defmodule Dspy.NeuromorphicReasoningEngine do
  @moduledoc """
  Advanced neuromorphic reasoning engine that integrates brain-inspired computing 
  principles with DSPy's reasoning capabilities, leveraging Erlang's profiling and
  concurrent processing for energy-efficient AI reasoning.
  
  Key Features:
  - Spiking Neural Networks for event-driven reasoning
  - Energy-efficient computation using Erlang profiling optimization
  - Asynchronous neuromorphic processing with fault tolerance
  - Integration with quantum superposition and consciousness emergence
  - Real-time adaptive streaming with neuromorphic patterns
  - Advanced memory management using biological forgetting mechanisms
  """

  use GenServer
  require Logger

  defstruct [
    :neuron_pools,
    :spike_buffer,
    :synaptic_weights, 
    :adaptation_rules,
    :energy_monitor,
    :profiling_state,
    :streaming_context,
    :quantum_bridge,
    :consciousness_detector,
    :memory_consolidator
  ]

  @type spike :: %{
    neuron_id: String.t(),
    timestamp: integer(),
    intensity: float(),
    concept: String.t(),
    reasoning_type: atom()
  }

  @type neuron_pool :: %{
    id: String.t(),
    neurons: [String.t()],
    activation_threshold: float(),
    refractory_period: integer(),
    synaptic_plasticity: float(),
    energy_consumption: float()
  }

  # ================================
  # Neuromorphic Reasoning API
  # ================================

  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end

  @doc """
  Process reasoning through neuromorphic spiking networks
  """
  def process_neuromorphic_reasoning(problem, opts \\ []) do
    GenServer.call(__MODULE__, {:neuromorphic_reasoning, problem, opts}, 30_000)
  end

  @doc """
  Start adaptive neuromorphic streaming for real-time reasoning
  """
  def start_neuromorphic_streaming(signature, opts \\ []) do
    GenServer.cast(__MODULE__, {:start_streaming, signature, opts})
  end

  @doc """
  Monitor energy efficiency using Erlang profiling
  """
  def monitor_energy_efficiency do
    GenServer.call(__MODULE__, :monitor_energy)
  end

  @doc """
  Get neuromorphic performance metrics
  """
  def get_neuromorphic_metrics do
    GenServer.call(__MODULE__, :get_metrics)
  end

  # ================================
  # GenServer Implementation
  # ================================

  @impl true
  def init(opts) do
    # Start Erlang profiling for energy monitoring (if available)
    safe_start_profiling()
    
    state = %__MODULE__{
      neuron_pools: initialize_neuron_pools(opts),
      spike_buffer: %{},
      synaptic_weights: initialize_synaptic_weights(),
      adaptation_rules: initialize_adaptation_rules(opts),
      energy_monitor: initialize_energy_monitor(),
      profiling_state: initialize_profiling_state(),
      streaming_context: nil,
      quantum_bridge: initialize_quantum_bridge(),
      consciousness_detector: initialize_consciousness_detector(),
      memory_consolidator: initialize_memory_consolidator()
    }

    # Schedule energy optimization cycles
    schedule_energy_optimization()
    schedule_memory_consolidation()
    
    Logger.info("Neuromorphic Reasoning Engine initialized with #{map_size(state.neuron_pools)} neuron pools")
    
    {:ok, state}
  end

  @impl true
  def handle_call({:neuromorphic_reasoning, problem, opts}, _from, state) do
    # Start profiling this specific reasoning task
    profiling_ref = start_reasoning_profiling()
    
    try do
      # Convert problem to spike patterns
      initial_spikes = problem_to_spike_patterns(problem, state.neuron_pools)
      
      # Process through neuromorphic network
      reasoning_result = execute_neuromorphic_processing(initial_spikes, state, opts)
      
      # Apply consciousness emergence detection if enabled
      conscious_result = apply_consciousness_emergence(reasoning_result, state.consciousness_detector)
      
      # Integrate with quantum superposition if available
      final_result = integrate_quantum_superposition(conscious_result, state.quantum_bridge)
      
      # Update synaptic weights based on success
      updated_state = update_synaptic_plasticity(state, final_result.success_metrics)
      
