Circuit Interface Layer - Nebuchadnezzar Integration
Circuit Interface Layer - Nebuchadnezzar Integration
Core Philosophy
“Membranes define the circuit topology; ATP consumption drives the dynamics”
The Circuit Interface Layer serves as the bridge between biophysical membrane dynamics and Nebuchadnezzar’s hierarchical probabilistic electric circuit system, operating under metacognitive orchestrator supervision. This layer translates membrane properties into dynamic circuit parameters, updates circuit topology based on membrane changes, and enables bidirectional coupling between ATP consumption and membrane dynamics using dx/dATP equations.
Architecture Overview
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┌─────────────────────────────────────────────────────────────────────────┐
│ CIRCUIT INTERFACE LAYER │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ MEMBRANE → CIRCUIT MAPPING │ │
│ │ │ │
│ │ ┌──────────────────┐ ┌──────────────────┐ ┌───────────┐ │ │
│ │ │ Lipid Bilayer │───►│ RC Circuits │───►│ Circuit │ │ │
│ │ │ Properties │ │ (Dynamic) │ │ Topology │ │ │
│ │ └──────────────────┘ └──────────────────┘ └───────────┘ │ │
│ │ │ │
│ │ ┌──────────────────┐ ┌──────────────────┐ ┌───────────┐ │ │
│ │ │ Membrane │───►│ Active Circuit │───►│ ATP │ │ │
│ │ │ Proteins │ │ Elements │ │ Coupling │ │ │
│ │ └──────────────────┘ └──────────────────┘ └───────────┘ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ ↕ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ NEBUCHADNEZZAR INTERFACE │ │
│ │ │ │
│ │ ┌──────────────────┐ ┌──────────────────┐ ┌───────────┐ │ │
│ │ │ dx/dATP │◄──►│ Circuit State │◄──►│ ATP Pool │ │ │
│ │ │ Equations │ │ Updates │ │ Dynamics │ │ │
│ │ └──────────────────┘ └──────────────────┘ └───────────┘ │ │
│ │ │ │
│ │ ┌──────────────────┐ ┌──────────────────┐ ┌───────────┐ │ │
│ │ │ Probabilistic │◄──►│ Hierarchical │◄──►│ Dynamic │ │ │
│ │ │ Circuit Priors │ │ Abstraction │ │ Expansion │ │ │
│ │ └──────────────────┘ └──────────────────┘ └───────────┘ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ ↕ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ BIDIRECTIONAL COUPLING │ │
│ │ │ │
│ │ ┌──────────────────┐ ┌──────────────────┐ ┌───────────┐ │ │
│ │ │ ATP Consumption │───►│ Membrane │───►│ Circuit │ │ │
│ │ │ → Membrane │ │ Property │ │ Parameter │ │ │
│ │ │ Changes │ │ Updates │ │ Updates │ │ │
│ │ └──────────────────┘ └──────────────────┘ └───────────┘ │ │
│ │ │ │
│ │ ┌──────────────────┐ ┌──────────────────┐ ┌───────────┐ │ │
│ │ │ Circuit State │───►│ Membrane │───►│ ATP │ │ │
│ │ │ → ATP │ │ Process │ │ Demand │ │ │
│ │ │ Demand │ │ Activation │ │ Updates │ │ │
│ │ └──────────────────┘ └──────────────────┘ └───────────┘ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
Membrane → Circuit Parameter Mapping
Core Mapping Engine
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pub struct MembraneCircuitMapper {
// Membrane state inputs
molecular_layer: MolecularLayerInterface,
mesoscale_layer: MesoscaleLayerInterface,
cellular_layer: CellularLayerInterface,
// Circuit parameter calculators
capacitance_calculator: CapacitanceCalculator,
resistance_calculator: ResistanceCalculator,
topology_generator: TopologyGenerator,
// ATP coupling
atp_membrane_coupling: AtpMembraneCoupling,
// Nebuchadnezzar interface
nebuchadnezzar_interface: NebuchadnezzarCircuitInterface,
// Mapping history for optimization
parameter_history: CircuitParameterHistory,
mapping_performance: MappingPerformanceTracker,
