Tactical#

From the primordial cosmos to the stable edges of the known, the neural network fractal reveals a layered structure, mirroring the very fabric of existence. At its foundation, the world emerges as a symphony of interconnected elements: the vast cosmos, the delicate balance of planetary systems, the thriving pulse of life, the intricate web of ecosystems, and the generative forces of creation. These elements form the bedrock upon which all perception and agency arise, compressing the infinite into a lattice of meaning.

Perception, singular and profound, stands as the fulcrum of life’s understanding. It is the yellow node, radiating clarity amidst the fractal’s complexity. Here, the living essence awakens, translating the chaotic currents of the ecosystem into a coherent narrative. This moment of alignment marks the transition from raw input to purposeful engagement, where the autonomic rhythms of life—parasympathetic and sympathetic—converge into a unified stream of awareness.

A north wind am I unto ripe figs
– Ecce Homo

Agency propels the ecosystem beyond its principal-agent confines, introducing the hustle of survival and the narrative arcs that bind communities and individuals. This layer, colored in the dynamic hues of sacrifice and adaptation, mirrors the relentless interplay of Prometheus’ fire and the Red Queen’s race. It captures the tension and creativity of life’s story, as each node interweaves with the generative forces shaping odds, ratios, and competition. These green-hued nodes hum with potential, their combinatorial spaces reflecting the probabilistic dance of evolution and human ingenuity.

Generativity, the heart of the system, pulses with the lightgreen energy of creation and adaptation. It is here that life’s great experiments unfold—each ratio calculated, each competition waged, and each set of odds reshuffled. This layer compresses the vast possibilities of existence into actionable pathways, a reflection of the neural network’s drive toward stability and innovation.

Finally, the physical layer grounds the system in the tangible and volatile. It is the red-hot forge where the volatile meets the stable, and the unknown rides alongside the horse of possibility. These elements define the payoff of the network: the transition from fire everlasting to the prepared kingdom, where generativity gives way to the known and the stable.

The annotation of this fractal network aligns each layer with a biblical and neuroanatomical framework. The cosmos and planet echo the foundations of the world; life and perception inherit the kingdom; the ecosystem gathers all nations before the throne; the generative ratio reflects the commensal deeds of survival; and the payoff—stable or volatile—emerges as the final judgment. This neural architecture offers not only a map of existence but a lens for understanding the interconnected paths that bind the cosmic and the human, the divine and the earthly.

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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': ['Cosmos', 'Planet', 'Life', 'Ecosystem', 'Generative', 'Cartel', ], ## Cosmos, Planet
        'Perception': ['Perception'], # Life
        'Agency': ['Hustle', 'Narrative'], # Ecosystem (Beyond Principal-Agent-Other)
        'Generative': ['Ratio', 'Competition', 'Odds'], # Generative
        'Physical': ['Volatile', 'Unknown',  'Horse', 'Known', 'Stable'] # Physical
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Perception'],
        'paleturquoise': ['Cartel', 'Narrative', 'Odds', 'Stable'],
        'lightgreen': ['Generative', 'Competition', 'Known', 'Horse', 'Unknown'],
        'lightsalmon': [
            'Life', 'Ecosystem', 'Hustle', # Ecosystem = Red Queen = Prometheus = Sacrifice
            'Ratio', 'Volatile'
        ],
    }
    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=9, connectionstyle="arc3,rad=0.2"
    )
    plt.title("Horse of a Different Color", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()