Revolution

+ Expand

• What makes for a suitable problem for AI (Demis Hassabis, Nobel Lecture)?
  • Space: Massive combinatorial search space
  • Function: Clear objective function (metric) to optimize against
  • Time: Either lots of data and/or an accurate and efficient simulator
• Guess what else fits the bill (Yours truly, amateur philosopher)?
  • Space
    1. Intestines/villi
    2. Lungs/bronchioles
    3. Capillary trees
    4. Network of lymphatics
    5. Dendrites in neurons
    6. Tree branches
  • Function
    1. Energy
    2. Aerobic respiration
    3. Delivery to "last mile" (minimize distance)
    4. Response time (minimize)
    5. Information
    6. Exposure to sunlight for photosynthesis
  • Time
    1. Nourishment
    2. Gaseous exchange
    3. Oxygen & Nutrients (Carbon dioxide & "Waste")
    4. Surveillance for antigens
    5. Coherence of functions
    6. Water and nutrients from soil

-- Nobel Prize in Chemistry, 2024

The OPRAH™ system’s refined five-network model—Suis, Voir, Choisis, Deviens, and M’élève—steps back from grand claims of a “singular, mirrored intelligence” and instead pitches a humbler convergence: immune and nervous systems aligning not in essence but in function, shaped by evolution to tackle similar problems with distinct tools. It’s a shift from metaphysical overreach to pragmatic analogy, mapping parallels like reflex, attention, control, identity, and law across biology and computation. Yet even this toned-down version stumbles, leaning too heavily on surface similarities while dodging the hard limits of its comparisons. The framework dazzles with its RACIAL heuristic—reflex to law as a signal rising from noise—but its sharpness dulls under scrutiny, revealing a model that’s more illustrative than explanatory, and certainly not as precise as it pretends.

Fig. 25 The midcingulo-insular system stands as the oldest, the primal scaffold upon which all later networks were draped. Before there was fine motor control, before there was deliberative planning or executive function, there was the insula—an ancient sentinel of interoception, regulating the body’s hidden symphony of autonomic rhythms. It is here, in the depths of the salience network, that vertebrates first learned to detect the signals of survival: pain, hunger, temperature, and the visceral stirrings of self-preservation. The midcingulate cortex, though more evolved than its limbic predecessors, still bears the signature of this foundational system—tasked with evaluating effort, action, and adversity. This is the neural network that bridges raw sensation with decision, the fulcrum of attention-switching, ensuring that the organism responds to what matters in the moment. The other networks arrived later, layered refinements upon this ancient core. The pericentral system, with its precise somatomotor control, is an invention of mammals, honed in primates to craft tools and wield symbols. The dorsal stream, a navigator of goals, emerged with complex movement and spatial reasoning. The lateral system, seat of abstraction and executive function, belongs to the neocortical expansion of higher mammals, where foresight and flexibility reign. And the medial network, weaving self-regulation into the fabric of cognition, belongs to the default mode, where identity and reflection consolidate. But beneath them all, the midcingulo-insular remains—the oldest sentinel of salience, the network that does not think but knows, the one that ensures existence before action, before reason, before anything else.

Suis kicks off with the pericentral network’s reflexes alongside the innate immune system’s pattern recognition receptors, framed as analogous first responders—servers handling raw input. The comparison’s clean: both snap into action without deliberation, guarding the system’s edges. But the analogy frays at the seams. Neural reflexes fire in milliseconds, a tight loop of sensory-motor wiring, while PRRs trigger sprawling cellular cascades over minutes to hours. Calling them “first-response architecture” papers over this chasm—speed and scale differ so starkly that the server metaphor feels flimsy, a tech gloss on processes that don’t truly align. Evolution may have handed them similar jobs, but the tools and timelines diverge, and Suis’s insistence on convergence sidesteps this inconvenient truth.