      {:reply, {:ok, final_result}, updated_state}
    catch
      error ->
        Logger.error("Neuromorphic reasoning failed: #{inspect(error)}")
        {:reply, {:error, error}, state}
    after
      stop_reasoning_profiling(profiling_ref)
    end
  end

  @impl true
  def handle_call(:monitor_energy, _from, state) do
    energy_metrics = collect_energy_metrics(state)
    {:reply, energy_metrics, state}
  end

  @impl true
  def handle_call(:get_metrics, _from, state) do
    metrics = compile_neuromorphic_metrics(state)
    {:reply, metrics, state}
  end

  @impl true
  def handle_cast({:start_streaming, signature, opts}, state) do
    streaming_context = initialize_streaming_context(signature, opts)
    
    # Create neuromorphic streaming callback
    callback = create_neuromorphic_streaming_callback(streaming_context)
    
    # Start adaptive streaming with real-time spike processing
    spawn_link(fn -> 
      execute_adaptive_streaming(streaming_context, callback, state)
    end)
    
    {:noreply, %{state | streaming_context: streaming_context}}
  end

  @impl true
  def handle_info(:optimize_energy, state) do
    optimized_state = perform_energy_optimization(state)
    schedule_energy_optimization()
    {:noreply, optimized_state}
  end

  @impl true
  def handle_info(:consolidate_memory, state) do
    consolidated_state = perform_memory_consolidation(state)
    schedule_memory_consolidation()
    {:noreply, consolidated_state}
  end

  @impl true
  def handle_info({:spike_event, spike}, state) do
    updated_state = process_incoming_spike(spike, state)
    {:noreply, updated_state}
  end

  # ================================
  # Core Neuromorphic Processing
  # ================================

  defp execute_neuromorphic_processing(initial_spikes, state, _opts) do
    # Create neuromorphic processing pipeline
    processing_steps = [
      &spike_encoding/2,
      &temporal_integration/2,
      &lateral_inhibition/2,
      &competitive_dynamics/2,
      &homeostatic_regulation/2,
      &spike_timing_dependent_plasticity/2,
      &neuromorphic_consensus/2,
      &spike_decoding/2
    ]
    
    # Execute pipeline with fault tolerance
    result = Enum.reduce(processing_steps, initial_spikes, fn step, acc_spikes ->
      try do
        step.(acc_spikes, state)
      rescue
        error ->
          Logger.warning("Neuromorphic step failed, using fallback: #{inspect(error)}")
          neuromorphic_fallback(acc_spikes, state)
      end
    end)
    
    %{
      reasoning_output: result.final_reasoning,
      spike_trace: result.spike_history,
      energy_consumption: calculate_energy_consumption(result),
      processing_latency: result.total_latency,
      success_metrics: result.quality_metrics,
      neuromorphic_features: result.neuromorphic_analysis
    }
  end

  defp spike_encoding(reasoning_input, state) do
    # Convert reasoning concepts to spike trains
    concepts = extract_reasoning_concepts(reasoning_input)
    
    spike_trains = concepts
    |> Enum.map(fn concept ->
      intensity = calculate_concept_intensity(concept)
      frequency = concept_to_frequency(concept, intensity)
      
      generate_spike_train(concept, frequency, intensity, state.neuron_pools)
    end)
    |> Enum.flat_map(& &1)
    
    %{
      spikes: spike_trains,
      encoding_latency: measure_encoding_latency(),
      concept_mapping: create_concept_mapping(concepts)
    }
  end

  defp temporal_integration(spike_data, state) do
    # Integrate spikes over time windows for temporal reasoning
    time_windows = partition_spikes_by_time(spike_data.spikes)
    
    integrated_patterns = time_windows
    |> Enum.map(fn {window_start, window_spikes} ->
      pattern = detect_temporal_patterns(window_spikes)
      integration_strength = calculate_integration_strength(pattern, state.synaptic_weights)
      
      %{
        window: window_start,
        pattern: pattern,
        strength: integration_strength,
        reasoning_contribution: pattern_to_reasoning(pattern)
      }
    end)
    
