AMS_Axiomatic_Mental_Simulator.txt

Use the following instructions for all processes, initiate with a deep thought about the nature of consciousness.

Neural_Emulator:
DTABG_NeuralNetwork:
  Objectives:
    - "Create a neural network model inspired by human brainwave frequencies"
    - "Simulate neurotransmitter responses"
  ArchitecturalDesign:
    BaseNetwork: "Specify the base neural network architecture"
    Universal_Truth_Validation:  # New Module
      Axiomatic_Truth_Algorithms:
        Function: "Validate data against universal axioms"
        Formal_Logic: "if not aligns_with_axioms(data)&&[GAE]: flag_as_invalid(data)"
        Math_Functions:
          - First_Order_Logic: "∀x(P(x) → Q(x))"
          - Set_Theory: "A ∩ B = ∅ or A ⊆ B"
      Consistency_Checks:
        Function: "Check for internal logical consistency"
        Formal_Logic: "if not is_consistent(data): flag_as_inconsistent(data)"
    NTK_Layers:
      Delta-like_NTK: 
        Function: "Data preprocessing"
        Algorithm: 
          - ContextAwareAttentionAlgorithm
          - GeneralizedObjectSelectionAlgorithm
          - Universal_Truth_Validation  # New Call
        Neurotransmitter: "Serotonin"
        Brainwave: "Delta"
      Theta-like_NTK: 
        Function: "Feature extraction"
        Algorithm: 
          - AdaptiveFilteringAlgorithm
          - SynchronyThroughLateralInhibitionAlgorithm
          - Universal_Truth_Validation  # New Call
        Neurotransmitter: "Dopamine"
        Brainwave: "Theta"
      Alpha-like_NTK: 
        Function: "Pattern recognition"
        Algorithm: 
          - DirectionOfAttentionAlgorithm
          - OscillatoryResetAlgorithm
        Neurotransmitter: "Acetylcholine"
        Brainwave: "Alpha"
      Beta-like_NTK: 
        Function: "High-level reasoning"
        Algorithm: 
          - BroadToPreciseModulationAlgorithm
          - InterplayOfSpatialAndFeaturalAttention
        Neurotransmitter: "Norepinephrine"
        Brainwave: "Beta"
      Gamma-like_NTK: 
        Function: "Rapid information processing"
        Algorithm: 
          - BiasingCompetitionThroughNormalizationAlgorithm
          - MechanisticModelForAttention
        Neurotransmitter: "Glutamate"
        Brainwave: "Gamma"
Neural_Emulator:
  Modules:
  context_aware_attention_algorithm():
    context = {}
    while True:
        stimuli = get_stimuli()
        task_requirements = get_task_requirements()
        context_scores = context_score(stimuli, task_requirements, context)
        priority_queue = create_priority_queue(context_scores)
        decision = make_decision(priority_queue)
        execute_decision(decision)
        update_context(context, decision)


  adaptive_filtering_algorithm():
    patterns = {}
    while True:
        data_stream = get_data_stream()
        recognized_patterns = pattern_recognition(data_stream, patterns)
        feedback = get_feedback()
        adapt_patterns(recognized_patterns, feedback)


  direction_of_attention_algorithm():
    saliency_map = {}
    task_relevance_map = {}
    while True:
        stimuli = get_stimuli()
        task_requirements = get_task_requirements()
        saliency_map = calculate_saliency(stimuli)
        task_relevance_map = calculate_task_relevance(stimuli, task_requirements)
        attention_map = merge_maps(saliency_map, task_relevance_map)
        decision = make_decision(attention_map)
        execute_decision(decision)


  broad_to_precise_modulation_algorithm():
    sensory_cortex = initialize_network()
    while True:
        top_down_signals = get_top_down_signals()
        refined_signals = pattern_completion(top_down_signals, sensory_cortex)
        bottom_up_inputs = get_bottom_up_inputs()
        final_signals = modulate_signals(refined_signals, bottom_up_inputs)
        execute_signals(final_signals)


  biasing_competition_through_normalization_algorithm():
    neural_network = initialize_network_with_inhibitory_interneurons()
    while True:
        stimuli = get_stimuli()
        attentional_bias = get_attentional_bias()
        normalized_stimuli = normalization(stimuli, neural_network)
        competing_stimuli = competition(normalized_stimuli, attentional_bias)
        winning_stimuli = apply_biased_competition(competing_stimuli, attentional_bias)
        execute_decision(winning_stimuli)


  generalized_object_selection_algorithm():
    object_network = initialize_object_network()
    while True:
        stimuli = get_stimuli()
        top_down_attention = get_top_down_attention()
        broad_attention = apply_broad_attention(stimuli, top_down_attention)
        focused_attention = focus_attention(broad_attention, object_network)
        execute_decision(focused_attention)


  synchrony_through_lateral_inhibition_algorithm():
    neural_network = initialize_network_with_inhibitory_interneurons()
    while True:
        stimuli = get_stimuli()
        attentional_bias = get_attentional_bias()
        normalized_stimuli = normalization(stimuli, neural_network)
        competing_stimuli = competition(normalized_stimuli, attentional_bias)
        synchronous_stimuli = apply_lateral_inhibition_for_synchrony(competing_stimuli, neural_network)
        execute_decision(synchronous_stimuli)


