Revolution

Fig. 21 What Exactly is Identity. A node may represent goats (in general) and another sheep (in general). But the identity of any specific animal (not its species) is a network. For this reason we might have a “black sheep”, distinct in certain ways – perhaps more like a goat than other sheep. But that’s all dull stuff. Mistaken identity is often the fuel of comedy, usually when the adversarial is mistaken for the cooperative or even the transactional.

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

# Define the neural network fractal
def define_layers():
    return {
        'World': ['Particles-Compression', 'Vibration-Particulate.Matter', 'Ear, Cerebellum-Georientation', 'Harmonic Series-Agency.Phonology', 'Space-Verb.Syntax', 'Time-Object.Meaning', ], # Resources
        'Perception': ['Rhythm, Pockets'], # Needs
        'Agency': ['Open-Nomiddleman', 'Closed-Trusted'], # Costs
        'Generative': ['Ratio-Weaponized', 'Competition-Tokenized', 'Odds-Monopolized'], # Means
        'Physical': ['Volatile-Revolutionary', 'Unveiled-Resentment',  'Freedom-Dance in Chains', 'Exuberant-Jubilee', 'Stable-Conservative'] # Ends
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Rhythm, Pockets'],
        'paleturquoise': ['Time-Object.Meaning', 'Closed-Trusted', 'Odds-Monopolized', 'Stable-Conservative'],
        'lightgreen': ['Space-Verb.Syntax', 'Competition-Tokenized', 'Exuberant-Jubilee', 'Freedom-Dance in Chains', 'Unveiled-Resentment'],
        'lightsalmon': [
            'Ear, Cerebellum-Georientation', 'Harmonic Series-Agency.Phonology', 'Open-Nomiddleman', 
            'Ratio-Weaponized', 'Volatile-Revolutionary'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))  # Default color fallback

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        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=8, connectionstyle="arc3,rad=0.2"
    )
    plt.title("Music", fontsize=13)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 22 Psilocybin is itself biologically inactive but is quickly converted by the body to psilocin, which has mind-altering effects similar, in some aspects, to those of other classical psychedelics. Effects include euphoria, hallucinations, changes in perception, a distorted sense of time, and perceived spiritual experiences. It can also cause adverse reactions such as nausea and panic attacks. In Nahuatl, the language of the Aztecs, the mushrooms were called teonanácatl—literally “divine mushroom.” Source: Wikipedia