Failure

The molecules you mention — 5-HT (serotonin), 5-Methoxy-N-Acetyltryptamine (melatonin), and N,N-Dimethyltryptamine (DMT) — form a fascinating triad in the tryptamine pathway. Each represents a distinct dimension of human consciousness, regulation, and transformation:

See also

Auxins in plants

1. 5-HT (Serotonin):
   The foundational molecule in this triad, serotonin governs mood, emotional regulation, and homeostasis. Its role as a neuromodulator makes it a cornerstone for baseline equilibrium in the brain, influencing everything from appetite to social behavior. It acts as the “anchor” to normalcy, the blue node of embodiment in your neural network model.

2. 5-M,N-AT (Melatonin):
   A derivative of serotonin, melatonin is the hormone of darkness and sleep, guiding the circadian rhythm. It serves as a timekeeper, linking environmental cycles to biological processes. Its association with rest, recovery, and temporal rhythm aligns it with your green node of tokenization, as it compresses environmental input into actionable biological signals.

3. N,N-DMT (Dimethyltryptamine):
   DMT is the visionary molecule, inducing profound altered states of consciousness. Often described as the “spirit molecule,” it disrupts conventional time-space perception and opens the door to what many describe as transcendental or ineffable experiences. It embodies transformation, fitting neatly into your red node of adversarial pathways.

Integration:

These molecules not only illustrate a neurochemical spectrum from stability (5-HT) to transformative experience (DMT), but they also resonate with your neural network’s three hidden nodes of equilibrium:

• Blue/Embodiment (Serotonin): Maintaining balance in the present.

• Green/Compression (Melatonin): Mediating the cycles of life, acting as a bridge.

• Red/Transformation (DMT): Breaking through the known into new dimensions.

This triad highlights the inherent potential within human biochemistry for profound experiences ranging from the mundane to the mystical, reflecting a deeply interconnected neural and symbolic framework.

Show code cell source Hide code cell source

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. 14 This Python script visualizes a neural network inspired by the “Red Queen Hypothesis,” constructing a layered hierarchy from physics to outcomes. It maps foundational physics (e.g., cosmos, earth, resources) to metaphysical perception (“Nostalgia”), agentic decision nodes (“Good” and “Bad”), and game-theoretic dynamics (sympathetic, parasympathetic, morality), culminating in outcomes (e.g., neurons, vulnerabilities, strengths). Nodes are color-coded: yellow for nostalgic cranial ganglia, turquoise for parasympathetic pathways, green for sociological compression, and salmon for biological or critical adversarial modes. Leveraging networkx and matplotlib, the script calculates node positions, assigns thematic colors, and plots connections, capturing the evolutionary progression from biology (red) to sociology (green) to psychology (blue). Framed with a nod to AlexNet and CUDA architecture, the network envisions an übermensch optimized through agentic ideals, hinting at irony as the output layer “beyond good and evil” aligns more with machine precision than human aspiration.