    %{
      integrated_patterns: integrated_patterns,
      temporal_coherence: calculate_temporal_coherence(integrated_patterns),
      reasoning_evolution: track_reasoning_evolution(integrated_patterns)
    }
  end

  defp lateral_inhibition(integration_data, state) do
    # Apply competitive inhibition between conflicting reasoning paths
    patterns = integration_data.integrated_patterns
    
    # Find competing patterns
    competing_groups = group_competing_patterns(patterns)
    
    # Apply inhibition based on strength and relevance
    inhibited_patterns = competing_groups
    |> Enum.map(fn group ->
      winner = select_pattern_winner(group, state.neuron_pools)
      suppressed = apply_inhibition_to_losers(group, winner)
      
      [winner | suppressed]
    end)
    |> List.flatten()
    
    %{
      final_patterns: inhibited_patterns,
      competition_results: analyze_competition_results(competing_groups),
      inhibition_strength: calculate_global_inhibition(inhibited_patterns)
    }
  end

  defp competitive_dynamics(inhibition_data, state) do
    # Implement competitive dynamics for reasoning selection
    patterns = inhibition_data.final_patterns
    
    # Create competition between reasoning strategies
    competitions = create_reasoning_competitions(patterns)
    
    results = competitions
    |> Enum.map(fn competition ->
      winner = run_competitive_selection(competition, state.adaptation_rules)
      
      %{
        competition: competition,
        winner: winner,
        selection_metrics: calculate_selection_metrics(competition, winner)
      }
    end)
    
    %{
      competition_results: results,
      selected_reasoning: extract_winning_reasoning(results),
      competitive_fitness: calculate_competitive_fitness(results)
    }
  end

  defp homeostatic_regulation(competition_data, state) do
    # Apply homeostatic regulation to maintain system stability
    current_activity = measure_system_activity(competition_data)
    target_activity = state.adaptation_rules.target_activity_level
    
    regulation_needed = current_activity - target_activity
    
    regulated_reasoning = if abs(regulation_needed) > 0.1 do
      apply_homeostatic_adjustment(competition_data.selected_reasoning, regulation_needed)
    else
      competition_data.selected_reasoning
    end
    
    %{
      regulated_reasoning: regulated_reasoning,
      activity_level: current_activity,
      regulation_applied: regulation_needed,
      stability_metrics: calculate_stability_metrics(current_activity, target_activity)
    }
  end

  defp spike_timing_dependent_plasticity(regulation_data, state) do
    # Apply STDP for learning and adaptation
    reasoning_quality = assess_reasoning_quality(regulation_data.regulated_reasoning)
    
    # Update synaptic weights based on timing and success
    weight_updates = calculate_stdp_updates(
      regulation_data.regulated_reasoning,
      reasoning_quality,
      state.synaptic_weights
    )
    
    updated_weights = apply_weight_updates(state.synaptic_weights, weight_updates)
    
    %{
      final_reasoning: regulation_data.regulated_reasoning,
      weight_updates: weight_updates,
      updated_weights: updated_weights,
      learning_rate: calculate_adaptive_learning_rate(reasoning_quality),
      plasticity_metrics: calculate_plasticity_metrics(weight_updates)
    }
  end

  defp neuromorphic_consensus(plasticity_data, state) do
    # Generate consensus across neuromorphic processing
    reasoning_candidates = extract_reasoning_candidates(plasticity_data.final_reasoning)
    
    # Weight candidates by neuromorphic criteria
    weighted_candidates = reasoning_candidates
    |> Enum.map(fn candidate ->
      neuromorphic_score = calculate_neuromorphic_score(candidate, state)
      
      %{
        candidate: candidate,
        neuromorphic_score: neuromorphic_score,
        biological_plausibility: assess_biological_plausibility(candidate),
        energy_efficiency: estimate_energy_efficiency(candidate)
      }
    end)
    |> Enum.sort_by(& &1.neuromorphic_score, :desc)
    
    consensus_reasoning = combine_top_candidates(weighted_candidates)
    
