Knowledge can be represented as a graph of relationships with inheritance and exceptions, such that 'Fido is a dog' lets Fido inherit 'has fur' on the fly, while an exception (Stubby has no tail despite dogs having tails) overrides just that one attribute, enabling enormous compression of information.

A cortical column needs only about 100 neurons because that is enough for input/output neurons, a dozen or so AND-gates, and supporting functions, which implies most of the brain's neurons are not there for brute-force learning but are scaffolding waiting to be given meaning through experience.

If we want to build AI that actually thinks, we need to stop layering up weights and start wiring in structure, because today's AI lacks understanding, generalization, and common sense precisely because it does not represent knowledge as a graph of concepts and relationships.

The neocortex is composed of millions of nearly uniform repeating structures called cortical columns that all start with the same circuitry, and they specialize (e.g., visual cortex tuning to edges/colors, prefrontal cortex to planning) based on the signals they receive, implying the cortex is built from a single repeated general-purpose module.

Modern artificial neural networks and predictive coding models assume smooth gradients, statistical functions, and uniform layers, and while good at pattern recognition, they fall short at reasoning, understanding exceptions, or explaining concepts—partly because biological neurons are too slow to operate that way.

The brain works not as a statistical engine but as a biological graph processor where concepts are nodes and relationships are precise structured connections, which is the only plausible way the brain could store knowledge.

Each cortical column functions like a node in a graph, with connections representing relationships (is-a, has-a, part-of) that support inheritance, exceptions, and bidirectional links, giving the brain meaningful symbolic reasoning that today's artificial neural networks cannot do.

Learning a relationship in the brain occurs by selectively strengthening a tiny subset of pre-existing near-zero-weight synapses (e.g., from the 'is-a' column to an AND-gate in the Fido column and from that gate to the 'dog' output neuron), so the physical structure is already in place and learning just activates it.

Bidirectional links allow the model to activate Fido and retrieve all dog attributes (unless overridden by exceptions), and also to activate 'dog' and discover its instances like Fido, by strengthening a mirror connection with the relationship type 'is instance of'.

The brain has three major parts: the brain stem managing vital functions like heartbeat and breathing, the cerebellum handling coordination and motor learning, and the neocortex where conscious thought, planning, and reasoning happen.

In computer simulations of this model, connections are represented as a few numbers in an array, so the lateral-versus-white-matter distinction between short and long connections need not be made and all connections are treated as lateral.

The brain runs on just 20 watts yet shows understanding and common sense far beyond today's AI systems.