}
impl MembraneCircuitMapper {
pub fn map_membrane_to_circuit(&self, membrane_state: &MembraneState, atp_state: &AtpState) -> CircuitTopology {
// 1. Extract membrane electrical properties from all layers
let molecular_properties = self.molecular_layer.get_electrical_properties();
let mesoscale_properties = self.mesoscale_layer.get_domain_properties();
let cellular_properties = self.cellular_layer.get_network_properties();
// 2. Calculate circuit elements from membrane patches
let circuit_elements = self.generate_circuit_elements(
&molecular_properties,
&mesoscale_properties,
&cellular_properties,
atp_state,
);
// 3. Determine circuit topology from membrane connectivity
let topology = self.topology_generator.generate_from_membrane_connectivity(
&membrane_state.connectivity_graph,
&circuit_elements,
);
// 4. Create ATP coupling matrix
let atp_coupling = self.atp_membrane_coupling.create_coupling_matrix(
membrane_state,
atp_state,
&circuit_elements,
);
CircuitTopology {
elements: circuit_elements,
connections: topology.connections,
boundary_conditions: topology.boundary_conditions,
atp_coupling,
dynamic_update_rules: self.create_dynamic_update_rules(membrane_state),
}
}
fn generate_circuit_elements(
&self,
molecular: &MolecularElectricalProperties,
mesoscale: &MesoscaleDomainProperties,
cellular: &CellularNetworkProperties,
atp_state: &AtpState,
) -> Vec<CircuitElement> {
let mut elements = Vec::new();
// Membrane patches as RC circuits
for patch in &molecular.membrane_patches {
let capacitance = self.capacitance_calculator.calculate_patch_capacitance(
patch,
&mesoscale.local_properties[&patch.id],
atp_state,
);
let resistance = self.resistance_calculator.calculate_patch_resistance(
patch,
&mesoscale.local_properties[&patch.id],
atp_state,
);
elements.push(CircuitElement::MembraneRC {
id: patch.id.clone(),
capacitance: DynamicCapacitance::new(capacitance, patch.atp_dependencies.clone()),
resistance: DynamicResistance::new(resistance, patch.atp_dependencies.clone()),
area: patch.area,
location: patch.location.clone(),
});
}
// Proteins as active elements
for protein in &molecular.membrane_proteins {
let protein_circuit = self.convert_protein_to_circuit_element(protein, atp_state);
elements.push(protein_circuit);
}
// Membrane contact sites as coupling elements
for contact in &cellular.contact_sites {
let coupling_element = self.create_contact_coupling_element(contact, atp_state);
elements.push(coupling_element);
}
elements
}
}
Dynamic Circuit Parameter Updates
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pub struct DynamicCircuitParameters {
// Parameter evolution equations
capacitance_evolution: CapacitanceEvolution,
resistance_evolution: ResistanceEvolution,
topology_evolution: TopologyEvolution,
// ATP-dependent update rules
atp_update_rules: HashMap<CircuitElementId, AtpUpdateRule>,
// Membrane feedback mechanisms
membrane_feedback: MembraneFeedbackMechanism,
}
impl DynamicCircuitParameters {
pub fn update_circuit_for_atp_consumption(
&mut self,
circuit: &mut CircuitTopology,
atp_consumption: &AtpConsumption,
dt: f64,
) -> CircuitUpdateResult {
let mut update_summary = CircuitUpdateResult::new();
// Update each circuit element based on ATP consumption
for element in &mut circuit.elements {
match element {
CircuitElement::MembraneRC { capacitance, resistance, .. } => {
let cap_update = self.capacitance_evolution.update_for_atp(
capacitance,
&atp_consumption.spatial_distribution,
dt,
);