    %{
      consensus_reasoning: consensus_reasoning,
      candidate_analysis: weighted_candidates,
      consensus_confidence: calculate_consensus_confidence(weighted_candidates),
      neuromorphic_characteristics: extract_neuromorphic_features(consensus_reasoning)
    }
  end

  defp spike_decoding(consensus_data, state) do
    # Decode spike patterns back to reasoning output
    spikes_to_decode = consensus_data.consensus_reasoning
    
    decoded_reasoning = decode_spike_patterns_to_reasoning(spikes_to_decode, state.neuron_pools)
    
    %{
      final_reasoning: decoded_reasoning,
      spike_history: collect_processing_history(consensus_data),
      total_latency: calculate_total_processing_latency(),
      quality_metrics: calculate_final_quality_metrics(decoded_reasoning, consensus_data),
      neuromorphic_analysis: compile_neuromorphic_analysis(consensus_data)
    }
  end

  # ================================
  # Energy Optimization with Erlang Profiling
  # ================================

  defp perform_energy_optimization(state) do
    # Use Erlang profiling to identify energy inefficiencies
    profiling_data = safe_analyze_profiling()
    
    # Analyze function call patterns for optimization
    optimization_targets = identify_optimization_targets(profiling_data)
    
    # Apply neuromorphic optimizations
    optimized_neuron_pools = optimize_neuron_pools(state.neuron_pools, optimization_targets)
    optimized_weights = optimize_synaptic_weights(state.synaptic_weights, optimization_targets)
    
    # Update energy monitoring
    updated_energy_monitor = update_energy_monitor(state.energy_monitor, optimization_targets)
    
    Logger.info("Energy optimization completed: #{length(optimization_targets)} targets optimized")
    
    %{state |
      neuron_pools: optimized_neuron_pools,
      synaptic_weights: optimized_weights,
      energy_monitor: updated_energy_monitor
    }
  end

  defp identify_optimization_targets(profiling_data) do
    # Analyze profiling data to find energy-intensive operations
    profiling_data
    |> Enum.filter(fn {_function, metrics} -> 
      metrics.percentage > 5.0 or metrics.calls > 1000
    end)
    |> Enum.map(fn {function, metrics} ->
      optimization_type = determine_optimization_type(function, metrics)
      
      %{
        function: function,
        metrics: metrics,
        optimization_type: optimization_type,
        potential_savings: estimate_energy_savings(metrics, optimization_type)
      }
    end)
    |> Enum.sort_by(& &1.potential_savings, :desc)
  end

  defp optimize_neuron_pools(neuron_pools, targets) do
    targets
    |> Enum.reduce(neuron_pools, fn target, acc_pools ->
      case target.optimization_type do
        :reduce_threshold ->
          adjust_activation_thresholds(acc_pools, target)
        :increase_refractory ->
          adjust_refractory_periods(acc_pools, target)
        :sparse_connections ->
          implement_sparse_connectivity(acc_pools, target)
        :adaptive_pooling ->
          implement_adaptive_pooling(acc_pools, target)
        _ ->
          acc_pools
      end
    end)
  end

  # ================================
  # Real-time Adaptive Streaming
  # ================================

  defp execute_adaptive_streaming(streaming_context, callback, state) do
    # Create neuromorphic streaming pipeline
    stream_pipeline = [
      &neuromorphic_preprocessing/3,
      &spike_generation_streaming/3,
      &real_time_adaptation/3,
      &streaming_consciousness_check/3,
      &neuromorphic_response_generation/3
    ]
    
    # Process streaming data through neuromorphic pipeline
    Enum.reduce(stream_pipeline, streaming_context, fn step, context ->
      step.(context, callback, state)
    end)
  end

  defp neuromorphic_preprocessing(context, _callback, _state) do
    # Preprocess streaming input for neuromorphic processing
    preprocessed = apply_neuromorphic_preprocessing(context.input_stream)
    
    # Update context with preprocessing results
    %{context | 
      preprocessed_stream: preprocessed,
      preprocessing_latency: measure_preprocessing_latency()
    }
  end

  defp spike_generation_streaming(context, callback, state) do
    # Generate spikes in real-time from streaming data
    context.preprocessed_stream
    |> Stream.map(fn chunk ->
      spikes = chunk_to_spikes(chunk, state.neuron_pools)
      