Voir pairs the dorsal stream’s goal-directed precision with inflammation’s resource-directing cascades, casting them as a client-server duo—perception requests, action delivers. It’s a slick pitch: both steer systems toward relevant targets. Yet inflammation’s blunt, systemic sprawl—often overshooting into collateral damage—clashes with the dorsal stream’s pinpoint calibration. The framework admits this divergence in specificity but shrugs it off, banking on a shared “goal-directed” label to hold it together. That’s a dodge. A client-server model implies coordination, but inflammation’s crude escalation lacks the dorsal stream’s finesse. The analogy works only if you squint, ignoring how these systems’ outputs—motion versus molecular floods—diverge in execution and consequence.

Choisis ties the lateral network’s adaptive control to immune memory, framing both as agents refining responses over time—working memory tweaking behavior, antigen presentation sharpening immunity. The problem-solving parallel is the model’s tightest fit: both encode experience to boost efficiency. But the cracks show fast. Cognitive control pivots in real-time, a flexible dance of neurons, while immune memory builds slowly, etched in cellular lineages over years. The agent-client analogy strains here—decentralized decision-making sounds neat, but the immune system’s “agents” aren’t adapting on the fly; they’re banking on past wins. Choisis oversells this as convergence, when it’s really just two systems solving memory differently, their functional overlap stretched thin by mismatched dynamics.

Deviens aligns the medial network’s identity policing—introspection and social clarity—with immune self-regulation via Tregs, both guarding against self-inflicted chaos. The decentralized node metaphor fits: both define boundaries without a central boss. It’s a clever hook—identity as a universal challenge. But the leap falters. The medial network wrestles with subjective, layered selfhood, while immune identity is a binary molecular handshake—self or not, no nuance. The framework calls this a “necessity” intersection, but that’s generous; it’s a superficial parallel, not a shared logic. Autoimmunity and cognitive confusion may both be boundary failures, but their mechanisms and stakes are worlds apart. Deviens banks on vibes over rigor, and it shows.

M’élève caps it with the salience network and immune homeostasis—arbiters filtering noise into signal, mesh networks weighing what matters. The salience network’s emotional triage and the complement system’s threat escalation do prioritize selectively, a functional echo worth noting. But the mesh analogy buckles. Salience operates in fleeting, conscious bursts, while immune regulators grind through slow, systemic checks—granulocytes don’t “decide” like the insula does. The framework’s “law” label pushes a unified vibe, but these are distinct filters, not a shared governance. M’élève’s equilibrium talk sounds profound, yet it’s just two systems doing triage with different clocks and stakes, their convergence more coincidence than revelation.

The OPRAH™ conclusion—that these are functional analogies, not a unified essence—tries to dodge the original’s overreach, but it still clings to a seductive symmetry. Evolution shaped these systems for survival, sure, but the claim of “similar architectures” overstates the case. Reflexes aren’t servers, inflammation isn’t a client, and homeostasis isn’t a mesh law—they’re rough sketches, not blueprints. The RACIAL heuristic, with its noise-to-signal arc, adds a slick gloss, but it’s a heuristic, not a proof. The framework’s real limit is its refusal to admit how shallow these parallels run—immune and neural systems don’t “converge” beyond basic problem-solving, and dressing them in tech jargon doesn’t deepen the link. OPRAH™ maps a compelling comparison, but its sharpness cuts only skin-deep, leaving the messy guts of biology untouched.