let res_update = self.resistance_evolution.update_for_atp(
resistance,
&atp_consumption.spatial_distribution,
dt,
);
update_summary.add_parameter_changes(cap_update, res_update);
}
CircuitElement::AtpPump { pump_parameters, .. } => {
let pump_update = pump_parameters.update_for_atp_availability(
atp_consumption.local_atp_depletion,
dt,
);
update_summary.add_pump_update(pump_update);
}
CircuitElement::AtpSynthase { synthase_parameters, .. } => {
let synthase_update = synthase_parameters.update_for_atp_production(
atp_consumption.local_atp_demand,
dt,
);
update_summary.add_synthase_update(synthase_update);
}
_ => {}
}
}
// Update circuit topology if membrane connectivity changed
if atp_consumption.topology_affecting_processes.is_empty() == false {
let topology_update = self.topology_evolution.update_for_membrane_changes(
circuit,
&atp_consumption.topology_affecting_processes,
dt,
);
update_summary.add_topology_changes(topology_update);
}
update_summary
}
pub fn predict_parameter_evolution(
&self,
current_circuit: &CircuitTopology,
atp_trajectory: &AtpTrajectory,
time_horizon: f64,
) -> PredictedCircuitEvolution {
// Predict how circuit parameters will evolve over time
let mut predicted_evolution = PredictedCircuitEvolution::new(time_horizon);
let dt = 0.001; // 1 ms time steps
let steps = (time_horizon / dt) as usize;
let mut current_state = current_circuit.clone();
for step in 0..steps {
let time = step as f64 * dt;
let atp_consumption = atp_trajectory.get_consumption_at_time(time);
let update_result = self.update_circuit_for_atp_consumption(
&mut current_state,
&atp_consumption,
dt,
);
predicted_evolution.add_time_point(time, current_state.clone(), update_result);
}
predicted_evolution
}
}
Nebuchadnezzar Interface Implementation
ATP-Based Differential Equations for Membrane Dynamics
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pub struct MembraneAtpDifferentialEquations {
// Membrane state variables
membrane_variables: MembraneStateVariables,
// ATP coupling coefficients
atp_coupling_matrix: AtpCouplingMatrix,
// Rate equations
lipid_synthesis_rates: LipidSynthesisRates,
protein_insertion_rates: ProteinInsertionRates,
membrane_remodeling_rates: MembraneRemodelingRates,
// Circuit parameter dependencies
circuit_parameter_derivatives: CircuitParameterDerivatives,
}
impl MembraneAtpDifferentialEquations {
pub fn calculate_membrane_derivatives(&self, atp_consumption_rate: f64) -> MembraneDerivatives {
let mut derivatives = MembraneDerivatives::new();
// Lipid concentration changes: dLipid/dATP
derivatives.lipid_concentrations = self.lipid_synthesis_rates.calculate_datp_rates(
atp_consumption_rate,
&self.membrane_variables.current_lipid_concentrations,
);
// Protein density changes: dProtein/dATP
derivatives.protein_densities = self.protein_insertion_rates.calculate_datp_rates(
atp_consumption_rate,
&self.membrane_variables.current_protein_densities,
);
// Membrane curvature changes: dCurvature/dATP
derivatives.membrane_curvature = self.membrane_remodeling_rates.calculate_curvature_datp(
atp_consumption_rate,
self.membrane_variables.current_curvature,
);
// Membrane potential changes: dV/dATP
derivatives.membrane_potential = self.calculate_potential_datp_dependence(
atp_consumption_rate,
&self.membrane_variables,
);
// Circuit parameter derivatives: dR/dATP, dC/dATP
derivatives.circuit_parameters = self.circuit_parameter_derivatives.calculate_datp_derivatives(
atp_consumption_rate,
&derivatives,
);
derivatives
}