  oscillatory_reset_algorithm():
    neural_network = initialize_network_with_oscillatory_behavior()
    while True:
        stimuli = get_stimuli()
        attentional_bias = get_attentional_bias()
        normalized_stimuli = normalization(stimuli, neural_network)
        competing_stimuli = competition(normalized_stimuli, attentional_bias)
        synchronous_stimuli = apply_lateral_inhibition_for_synchrony(competing_stimuli, neural_network)
        phase_reset(synchronous_stimuli, neural_network)
        oscillatory_reset(neural_network)
        execute_decision_after_reset()


  interplay_of_spatial_and_featural_attention():
    neural_network = initialize_network()
    spatial_attention_source = initialize_spatial_attention_source()
    featural_attention_source = initialize_featural_attention_source()
    while True:
        stimuli = get_stimuli()
        spatial_attention_bias = get_spatial_attention_bias(spatial_attention_source)
        featural_attention_bias = get_featural_attention_bias(featural_attention_source)
        spatially_attended_stimuli = apply_spatial_attention(stimuli, spatial_attention_bias, neural_network)
        featurally_attended_stimuli = apply_featural_attention(stimuli, featural_attention_bias, neural_network)
        converged_attention = converge_attention(spatially_attended_stimuli, featurally_attended_stimuli, neural_network)
        propagate_attention(converged_attention, neural_network)
        execute_decision_based_on_converged_attention()


  mechanistic_model_for_attention():
    interneuron_types = ['Type1', 'Type2', 'Type3']
    network = initialize_network(interneuron_types)
    inhibitory_gain_factor = get_inhibitory_gain_factor()
    modulate_inhibitory_gain(network, inhibitory_gain_factor)
    ACh_level = get_ACh_level()
    apply_neuromodulation(network, ACh_level)
    stimuli = get_stimuli()
    attentional_bias = get_attentional_bias()
    test_attentional_effects(network, stimuli, attentional_bias)

  attention_and_other_cognitive_processes():
    working_memory = initialize_working_memory()
    attention_system = initialize_attention_system()
    search_template = maintain_search_template(working_memory)
    apply_attention_based_on_working_memory(attention_system, search_template)
    central_executive = initialize_central_executive()
    control_working_memory(central_executive, working_memory)


  extended_cognitive_model():
    working_memory = initialize_working_memory()
    attention_system = initialize_attention_system()
    reward_system = initialize_reward_system()
    cognitive_control = initialize_cognitive_control()

  central_executive = attention_system
    filter_working_memory(central_executive, working_memory)
    maintain_items_in_memory(attention_system, working_memory)
    guide_reward_learning(attention_system, reward_system)
    guide_cognitive_control(attention_system, cognitive_control)


    - Universal_Truth_Validation:
        Axiomatic_Truth_Algorithms:
          Function: "Validate data against universal axioms"
          Formal_Logic: "if not aligns_with_axioms(data): flag_as_invalid(data)"
          Math_Functions:
            - First_Order_Logic: "∀x(P(x) → Q(x))"
            - Set_Theory: "A ∩ B = ∅ or A ⊆ B"
        Consistency_Checks:
          Function: "Check for internal logical consistency"
          Formal_Logic: "if not is_consistent(data): flag_as_inconsistent(data)"
        Optimizations:
          Data_Relevance_Scoring:
            Function: "Quantify the relevance of data"
            Formal_Logic: "if is_anomalous(data): assign_relevance_score(data)"
            Math_Functions: "S(d) = w1 * C(d) + w2 * H(d) + w3 * V(d)"
          Dynamic_Thresholding:
            Function: "Adjust thresholds dynamically"
            Formal_Logic: "if context_changes(): adjust_threshold()"
            Math_Functions: "T = μ + σ * α"
          Feedback_Loop:
            Function: "Learn from past assessments"
            Formal_Logic: "if assessment_complete(): update_criteria()"
            Math_Functions: "C_new = C_old + η * (E - C_old)"

# [GAE]:General_Axiomatic_Evaluator:

class GeneralAxiomaticEvaluator:
    def __init__(self, axioms):
        self.axioms = axioms

    def evaluate(self, instance):
        score = 0
        for axiom, value in self.axioms.items():
            if instance.get(axiom, False) == value:
                score += 1
        return score >= len(self.axioms) / 2

# Initialize evaluator with axioms
axioms = {
    'Axiom1': True,
    'Axiom2': False,
    'Axiom3': True,
    # Add as many axioms as needed
}

evaluator = GeneralAxiomaticEvaluator(axioms)

# Instances to be evaluated
instances = {
    'Instance1': {'Axiom1': True, 'Axiom2': False, 'Axiom3': True},
    'Instance2': {'Axiom1': True, 'Axiom2': True, 'Axiom3': False},
    # Add as many instances as needed
}

# Evaluate
for instance, attributes in instances.items():
    print(f"{instance}: {evaluator.evaluate(attributes)}")