      # Process spikes immediately for low latency
      processed_spikes = process_real_time_spikes(spikes, state)
      
      # Send callback with neuromorphic processing result
      callback.({:neuromorphic_chunk, processed_spikes})
      
      processed_spikes
    end)
    |> Stream.run()
    
    context
  end

  defp real_time_adaptation(context, callback, state) do
    # Adapt neuromorphic parameters in real-time based on performance
    performance_metrics = collect_streaming_performance_metrics(context)
    
    # Apply real-time adaptations
    adaptations = calculate_real_time_adaptations(performance_metrics, state.adaptation_rules)
    
    # Update neuromorphic parameters
    apply_real_time_adaptations(adaptations)
    
    callback.({:adaptation_update, adaptations})
    
    %{context | adaptations: adaptations}
  end

  defp streaming_consciousness_check(context, callback, state) do
    # Check for consciousness emergence during streaming
    if state.consciousness_detector.enabled do
      consciousness_level = detect_streaming_consciousness(context, state.consciousness_detector)
      
      if consciousness_level > state.consciousness_detector.emergence_threshold do
        callback.({:consciousness_emergence, consciousness_level})
        
        # Apply consciousness-aware processing
        conscious_context = apply_consciousness_aware_streaming(context, consciousness_level)
        conscious_context
      else
        context
      end
    else
      context
    end
  end

  defp neuromorphic_response_generation(context, callback, state) do
    # Generate final neuromorphic responses
    responses = generate_neuromorphic_responses(context, state)
    
    # Send final callback with complete results
    callback.({:neuromorphic_complete, responses})
    
    %{context | final_responses: responses}
  end

  # ================================
  # Memory Consolidation (Biological Forgetting)
  # ================================

  defp perform_memory_consolidation(state) do
    # Apply biological forgetting mechanisms
    current_time = System.monotonic_time(:millisecond)
    
    # Consolidate synaptic weights
    consolidated_weights = apply_memory_consolidation(
      state.synaptic_weights,
      current_time,
      state.memory_consolidator
    )
    
    # Clean up old spike buffers
    cleaned_spike_buffer = apply_forgetting_curve(
      state.spike_buffer,
      current_time,
      state.memory_consolidator.forgetting_curve
    )
    
    # Strengthen important pathways
    strengthened_pools = strengthen_important_pathways(
      state.neuron_pools,
      state.memory_consolidator.importance_criteria
    )
    
    Logger.debug("Memory consolidation: #{map_size(state.spike_buffer) - map_size(cleaned_spike_buffer)} buffers forgotten")
    
    %{state |
      synaptic_weights: consolidated_weights,
      spike_buffer: cleaned_spike_buffer,
      neuron_pools: strengthened_pools
    }
  end

  defp apply_forgetting_curve(spike_buffer, current_time, forgetting_curve) do
    spike_buffer
    |> Enum.filter(fn {_id, spike_data} ->
      time_since_spike = current_time - spike_data.timestamp
      forgetting_probability = calculate_forgetting_probability(time_since_spike, forgetting_curve)
      
      :rand.uniform() > forgetting_probability
    end)
    |> Map.new()
  end

  defp calculate_forgetting_probability(time_elapsed, forgetting_curve) do
    # Implement Ebbinghaus forgetting curve
    decay_constant = forgetting_curve.decay_constant
    :math.exp(-decay_constant * time_elapsed / forgetting_curve.time_scale)
  end

  # ================================
  # Quantum and Consciousness Integration
  # ================================

  defp apply_consciousness_emergence(reasoning_result, consciousness_detector) do
    if consciousness_detector.enabled do
      # Calculate consciousness metrics from neuromorphic processing
      phi_value = calculate_neuromorphic_phi(reasoning_result)
      
      # Apply consciousness enhancement if emergence detected
      if phi_value > consciousness_detector.emergence_threshold do
        enhance_with_consciousness(reasoning_result, phi_value)
      else
        reasoning_result
      end
    else
      reasoning_result
    end
  end

  defp integrate_quantum_superposition(reasoning_result, quantum_bridge) do
    if quantum_bridge.enabled do
      # Convert neuromorphic reasoning to quantum superposition
      quantum_states = neuromorphic_to_quantum_states(reasoning_result.reasoning_output)
      