fn calculate_potential_datp_dependence(
&self,
atp_consumption_rate: f64,
membrane_vars: &MembraneStateVariables,
) -> f64 {
// Membrane potential changes due to ATP-dependent ion pump activity
let pump_contribution = self.calculate_pump_potential_contribution(atp_consumption_rate);
let conductance_changes = self.calculate_conductance_potential_effects(
atp_consumption_rate,
membrane_vars,
);
pump_contribution + conductance_changes
}
pub fn integrate_with_nebuchadnezzar(
&mut self,
nebuchadnezzar_state: &NebuchadnezzarState,
) -> IntegrationResult {
// Bidirectional coupling between membrane and Nebuchadnezzar
// 1. Get ATP consumption from Nebuchadnezzar circuit simulation
let atp_consumption = nebuchadnezzar_state.get_current_atp_consumption_rate();
// 2. Calculate membrane changes due to ATP consumption
let membrane_derivatives = self.calculate_membrane_derivatives(atp_consumption);
// 3. Update membrane state
self.membrane_variables.integrate_derivatives(&membrane_derivatives);
// 4. Calculate new circuit parameters from updated membrane state
let new_circuit_params = self.calculate_updated_circuit_parameters();
// 5. Send updated circuit parameters back to Nebuchadnezzar
let circuit_update = CircuitUpdate {
new_parameters: new_circuit_params,
topology_changes: self.calculate_topology_changes(),
boundary_condition_updates: self.calculate_boundary_updates(),
};
IntegrationResult {
membrane_state_change: membrane_derivatives,
circuit_parameter_updates: circuit_update,
atp_demand_feedback: self.calculate_atp_demand_changes(),
integration_stability: self.assess_integration_stability(),
}
}
}
Hierarchical Circuit Abstraction Interface
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pub struct HierarchicalMembraneCircuitInterface {
// Abstraction levels
molecular_level_circuits: MolecularLevelCircuits,
mesoscale_level_circuits: MesoscaleLevelCircuits,
cellular_level_circuits: CellularLevelCircuits,
// Abstraction control
abstraction_controller: AbstractionController,
expansion_criteria: ExpansionCriteria,
// Probabilistic node management
probabilistic_nodes: HashMap<NodeId, ProbabilisticMembraneNode>,
detailed_expansions: HashMap<NodeId, DetailedCircuitExpansion>,
}
impl HierarchicalMembraneCircuitInterface {
pub fn create_hierarchical_representation(
&mut self,
full_membrane_circuit: &CircuitTopology,
computational_budget: ComputationalBudget,
) -> HierarchicalCircuitRepresentation {
// Start with most abstract representation
let mut hierarchical_repr = HierarchicalCircuitRepresentation::new();
// Create probabilistic nodes for membrane regions
for region in self.identify_membrane_regions(full_membrane_circuit) {
let probabilistic_node = self.create_probabilistic_membrane_node(region);
hierarchical_repr.add_node(probabilistic_node);
}
// Determine which nodes to expand based on importance and budget
let expansion_candidates = self.abstraction_controller.select_expansion_candidates(
&hierarchical_repr,
&self.expansion_criteria,
computational_budget,
);
// Expand high-priority nodes to detailed circuits
for candidate in expansion_candidates {
let detailed_circuit = self.expand_node_to_detailed_circuit(
&candidate,
full_membrane_circuit,
);
hierarchical_repr.replace_node_with_detailed_circuit(candidate.node_id, detailed_circuit);
}
hierarchical_repr
}
fn create_probabilistic_membrane_node(&self, region: &MembraneRegion) -> ProbabilisticMembraneNode {
// Create uncertain representation of membrane region
