Engineering

Mapping Tryptamine to Modes

1. Serotonin /5-Hydroxy (Cooperative):

Serotonin plays a crucial role in maintaining homeostasis, regulating mood, cognition, and physiological balance. Its steady, harmonizing function corresponds to the cooperative mode, which seeks equilibrium and unity within the system.

2. Melatonin/5-Methyl, N-Acetyl (Iterative):

Melatonin governs the sleep-wake cycle, creating regularity and synchrony through its modulation of circadian rhythms. This iterative, cyclical process reflects the iterative mode, emphasizing rhythm, gradual refinement, and adaptability to environmental feedback.

3. Ayahuasca/N,N-Dimethyl (Adversarial):

Fig. 8 N,N-Dimethyl Tryptamine. Fascinating!

Ayahuasca disrupts the status quo, pushing boundaries of perception and understanding. Its intense effects challenge the brain to adapt to new paradigms, aligning with the adversarial mode, which thrives on transformation through conflict and tension.

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Summary Table:

Molecule	Mode	Justification
Serotonin	Cooperative	Harmonizes systems to maintain homeostasis. (Chords)
Melatonin	Iterative	Governs rhythmic cycles, enabling adaptation. (Rhythm)
Ayahuasca	Adversarial	Disrupts frameworks, driving transformation. (Melody)

This highlights serotonin’s cooperative essence in fostering balance, while melatonin’s rhythmic nature exemplifies iteration.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network structure
def define_layers():
    return {
        'World': ['Cosmos', 'Earth', 'Life', 'Cost', 'Parallel', 'Time'], # Divine: Cosmos-Earth; Red Queen: Life-Cost; Machine: Parallel-Time
        'Perception': ['Perspectivism'],
        'Agency': ['Surprise', 'Optimism'],
        'Generativity': ['Anarchy', 'Oligarchy', 'Monarchy'],
        'Physicality': ['Dynamic', 'Partisan', 'Common Wealth', 'Non-Partisan', 'Static']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Perspectivism':
        return 'yellow'
    if layer == 'World' and node in [ 'Time']:
        return 'paleturquoise'
    if layer == 'World' and node in [ 'Parallel']:
        return 'lightgreen'
    if layer == 'World' and node in [ 'Cosmos', 'Earth']:
        return 'lightgray'
    elif layer == 'Agency' and node == 'Optimism':
        return 'paleturquoise'
    elif layer == 'Generativity':
        if node == 'Monarchy':
            return 'paleturquoise'
        elif node == 'Oligarchy':
            return 'lightgreen'
        elif node == 'Anarchy':
            return 'lightsalmon'
    elif layer == 'Physicality':
        if node == 'Static':
            return 'paleturquoise'
        elif node in ['Non-Partisan', 'Common Wealth', 'Partisan']:
            return 'lightgreen'
        elif node == 'Dynamic':
            return 'lightsalmon'
    return 'lightsalmon'  # Default color

# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2  # Center the layer vertically
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0  # Align nodes horizontally

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
        for node, position in zip(nodes, y_positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(assign_colors(node, layer_name))

    # Add edges (without weights)
    for layer_pair in [
        ('World', 'Perception'), ('Perception', 'Agency'), ('Agency', 'Generativity'), ('Generativity', 'Physicality')
    ]:
        source_layer, target_layer = layer_pair
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Snails Pace vs. Compressed Time", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 9 Time, Life, Cosmos, Perception. When people consume ayahuasca, they may enter an altered state of consciousness. Many experience intense visions, deep emotions, and significant introspection. The effects vary depending on the individual and the environment. Physical effects: Some people feel nausea, vomit (often seen as purifying), and become dizzy. Psychological effects: Vivid visions, shifts in the perception of time, and a connection with nature and the cosmos are common. In a safe ceremonial setting, participants can explore these effects in depth. For tourists, this offers a unique opportunity to embark on a spiritual journey. Source: Lorenzo Expeditions