No brain. It still solves the maze.
In 2000, Toshiyuki Nakagaki grew a mat of Physarum polycephalum across a plastic maze, then set an oat flake at each of two exits. The slime, spread thick through every corridor and dead end, began to withdraw.
Within hours it had abandoned the blind alleys and thinned to a single tube tracing the shortest route between the two foods — the same answer a shortest-path algorithm would return, computed by an organism with no nervous system, no memory cells, no idea it was in a maze at all.
It doesn't search. It floods everywhere at once, then lets the paths that don't pay off starve. Selection, not planning.
It redrew the Tokyo rail map.
In 2010, Atsushi Tero's team laid oat flakes across a scaled map of greater Tokyo — one flake for each surrounding city — put the slime on the spot marked Tokyo, and waited.
In about a day it grew a web of tubes linking every flake. Statistically, the result matched the real rail network engineers spent a century designing: comparable total length, comparable fault tolerance, comparable cost. Cut a tube and the flow re-routes.
The team distilled the behaviour into a rule for adaptive network design — tubes carrying more flow thicken, idle tubes wither — now borrowed for road, pipe and data networks.
Three sensors. One rule.
Every filament above runs the same tiny program each frame. There is no leader and no map — only local scent and a bias to follow it. Multiply by hundreds of thousands and the networks you scrolled past appear on their own. This is that rule, running live on your GPU.
Smell three ways
Each agent samples the trail map ahead-left, straight-ahead and ahead-right, at a set angle and distance.
Turn toward scent
If the centre reads strongest, hold course. Otherwise rotate toward whichever side smells richer — and if it's a tie, turn at random.
Leave a trail, decay
Move forward, drop a little chemical. Each frame the whole map blurs and fades — so only paths that stay busy survive.