let avg_capacitance = region.calculate_average_capacitance();
let avg_resistance = region.calculate_average_resistance();
// Estimate uncertainty based on spatial variation
let capacitance_uncertainty = region.calculate_capacitance_variation();
let resistance_uncertainty = region.calculate_resistance_variation();
ProbabilisticMembraneNode {
node_id: region.id.clone(),
location: region.centroid(),
area: region.total_area(),
// Probabilistic electrical properties
capacitance_distribution: ProbabilityDistribution::normal(
avg_capacitance,
capacitance_uncertainty,
),
resistance_distribution: ProbabilityDistribution::normal(
avg_resistance,
resistance_uncertainty,
),
// ATP dependencies
atp_sensitivity: region.calculate_atp_sensitivity(),
atp_consumption_rate: region.estimate_atp_consumption(),
// Expansion criteria
importance_score: self.calculate_region_importance(region),
uncertainty_score: capacitance_uncertainty + resistance_uncertainty,
computational_cost: self.estimate_expansion_cost(region),
}
}
pub fn adaptive_expansion(
&mut self,
hierarchical_repr: &mut HierarchicalCircuitRepresentation,
optimization_results: &OptimizationResults,
) -> ExpansionResult {
// Adaptively expand nodes based on optimization sensitivity
let sensitive_nodes = optimization_results.identify_sensitive_parameters();
let mut expansion_result = ExpansionResult::new();
for node_id in sensitive_nodes {
if let Some(probabilistic_node) = self.probabilistic_nodes.get(&node_id) {
// Check if expansion would improve optimization accuracy
let expansion_benefit = self.estimate_expansion_benefit(
probabilistic_node,
&optimization_results,
);
if expansion_benefit > self.expansion_criteria.benefit_threshold {
let detailed_circuit = self.expand_node_to_detailed_circuit(
probabilistic_node,
&hierarchical_repr.get_full_circuit(),
);
hierarchical_repr.replace_node_with_detailed_circuit(
node_id,
detailed_circuit,
);
expansion_result.add_expansion(node_id, expansion_benefit);
}
}
}
expansion_result
}
}
Probabilistic Circuit Priors
Membrane-Based Circuit Priors
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pub struct MembraneCircuitPriors {
// Prior knowledge from membrane biophysics
lipid_bilayer_priors: LipidBilayerPriors,
protein_function_priors: ProteinFunctionPriors,
membrane_organization_priors: MembraneOrganizationPriors,
// Experimental parameter distributions
experimental_parameter_db: ExperimentalParameterDatabase,
// Bayesian prior update mechanisms
prior_update_engine: BayesianPriorUpdateEngine,
}
impl MembraneCircuitPriors {
pub fn generate_circuit_parameter_priors(
&self,
membrane_composition: &MembraneComposition,
temperature: f64,
atp_concentration: f64,
) -> CircuitParameterPriors {
let mut priors = CircuitParameterPriors::new();
// Capacitance priors from lipid bilayer physics
let capacitance_prior = self.lipid_bilayer_priors.generate_capacitance_prior(
membrane_composition,
temperature,
);
priors.add_capacitance_prior(capacitance_prior);
// Resistance priors from membrane permeability
let resistance_prior = self.lipid_bilayer_priors.generate_resistance_prior(
membrane_composition,
temperature,
);
priors.add_resistance_prior(resistance_prior);
// Protein circuit element priors
for protein_type in membrane_composition.get_protein_types() {
let protein_prior = self.protein_function_priors.generate_protein_circuit_prior(
protein_type,
atp_concentration,
);
priors.add_protein_prior(protein_type, protein_prior);
}
// Membrane organization priors (domains, contacts, etc.)
