>> To train the system with a minimal amount of computing and memory, the researchers actually built two identical networks on top of each other. In the “clamped” network, they fed in the input voltages and fixed the output voltage to the value they wanted. In the “free” network, they fixed just the input voltage and then let all the other voltages float to whatever value they would, which generally gave the wrong voltage at the output.

>> The system then adjusted resistances in the two networks according to a simple rule that depended on whether the voltage difference across a resistor in the clamped network was bigger or smaller than the voltage difference across the corresponding resistor in the free network. After several iterations, those adjustments brought all voltages at all the nodes in the two networks into agreement and trained both networks to give the right output for a given input.

How is this not just a perceptron? I'm also curious about the rule for adjusting resistances in both networks.