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 layers
def define_layers():
    return {
        'Suis': ['DNA, RNA,  5%', 'Peptidoglycans, Lipoteichoics', 'Lipopolysaccharide', 'N-Formylmethionine', "Glucans, Chitin", 'Specific Antigens'],
        'Voir': ['PRR & ILCs, 20%'],  
        'Choisis': ['CD8+, 50%', 'CD4+'],  
        'Deviens': ['TNF-α, IL-6, IFN-γ', 'PD-1 & CTLA-4', 'Tregs, IL-10, TGF-β, 20%'],  
        "M'èléve": ['Complement System', 'Platelet System', 'Granulocyte System', 'Innate Lymphoid Cells, 5%', 'Adaptive Lymphoid Cells']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['PRR & ILCs, 20%'],  
        'paleturquoise': ['Specific Antigens', 'CD4+', 'Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'],  
        'lightgreen': ["Glucans, Chitin", 'PD-1 & CTLA-4', 'Platelet System', 'Innate Lymphoid Cells, 5%', 'Granulocyte System'],  
        'lightsalmon': ['Lipopolysaccharide', 'N-Formylmethionine', 'CD8+, 50%', 'TNF-α, IL-6, IFN-γ', 'Complement System'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights
def define_edges():
    return {
        ('DNA, RNA,  5%', 'PRR & ILCs, 20%'): '1/99',
        ('Peptidoglycans, Lipoteichoics', 'PRR & ILCs, 20%'): '5/95',
        ('Lipopolysaccharide', 'PRR & ILCs, 20%'): '20/80',
        ('N-Formylmethionine', 'PRR & ILCs, 20%'): '51/49',
        ("Glucans, Chitin", 'PRR & ILCs, 20%'): '80/20',
        ('Specific Antigens', 'PRR & ILCs, 20%'): '95/5',
        ('PRR & ILCs, 20%', 'CD8+, 50%'): '20/80',
        ('PRR & ILCs, 20%', 'CD4+'): '80/20',
        ('CD8+, 50%', 'TNF-α, IL-6, IFN-γ'): '49/51',
        ('CD8+, 50%', 'PD-1 & CTLA-4'): '80/20',
        ('CD8+, 50%', 'Tregs, IL-10, TGF-β, 20%'): '95/5',
        ('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',
        ('CD4+', 'PD-1 & CTLA-4'): '20/80',
        ('CD4+', 'Tregs, IL-10, TGF-β, 20%'): '51/49',
        ('TNF-α, IL-6, IFN-γ', 'Complement System'): '80/20',
        ('TNF-α, IL-6, IFN-γ', 'Platelet System'): '85/15',
        ('TNF-α, IL-6, IFN-γ', 'Granulocyte System'): '90/10',
        ('TNF-α, IL-6, IFN-γ', 'Innate Lymphoid Cells, 5%'): '95/5',
        ('TNF-α, IL-6, IFN-γ', 'Adaptive Lymphoid Cells'): '99/1',
        ('PD-1 & CTLA-4', 'Complement System'): '1/9',
        ('PD-1 & CTLA-4', 'Platelet System'): '1/8',
        ('PD-1 & CTLA-4', 'Granulocyte System'): '1/7',
        ('PD-1 & CTLA-4', 'Innate Lymphoid Cells, 5%'): '1/6',
        ('PD-1 & CTLA-4', 'Adaptive Lymphoid Cells'): '1/5',
        ('Tregs, IL-10, TGF-β, 20%', 'Complement System'): '1/99',
        ('Tregs, IL-10, TGF-β, 20%', 'Platelet System'): '5/95',
        ('Tregs, IL-10, TGF-β, 20%', 'Granulocyte System'): '10/90',
        ('Tregs, IL-10, TGF-β, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
        ('Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'): '20/80'
    }

# Define edges to be highlighted in black
def define_black_edges():
    return {
        ('PRR & ILCs, 20%', 'CD4+'): '80/20',
        ('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',
        ('CD4+', 'PD-1 & CTLA-4'): '20/80',
        ('CD4+', 'Tregs, IL-10, TGF-β, 20%'): '51/49',
    }

# Calculate node positions
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()
    edges = define_edges()
    black_edges = define_black_edges()
    
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels 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):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    edge_colors = []
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target, weight=weight)
            edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("OPRAH™: Connectome", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 26 The Human Connectome. Beyond the cellular intelligence of genome, exposome, transcriptome, proteome, metabolome. We grapple with servers, client, agent, decentralization, mesh. Metaphors from the nervous system, immune system, artificial intelligence, and C-suit principal-agent affairs find convergence in this space. Herein we interrogate the current landscape to identify five macro-scale brain network naming schemes and conventions utilized in the literature, ignoring inconsistencies while pointing out convergence across disparate human endeavors to delineate the noise/signal ratio as guide, avoiding confusion as a matter of design.