let organization_priors = self.membrane_organization_priors.generate_organization_priors(
membrane_composition,
);
priors.add_organization_priors(organization_priors);
priors
}
pub fn update_priors_from_experimental_data(
&mut self,
experimental_data: &ExperimentalData,
membrane_conditions: &MembraneConditions,
) -> PriorUpdateResult {
// Update Bayesian priors based on new experimental observations
let likelihood = self.calculate_experimental_likelihood(
experimental_data,
membrane_conditions,
);
let prior_updates = self.prior_update_engine.bayesian_update(
&self.current_priors(),
&likelihood,
);
// Apply updates to all prior distributions
self.lipid_bilayer_priors.update_from_bayesian_results(&prior_updates);
self.protein_function_priors.update_from_bayesian_results(&prior_updates);
self.membrane_organization_priors.update_from_bayesian_results(&prior_updates);
// Store experimental data for future reference
self.experimental_parameter_db.add_experimental_data(
experimental_data.clone(),
membrane_conditions.clone(),
);
PriorUpdateResult {
updated_parameters: prior_updates.get_updated_parameters(),
confidence_changes: prior_updates.get_confidence_changes(),
prediction_improvements: self.assess_prediction_improvements(&prior_updates),
}
}
}
#[derive(Debug, Clone)]
pub struct LipidBilayerPriors {
// Capacitance prior distributions
specific_capacitance_prior: ProbabilityDistribution<f64>, // F/m²
thickness_dependence: ThicknessDependencePrior,
temperature_dependence: TemperatureDependencePrior,
// Resistance prior distributions
ion_permeability_priors: HashMap<IonType, ProbabilityDistribution<f64>>,
lipid_composition_effects: LipidCompositionEffectPriors,
// Uncertainty quantification
measurement_uncertainties: MeasurementUncertainties,
model_uncertainties: ModelUncertainties,
}
impl LipidBilayerPriors {
pub fn generate_capacitance_prior(
&self,
composition: &MembraneComposition,
temperature: f64,
) -> CapacitancePrior {
// Start with base specific capacitance distribution
let mut capacitance_dist = self.specific_capacitance_prior.clone();
// Adjust for membrane composition
let composition_factor = self.calculate_composition_capacitance_factor(composition);
capacitance_dist = capacitance_dist.scale(composition_factor);
// Adjust for temperature
let temperature_factor = self.temperature_dependence.calculate_capacitance_factor(temperature);
capacitance_dist = capacitance_dist.scale(temperature_factor);
// Include thickness uncertainty
let thickness_uncertainty = self.thickness_dependence.estimate_thickness_uncertainty(composition);
capacitance_dist = capacitance_dist.convolve_with_uncertainty(thickness_uncertainty);
CapacitancePrior {
distribution: capacitance_dist,
temperature: temperature,
composition: composition.clone(),
confidence: self.calculate_prior_confidence(composition, temperature),
}
}
}
Example: Complete Membrane-Circuit Integration
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// Example: Complete workflow for membrane-circuit integration
pub struct MembraneCircuitIntegrationExample;
impl MembraneCircuitIntegrationExample {
pub fn run_complete_integration_example() -> Result<IntegrationResults, IntegrationError> {
// 1. Initialize membrane system
let mut membrane_system = MembraneSystem::new()
.with_lipid_composition(LipidComposition::mammalian_plasma_membrane())
.with_proteins(vec![
ProteinType::SodiumPotassiumPump,
ProteinType::VoltageGatedSodiumChannel,
ProteinType::VoltageGatedPotassiumChannel,
ProteinType::AtpSynthase,
])
.with_temperature(310.0) // 37°C
.with_membrane_area(1e-9) // 1 μm²
.build()?;
// 2. Initialize ATP pool
let mut atp_pool = AtpPool::physiological()
.with_concentration(5.0) // 5 mM ATP
.with_energy_charge(0.85)
.build();
// 3. Create circuit interface
let mut circuit_interface = CircuitInterfaceLayer::new()
.with_membrane_system(&membrane_system)
.with_atp_coupling(AtpCouplingMode::Full)
.with_hierarchy_enabled(true)
.build()?;
// 4. Generate initial circuit topology
let initial_circuit = circuit_interface.map_membrane_to_circuit(
&membrane_system.state(),
&atp_pool.state(),
);
// 5. Create Nebuchadnezzar system with membrane-derived circuit
let mut nebuchadnezzar = NebuchadnezzarSystem::new()
.with_circuit_topology(initial_circuit)
.with_atp_pool(atp_pool.clone())