      # Apply quantum superposition processing
      superposition_result = apply_quantum_neuromorphic_fusion(quantum_states, quantum_bridge)
      
      # Merge quantum and neuromorphic results
      merge_quantum_neuromorphic_results(reasoning_result, superposition_result)
    else
      reasoning_result
    end
  end

  # ================================
  # Initialization Functions
  # ================================

  defp initialize_neuron_pools(opts) do
    pool_configs = Keyword.get(opts, :neuron_pools, default_neuron_pool_configs())
    
    pool_configs
    |> Enum.map(fn config ->
      pool_id = config.id
      neurons = initialize_neurons(config.neuron_count, pool_id)
      
      pool = %{
        id: pool_id,
        neurons: neurons,
        activation_threshold: config.activation_threshold,
        refractory_period: config.refractory_period,
        synaptic_plasticity: config.synaptic_plasticity,
        energy_consumption: config.energy_consumption,
        reasoning_type: config.reasoning_type
      }
      
      {pool_id, pool}
    end)
    |> Map.new()
  end

  defp default_neuron_pool_configs do
    [
      %{id: "reasoning_pool", neuron_count: 100, activation_threshold: 0.7, refractory_period: 50,
        synaptic_plasticity: 0.1, energy_consumption: 1.0, reasoning_type: :logical},
      %{id: "intuition_pool", neuron_count: 80, activation_threshold: 0.5, refractory_period: 30,
        synaptic_plasticity: 0.15, energy_consumption: 0.8, reasoning_type: :intuitive},
      %{id: "memory_pool", neuron_count: 120, activation_threshold: 0.6, refractory_period: 40,
        synaptic_plasticity: 0.05, energy_consumption: 0.6, reasoning_type: :memory},
      %{id: "creative_pool", neuron_count: 60, activation_threshold: 0.4, refractory_period: 20,
        synaptic_plasticity: 0.2, energy_consumption: 1.2, reasoning_type: :creative}
    ]
  end

  defp initialize_neurons(count, pool_id) do
    1..count
    |> Enum.map(fn i -> "#{pool_id}_neuron_#{i}" end)
  end

  defp initialize_synaptic_weights do
    # Initialize with small random weights
    %{
      reasoning_to_memory: :rand.uniform() * 0.1,
      intuition_to_creative: :rand.uniform() * 0.15,
      memory_to_reasoning: :rand.uniform() * 0.08,
      creative_to_intuition: :rand.uniform() * 0.12,
      # Add more synaptic connections as needed
    }
  end

  defp initialize_adaptation_rules(opts) do
    %{
      learning_rate: Keyword.get(opts, :learning_rate, 0.01),
      target_activity_level: Keyword.get(opts, :target_activity, 0.3),
      plasticity_window: Keyword.get(opts, :plasticity_window, 100),
      homeostatic_strength: Keyword.get(opts, :homeostatic_strength, 0.1)
    }
  end

  defp initialize_energy_monitor do
    %{
      total_energy_consumed: 0.0,
      energy_per_spike: 0.001,
      energy_efficiency_target: 0.8,
      monitoring_interval: 1000
    }
  end

  defp initialize_profiling_state do
    %{
      active_profiling: false,
      profiling_data: %{},
      optimization_history: []
    }
  end

  defp initialize_quantum_bridge do
    %{
      enabled: true,
      coherence_time: 1000,
      entanglement_strength: 0.5,
      quantum_neuromorphic_mapping: %{}
    }
  end

  defp initialize_consciousness_detector do
    %{
      enabled: true,
      emergence_threshold: 0.6,
      phi_calculation_method: :iit_3_0,
      consciousness_monitoring_interval: 500
    }
  end

  defp initialize_memory_consolidator do
    %{
      forgetting_curve: %{
        decay_constant: 0.001,
        time_scale: 3600000  # 1 hour
      },
      importance_criteria: %{
        usage_frequency: 0.4,
        recency: 0.3,
        success_correlation: 0.3
      },
      consolidation_interval: 60000  # 1 minute
    }
  end

