The Wiring Rule
Donald Hebb never said “neurons that fire together, wire together.”
That catchy summary came from neuroscientist Carla Shatz in 1992, four decades after Hebb’s 1949 book The Organization of Behavior. What Hebb proposed was subtler and more radical: when two neurons repeatedly activate at the same time, the physical connection between them strengthens. Not the signal — the synapse itself. The hardware rewires.
This matters because it means experience isn’t just recorded — it’s built. London taxi drivers who spend years memorising the city’s 25,000 streets develop measurably larger posterior hippocampi than bus drivers who follow fixed routes. Same starting equipment, different wiring — shaped entirely by what got used.
The principle scales beyond biology. Machine learning algorithms descended directly from Hebb’s rule: artificial neural networks strengthen connections between nodes that activate together during training, gradually encoding patterns into structure. The algorithm doesn’t store a memory of a cat — it reshapes itself until recognising cats is what it does.
This is Hebb’s real insight. Learning isn’t filing information away. It’s becoming a different system — one where the thing you’ve practised is now the path of least resistance. Every repetition deepens the groove. Which is why habits feel automatic and first impressions prove so durable: the wiring was laid before you decided whether to keep it.