This simple spreading activation model also crashes up against modern neuroscience research, which mostly contradicts the idea of a "grandmother cell", ie a single neuron that represents a single concept like your grandmother.

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Association between one idea and another is not through physical contiguity, but through similarities in the pattern. "Grandmother" probably has most of the same neurons in the same state as "grandfather", and so it takes only a tiny stimulus to push the net from one attractor state to the other.

This is extremely unlikely. Associations can be made between concepts long after the patterns for those concepts have been learned. For a different explanation, see my 2000 article, A neuronal basis for the fan effect. It used the idea of convergence zones, promoted by Antonio Damasio (Damasio, A. R. (1990), Synchronous activation in multiple cortical regions: A mechanism for recall. The Neurosciences 2:287–296). My paper did this:

• Have binary-neuron network 1 represent one concept by a collection of activated nodes
• Have network 2 (or the same network in the next timestep) represent another concept the same way
• Have a third network (the convergence zone) learn associations between patterns in those two networks, using the Amari/Hopfield algorithm.

Then the settling of the neurons in the convergence zone into a low-energy state causes the presence of one pattern in network 1 to recall an associated pattern in network 2, with dynamics and error rates that closely mimic John Anderson's experiments on the quantitative measurement of spreading activation in humans.

(I was careful in my article to give credit to Amari, who invented the Hopfield network 10 years before Hopfield did. But I see now the editor "fixed" my reference to no longer give Amari priority.)