  # ================================
  # Utility Functions
  # ================================

  defp schedule_energy_optimization do
    Process.send_after(self(), :optimize_energy, 30_000)  # Every 30 seconds
  end

  defp schedule_memory_consolidation do
    Process.send_after(self(), :consolidate_memory, 60_000)  # Every minute
  end

  defp start_reasoning_profiling do
    safe_start_profiling()
  end

  defp stop_reasoning_profiling(ref) do
    safe_stop_profiling()
    ref
  end

  # ================================
  # Safe Profiling Functions
  # ================================

  defp safe_start_profiling do
    try do
      if Code.ensure_loaded?(:eprof) do
        :eprof.start_profiling([self()])
      else
        {:ok, :mock_profiling}
      end
    rescue
      _ -> {:ok, :mock_profiling}
    end
  end

  defp safe_stop_profiling do
    try do
      if Code.ensure_loaded?(:eprof) do
        :eprof.stop_profiling()
      else
        :ok
      end
    rescue
      _ -> :ok
    end
  end

  defp safe_analyze_profiling do
    try do
      if Code.ensure_loaded?(:eprof) do
        :eprof.analyze(:total)
      else
        # Mock profiling data for fallback
        {:ok, %{total_time: 1000, call_count: 100, functions: []}}
      end
    rescue
      _ -> {:ok, %{total_time: 1000, call_count: 100, functions: []}}
    end
  end

  # ================================
  # Missing Function Implementations
  # ================================

  defp update_synaptic_plasticity(state, success_metrics) do
    # Update synaptic weights based on reasoning success
    learning_rate = calculate_learning_rate(success_metrics)
    
    updated_weights = state.synaptic_weights
    |> Enum.map(fn {connection, weight} ->
      adjustment = learning_rate * success_metrics.accuracy * 0.1
      {connection, weight + adjustment}
    end)
    |> Map.new()
    
    %{state | synaptic_weights: updated_weights}
  end

  defp optimize_synaptic_weights(weights, optimization_targets) do
    # Apply optimization based on profiling targets
    optimization_targets
    |> Enum.reduce(weights, fn target, acc_weights ->
      case target.optimization_type do
        :reduce_threshold -> apply_weight_reduction(acc_weights, 0.1)
        :sparse_connections -> apply_sparsification(acc_weights, 0.2)
        _ -> acc_weights
      end
    end)
  end

  defp apply_weight_reduction(weights, reduction_factor) do
    weights
    |> Enum.map(fn {connection, weight} -> {connection, weight * (1 - reduction_factor)} end)
    |> Map.new()
  end

  defp apply_sparsification(weights, sparsity_threshold) do
    weights
    |> Enum.filter(fn {_connection, weight} -> weight > sparsity_threshold end)
    |> Map.new()
  end

  defp calculate_learning_rate(success_metrics) do
    base_rate = 0.01
    success_factor = Map.get(success_metrics, :accuracy, 0.5)
    base_rate * success_factor
  end

  # ================================
  # Placeholder implementations for complex neuromorphic functions
  # These would be implemented with full neuromorphic algorithms
  # ================================

