    // Thought validation and optimization
    four_sided_triangle: FourSidedTriangleValidator,
}

pub enum NeuralInput {
    Visual(VisualData),
    Auditory(AudioData),
    Temporal(TemporalPattern),
    Fire(FirePattern),
    Semantic(SemanticContent),
}

impl NeuralUnit {
    pub fn process_input(&mut self, input: NeuralInput) -> NeuralOutput {
        match input {
            NeuralInput::Visual(data) => {
                // Delegate to Helicopter for reconstruction-based understanding
                let visual_understanding = self.specialized_layer
                    .helicopter_vision.autonomous_reconstruction(data);
                
                // Convert to quantum membrane states
                self.quantum_membrane.encode_visual_pattern(visual_understanding)
            },
            
            NeuralInput::Auditory(audio) => {
                // Process through Heihachi fire-emotion mapping
                let emotional_pattern = self.specialized_layer
                    .heihachi_processor.extract_fire_emotion_mapping(audio);
                
                // Convert to oscillatory patterns
                self.biological_processor.encode_emotional_oscillations(emotional_pattern)
            },
            
            NeuralInput::Semantic(content) => {
                // Process through Kwasa-Kwasa semantic understanding
                let semantic_understanding = self.specialized_layer
                    .kwasa_kwasa_semantic.develop_scientific_understanding(content);
                
                // Validate through Four Sided Triangle
                let validated_thought = self.specialized_layer
                    .four_sided_triangle.optimize_thought_structure(semantic_understanding);
                
                self.quantum_membrane.encode_validated_thought(validated_thought)
            },
            
            // Additional input types...
        }
    }
}

impl QuantumCoherenceProcessor {
    pub fn process_coherent_field(&mut self, input: EncodedInput) -> CoherentField {
        // Generate collective quantum field from ion channels
        let ion_field = self.generate_collective_ion_field(input);
        
        // Apply environment-assisted quantum transport (ENAQT)
        let enhanced_field = self.apply_enaqt_enhancement(ion_field);
        
        // Optimize for fire-wavelength coupling (600-700nm)
        let fire_optimized = self.optimize_fire_wavelength_coupling(enhanced_field);
        
        // Maintain coherence through hardware oscillation coupling
        let hardware_coupled = self.couple_to_hardware_oscillations(fire_optimized);
        
        CoherentField::new(hardware_coupled)
    }
    
    fn generate_collective_ion_field(&self, input: EncodedInput) -> CollectiveIonField {
        // H+ ion quantum tunneling for consciousness substrate
        let proton_field = self.simulate_proton_tunneling(input.proton_activation);
        
        // Metal ion coordination (Na+, K+, Ca2+, Mg2+)
        let metal_ion_field = self.coordinate_metal_ions(input.metal_ion_states);
        
        // Combine into collective quantum field
        CollectiveIonField::combine(proton_field, metal_ion_field)
    }
}

impl BiologicalSignalConverter {
    pub fn convert_quantum_to_biological(&mut self, 
        quantum_field: CoherentField) -> BiologicalResponse {
        
        // Convert quantum coherence to membrane potential changes
        let membrane_potential = self.quantum_to_potential_conversion(quantum_field);
        
        // Apply Hodgkin-Huxley dynamics with quantum corrections
        let ionic_currents = self.calculate_quantum_corrected_currents(membrane_potential);
        
        // Generate action potential if threshold exceeded
        let action_potential = self.generate_action_potential_if_threshold(ionic_currents);
        
        // Encode in oscillatory patterns across frequency bands
        let oscillatory_encoding = self.encode_in_oscillations(action_potential);
        
        BiologicalResponse::new(oscillatory_encoding)
    }
}

pub struct NeuralNetworkTopology {
    // Specialized neural unit types
    visual_processing_units: Vec<VisualNeuralUnit>,
    auditory_processing_units: Vec<AuditoryNeuralUnit>,
    semantic_processing_units: Vec<SemanticNeuralUnit>,
    fire_processing_units: Vec<FireNeuralUnit>,
    metacognitive_units: Vec<MetacognitiveNeuralUnit>,
    