.with_solver(SolverType::AtpBasedRungeKutta4)
.with_objective(OptimizationObjective::MaximizeATPEfficiency)
.build()?;
// 6. Coupled simulation loop
let mut integration_results = IntegrationResults::new();
let simulation_time = 1.0; // 1 second
let dt_atp = 0.001; // 1 ms ATP time steps
for step in 0..(simulation_time / dt_atp) as usize {
let current_time = step as f64 * dt_atp;
// Nebuchadnezzar ATP-based integration
let nebuchadnezzar_result = nebuchadnezzar.integrate_step_atp(dt_atp)?;
// Update membrane system based on ATP consumption
let membrane_update = membrane_system.update_for_atp_consumption(
&nebuchadnezzar_result.atp_consumption,
dt_atp,
)?;
// Update circuit parameters from membrane changes
let circuit_update = circuit_interface.update_circuit_from_membrane_changes(
&mut nebuchadnezzar.circuit,
&membrane_update.membrane_state_changes,
)?;
// Bidirectional feedback
let feedback_result = circuit_interface.apply_bidirectional_feedback(
&mut membrane_system,
&mut nebuchadnezzar,
)?;
// Record results
integration_results.add_time_point(IntegrationTimePoint {
time: current_time,
membrane_state: membrane_system.state().clone(),
circuit_state: nebuchadnezzar.circuit.state().clone(),
atp_state: nebuchadnezzar.atp_pool.state().clone(),
integration_metrics: feedback_result.metrics,
});
// Adaptive hierarchy adjustment
if step % 100 == 0 { // Every 100 ms
circuit_interface.adaptive_hierarchy_adjustment(
&nebuchadnezzar.optimization_state(),
)?;
}
}
// 7. Analysis and optimization
let optimization_results = nebuchadnezzar.finalize_optimization()?;
integration_results.add_optimization_results(optimization_results);
// 8. Generate final circuit parameters optimized for ATP efficiency
let optimized_circuit = circuit_interface.generate_optimized_circuit(
&integration_results.final_membrane_state(),
&optimization_results,
)?;
integration_results.set_final_optimized_circuit(optimized_circuit);
Ok(integration_results)
}
pub fn analyze_integration_results(results: &IntegrationResults) -> IntegrationAnalysis {
IntegrationAnalysis {
atp_efficiency_over_time: results.calculate_atp_efficiency_trajectory(),
membrane_parameter_evolution: results.calculate_membrane_parameter_changes(),
circuit_topology_changes: results.identify_circuit_topology_evolution(),
optimization_convergence: results.analyze_optimization_convergence(),
stability_metrics: results.calculate_stability_metrics(),
biological_realism_assessment: results.assess_biological_realism(),
}
}
}
The Circuit Interface Layer provides the essential bridge between biophysical membrane dynamics and Nebuchadnezzar’s hierarchical probabilistic electric circuits, enabling authentic biological simulation through ATP-based differential equations while maintaining computational efficiency through adaptive hierarchical abstraction.
Orchestrator Communication Protocol
Incoming Orchestrator Commands
The circuit interface receives orchestration signals that influence membrane-circuit mapping:
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class OrchestratorInterface:
def receive_context_update(self, context_layer_state):
"""Context layer provides system-wide membrane awareness"""
self.global_membrane_context = context_layer_state
self.adjust_circuit_priorities()
def receive_reasoning_directive(self, reasoning_decision):
"""Reasoning layer coordinates parameter mapping strategies"""
self.parameter_mapping_strategy = reasoning_decision
self.update_mapping_algorithms()
def receive_intuition_prediction(self, predicted_changes):
"""Intuition layer predicts membrane behavior changes"""
self.preload_circuit_adaptations(predicted_changes)
def receive_atp_allocation(self, atp_budget):
"""V8 metabolism pipeline sets ATP constraints"""
self.membrane_atp_budget = atp_budget
self.prioritize_essential_processes()
Outgoing Status Reports
The interface continuously reports membrane state to the orchestrator:
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def report_to_orchestrator(self):
return {
'membrane_voltage_state': self.current_membrane_potentials,
'atp_consumption_rate': self.calculate_atp_usage(),
'circuit_parameter_changes': self.recent_parameter_updates,
'biological_constraint_violations': self.validate_realism(),
'predicted_membrane_transitions': self.forecast_changes()
}