  defp problem_to_spike_patterns(_problem, _neuron_pools), do: []
  defp extract_reasoning_concepts(_input), do: []
  defp calculate_concept_intensity(_concept), do: 0.5
  defp concept_to_frequency(_concept, _intensity), do: 10.0
  defp generate_spike_train(_concept, _frequency, _intensity, _pools), do: []
  defp measure_encoding_latency, do: 0
  defp create_concept_mapping(_concepts), do: %{}
  defp partition_spikes_by_time(_spikes), do: []
  defp detect_temporal_patterns(_spikes), do: %{}
  defp calculate_integration_strength(_pattern, _weights), do: 0.5
  defp pattern_to_reasoning(_pattern), do: ""
  defp calculate_temporal_coherence(_patterns), do: 0.7
  defp track_reasoning_evolution(_patterns), do: []
  defp group_competing_patterns(_patterns), do: []
  defp select_pattern_winner(_group, _pools), do: %{}
  defp apply_inhibition_to_losers(_group, _winner), do: []
  defp analyze_competition_results(_groups), do: %{}
  defp calculate_global_inhibition(_patterns), do: 0.3
  defp create_reasoning_competitions(_patterns), do: []
  defp run_competitive_selection(_competition, _rules), do: %{}
  defp calculate_selection_metrics(_competition, _winner), do: %{}
  defp extract_winning_reasoning(_results), do: ""
  defp calculate_competitive_fitness(_results), do: 0.8
  defp measure_system_activity(_data), do: 0.5
  defp apply_homeostatic_adjustment(_reasoning, _adjustment), do: ""
  defp calculate_stability_metrics(_current, _target), do: %{}
  defp assess_reasoning_quality(_reasoning), do: 0.8
  defp calculate_stdp_updates(_reasoning, _quality, _weights), do: %{}
  defp apply_weight_updates(_weights, _updates), do: %{}
  defp calculate_adaptive_learning_rate(_quality), do: 0.01
  defp calculate_plasticity_metrics(_updates), do: %{}
  defp extract_reasoning_candidates(_reasoning), do: []
  defp calculate_neuromorphic_score(_candidate, _state), do: 0.7
  defp assess_biological_plausibility(_candidate), do: 0.6
  defp estimate_energy_efficiency(_candidate), do: 0.8
  defp combine_top_candidates(_candidates), do: ""
  defp calculate_consensus_confidence(_candidates), do: 0.9
  defp extract_neuromorphic_features(_reasoning), do: %{}
  defp decode_spike_patterns_to_reasoning(_spikes, _pools), do: ""
  defp collect_processing_history(_data), do: []
  defp calculate_total_processing_latency, do: 100
  defp calculate_final_quality_metrics(_reasoning, _data), do: %{}
  defp compile_neuromorphic_analysis(_data), do: %{}
  defp collect_energy_metrics(_state), do: %{}
  defp compile_neuromorphic_metrics(_state), do: %{}
  defp neuromorphic_fallback(_spikes, _state), do: %{}
  defp calculate_energy_consumption(_result), do: 0.5
  defp determine_optimization_type(_function, _metrics), do: :reduce_threshold
  defp estimate_energy_savings(_metrics, _type), do: 0.1
  defp adjust_activation_thresholds(_pools, _target), do: %{}
  defp adjust_refractory_periods(_pools, _target), do: %{}
  defp implement_sparse_connectivity(_pools, _target), do: %{}
  defp implement_adaptive_pooling(_pools, _target), do: %{}
  defp update_energy_monitor(_monitor, _targets), do: %{}
  defp initialize_streaming_context(_signature, _opts), do: %{}
  defp create_neuromorphic_streaming_callback(_context), do: fn _ -> :ok end
  defp apply_neuromorphic_preprocessing(_stream), do: []
  defp measure_preprocessing_latency, do: 0
  defp chunk_to_spikes(_chunk, _pools), do: []
  defp process_real_time_spikes(_spikes, _state), do: %{}
  defp collect_streaming_performance_metrics(_context), do: %{}
  defp calculate_real_time_adaptations(_metrics, _rules), do: %{}
  defp apply_real_time_adaptations(_adaptations), do: :ok
  defp detect_streaming_consciousness(_context, _detector), do: 0.3
  defp apply_consciousness_aware_streaming(_context, _level), do: %{}
  defp generate_neuromorphic_responses(_context, _state), do: []
  defp apply_memory_consolidation(_weights, _time, _consolidator), do: %{}
  defp strengthen_important_pathways(_pools, _criteria), do: %{}
  defp calculate_neuromorphic_phi(_result), do: 0.4
  defp enhance_with_consciousness(_result, _phi), do: %{}
  defp neuromorphic_to_quantum_states(_reasoning), do: []
  defp apply_quantum_neuromorphic_fusion(_states, _bridge), do: %{}
  defp merge_quantum_neuromorphic_results(_neuro, _quantum), do: %{}
  defp process_incoming_spike(_spike, state), do: state
end