    // Network connectivity patterns
    small_world_connections: SmallWorldConnectivity,
    scale_free_hubs: ScaleFreeTopology,
    modular_structure: ModularNetworkTopology,
    
    // Cross-modal integration pathways
    visual_auditory_bridges: CrossModalConnectivity,
    semantic_emotional_bridges: SemanticEmotionalConnectivity,
    consciousness_emergence_layer: ConsciousnessEmergenceNetwork,
}

impl NeuralNetworkTopology {
    pub fn propagate_signal(&mut self, signal: NeuralSignal, source: NeuronID) 
        -> PropagationResult {
        
        // Determine signal type and routing
        let routing_strategy = self.determine_routing_strategy(&signal);
        
        match routing_strategy {
            RoutingStrategy::Visual => {
                self.route_through_visual_processing(signal, source)
            },
            RoutingStrategy::Auditory => {
                self.route_through_auditory_processing(signal, source)
            },
            RoutingStrategy::CrossModal => {
                self.route_through_cross_modal_integration(signal, source)
            },
            RoutingStrategy::Consciousness => {
                self.route_through_consciousness_emergence(signal, source)
            },
        }
    }
}

pub struct QuantumSynapse {
    // Quantum tunneling probability for neurotransmitter release
    tunneling_probability: f64,
    
    // ATP-constrained vesicle availability
    vesicle_pool: ATPConstrainedVesiclePool,
    
    // Oscillation-dependent release timing
    oscillation_dependent_timing: OscillationTiming,
    
    // Plasticity state
    plasticity_state: SynapticPlasticityState,
}

impl QuantumSynapse {
    pub fn transmit_signal(&mut self, presynaptic_signal: ActionPotential) 
        -> SynapticTransmission {
        
        // Calculate quantum tunneling probability for Ca2+ channels
        let calcium_tunneling = self.calculate_calcium_tunneling_probability(
            presynaptic_signal.amplitude
        );
        
        // Determine neurotransmitter release based on quantum probability
        let release_probability = self.quantum_enhanced_release_probability(
            calcium_tunneling,
            self.oscillation_dependent_timing.current_phase()
        );
        
        // Check ATP availability for vesicle fusion
        if self.vesicle_pool.check_atp_availability() {
            let neurotransmitter_amount = self.calculate_release_amount(release_probability);
            
            // Update synaptic plasticity based on timing
            self.plasticity_state.update_based_on_timing(presynaptic_signal.timing);
            
            SynapticTransmission::Success(neurotransmitter_amount)
        } else {
            SynapticTransmission::ATPConstrained
        }
    }
}

pub struct QuantumSTDP {
    // Traditional STDP parameters
    ltp_amplitude: f64,
    ltd_amplitude: f64,
    ltp_time_constant: f64,
    ltd_time_constant: f64,
    
    // Quantum enhancement factors
    quantum_coherence_factor: f64,
    collective_field_influence: f64,
    oscillation_phase_coupling: f64,
}

impl QuantumSTDP {
    pub fn update_synaptic_weight(&mut self, 
        pre_spike_time: f64, 
        post_spike_time: f64,
        quantum_context: QuantumCoherenceContext) -> WeightUpdate {
        
        let time_diff = post_spike_time - pre_spike_time;
        
        // Calculate traditional STDP component
        let traditional_update = if time_diff > 0.0 {
            self.ltp_amplitude * (-time_diff / self.ltp_time_constant).exp()
        } else {
            -self.ltd_amplitude * (time_diff / self.ltd_time_constant).exp()
        };
        
        // Apply quantum enhancement
        let quantum_enhancement = self.calculate_quantum_enhancement(
            quantum_context.coherence_strength,
            quantum_context.collective_field_phase,
            quantum_context.oscillation_alignment
        );
        
        // Combine traditional and quantum components
        let total_update = traditional_update * (1.0 + quantum_enhancement);
        
        WeightUpdate::new(total_update)
    }
}

pub struct MetabolicHomeostasis {
    // Target firing rate for homeostasis
    target_firing_rate: f64,
    
    // ATP-based scaling factor
    atp_scaling_factor: f64,
    
    // Oscillation-dependent modulation
    oscillation_modulation: OscillationModulation,
    
    // Time constants for adaptation
    fast_adaptation_tau: f64,
    slow_adaptation_tau: f64,
}

impl MetabolicHomeostasis {
    pub fn adjust_neural_excitability(&mut self, 
        current_firing_rate: f64,
        atp_level: f64,
        oscillation_state: OscillationState) -> ExcitabilityAdjustment {
        
        // Calculate homeostatic pressure
        let homeostatic_pressure = (self.target_firing_rate - current_firing_rate) 
            / self.target_firing_rate;
        
        // Modulate by ATP availability
        let atp_modulated_pressure = homeostatic_pressure * 
            self.calculate_atp_scaling(atp_level);
        
        // Apply oscillation-dependent modulation
        let oscillation_modulated = self.oscillation_modulation
            .modulate_homeostatic_pressure(atp_modulated_pressure, oscillation_state);
        
        // Calculate final excitability adjustment
        ExcitabilityAdjustment::new(oscillation_modulated)
    }
}

pub struct FireEmotionalNeuralUnit {
    // Core neural unit functionality
    core_unit: QuantumEnhancedNeuralUnit,
    
    // Heihachi fire-emotion mapping
    fire_emotion_mapper: HeihachiFire EmotionMapper,
    
    // Fire-wavelength optimization (600-700nm)
    fire_wavelength_resonance: FireWavelengthResonance,
    
    // Emotional state encoding
    emotional_state_encoder: EmotionalStateEncoder,
}

impl FireEmotionalNeuralUnit {
    pub fn process_fire_input(&mut self, fire_pattern: FirePattern) -> EmotionalResponse {
        // Map fire pattern to emotional content using Heihachi
        let emotional_content = self.fire_emotion_mapper
            .extract_emotional_content(fire_pattern);
        
        // Optimize quantum coherence for fire wavelengths
        let wavelength_optimized = self.fire_wavelength_resonance
            .optimize_for_fire_spectrum(emotional_content);
        
        // Encode in neural oscillations
        let oscillatory_encoding = self.emotional_state_encoder
            .encode_emotional_oscillations(wavelength_optimized);
        
        // Process through core neural computation
        let neural_response = self.core_unit.process_encoded_input(oscillatory_encoding);
        
        EmotionalResponse::new(neural_response)
    }
}

pub struct VisualReconstructionNeuralUnit {
    // Core neural unit
    core_unit: QuantumEnhancedNeuralUnit,
    
    // Helicopter visual understanding
    helicopter_processor: HelicopterVisualProcessor,
    
    // Reconstruction-based understanding validation
    understanding_validator: ReconstructionValidator,
    
    // Visual feature encoding
    visual_feature_encoder: VisualFeatureEncoder,
}

impl VisualReconstructionNeuralUnit {
    pub fn process_visual_input(&mut self, visual_data: VisualData) 
        -> VisualUnderstanding {
        
        // Validate understanding through reconstruction
        let reconstruction_fidelity = self.helicopter_processor
            .autonomous_reconstruction(visual_data.clone());
        
        // Only proceed if reconstruction meets fidelity threshold
        if self.understanding_validator.validate_understanding(reconstruction_fidelity) {
            // Encode visual features in neural patterns
            let neural_encoding = self.visual_feature_encoder
                .encode_visual_patterns(visual_data);
            
            // Process through quantum-enhanced neural computation
            let processed_understanding = self.core_unit
                .process_encoded_input(neural_encoding);
            
            VisualUnderstanding::Validated(processed_understanding)
        } else {
            VisualUnderstanding::InsufficientFidelity(reconstruction_fidelity)
        }
    }
}

pub struct SemanticProcessingNetwork {
    // Network of semantic processing units
    semantic_units: Vec<SemanticNeuralUnit>,
    
    // Kwasa-Kwasa semantic understanding engine
    kwasa_kwasa_engine: KwasaKwasaSemanticEngine,
    
    // Four Sided Triangle thought validation
    thought_validator: FourSidedTriangleValidator,
    
    // Semantic network topology
    semantic_topology: SemanticNetworkTopology,
}

impl SemanticProcessingNetwork {
    pub fn process_semantic_content(&mut self, content: SemanticContent) 
        -> ValidatedUnderstanding {
        
        // Develop scientific understanding using Kwasa-Kwasa
        let scientific_understanding = self.kwasa_kwasa_engine
            .develop_scientific_understanding(content);
        
        // Validate thought structure using Four Sided Triangle
        let validated_thought = self.thought_validator
            .validate_and_optimize_thought(scientific_understanding);
        
        // Distribute across semantic processing network
        let network_processed = self.distribute_across_network(validated_thought);
        
        // Integrate responses from semantic units
        let integrated_understanding = self.integrate_semantic_responses(network_processed);
        
        ValidatedUnderstanding::new(integrated_understanding)
    }
}

pub struct ConsciousnessEmergenceLayer {
    // Cross-modal integration units
    cross_modal_integrators: Vec<CrossModalIntegrator>,
    
    // Temporal binding mechanisms
    temporal_binding: TemporalBindingMechanism,
    
    // Global workspace architecture
    global_workspace: GlobalWorkspace,
    
    // Quantum coherence orchestrator
    quantum_orchestrator: QuantumCoherenceOrchestrator,
    
    // Izinyoka metacognitive orchestration
    metacognitive_orchestrator: IzinyokaMetacognitive,
}

impl ConsciousnessEmergenceLayer {
    pub fn integrate_conscious_experience(&mut self, 
        multi_modal_inputs: MultiModalInputs) -> ConsciousExperience {
        
        // Integrate across sensory modalities
        let cross_modal_integration = self.integrate_cross_modal_inputs(multi_modal_inputs);
        
        // Bind temporal sequences into coherent experience
        let temporally_bound = self.temporal_binding
            .bind_temporal_sequences(cross_modal_integration);
        
        // Process through global workspace for conscious access
        let globally_accessible = self.global_workspace
            .make_globally_accessible(temporally_bound);
        
        // Orchestrate quantum coherence for consciousness substrate
        let quantum_orchestrated = self.quantum_orchestrator
            .orchestrate_collective_coherence(globally_accessible);
        
        // Apply metacognitive orchestration using Izinyoka
        let metacognitively_orchestrated = self.metacognitive_orchestrator
            .apply_metacognitive_control(quantum_orchestrated);
        
        ConsciousExperience::new(metacognitively_orchestrated)
    }
}

pub struct QuantumCoherenceOrchestrator {
    // Collective ion field coordinator
    ion_field_coordinator: CollectiveIonFieldCoordinator,
    
    // Hardware oscillation synchronizer
    hardware_sync: HardwareOscillationSynchronizer,
    
    // Fire-wavelength coherence enhancer
    fire_coherence_enhancer: FireCoherenceEnhancer,
    
    // ENAQT optimization engine
    enaqt_optimizer: ENAQTOptimizer,
}

impl QuantumCoherenceOrchestrator {
    pub fn orchestrate_collective_coherence(&mut self, 
        conscious_content: GloballyAccessibleContent) -> CoherentConsciousState {
        
        // Coordinate collective ion fields across all neural units
        let coordinated_fields = self.ion_field_coordinator
            .coordinate_collective_fields(conscious_content);
        
        // Synchronize with hardware oscillations for stability
        let hardware_synchronized = self.hardware_sync
            .synchronize_quantum_fields(coordinated_fields);
        
        // Enhance coherence using fire-wavelength optimization
        let fire_enhanced = self.fire_coherence_enhancer
            .enhance_coherence_with_fire_coupling(hardware_synchronized);
        
        // Optimize using environment-assisted quantum transport
        let enaqt_optimized = self.enaqt_optimizer
            .optimize_environmental_coupling(fire_enhanced);
        
        CoherentConsciousState::new(enaqt_optimized)
    }
}