Slime mold growth simulator

Watch a virtual Physarum polycephalum explore, find food, and build its network in this interactive growth simulator.

Click the canvas to place food, then start the simulation.
Blob (plasmodium) Food Repellent (salt) Reinforced tubes

Blob status

Time elapsed0 min
Particles0
Food found0 / 0
Active tubes0

How it works

  1. The blob explores in all directions from its starting point.
  2. When a filament reaches food, that path is reinforced.
  3. Filaments that reach no food retract over time.
  4. The result is an efficient network connecting all food sources.

Preset scenarios

What this simulator represents

This simulator is a simplified model of the Physarum Solver algorithm. Real slime mold works through cytoplasmic oscillations and conductance feedback: tubes that carry more cytoplasm grow wider, which in turn carries even more cytoplasm, creating a positive reinforcement loop toward food sources.

The algorithm here reproduces this core principle: random exploration, reinforcement of productive paths, retraction of unproductive ones. The visual result is close to what researchers observe in laboratory conditions.

Learn more about the Physarum Solver and AI

What this simulation actually models

The model on this page does not imitate what slime mold looks like. It imitates the rule it runs on. The network you watch forming comes from three mechanisms only, the same ones at work in Physarum polycephalum.

  1. Undirected exploration. At first the organism spreads in every direction at once, with no preference, like a front advancing at random.
  2. Reinforcement of useful paths. As soon as a branch reaches food, the flow through it increases, it thickens, it offers less resistance and it captures still more flow. The loop feeds itself.
  3. Decay of everything else. A branch leading nowhere carries nothing, thins out and eventually disappears, freeing material for the rest.

No step involves a decision or a global view. The final structure emerges from three local rules, which is exactly the point made in distributed intelligence.

What to watch for

The search phase takes the longest. Until a food source is reached, nothing organises itself: the network stays dense and directionless. Real cultures behave the same way, which is why food should be placed a centimetre or two away rather than directly on the organism.

The final network is never the shortest one. It almost always keeps a few redundant links. That is not a flaw in the model: it is the same trade-off between cost, efficiency and tolerance to breakage that the real organism produces, and the reason engineers took an interest after the Tokyo experiment described in the maze experiment.

Two identical runs give two different networks. Restart with the same food sources: the reinforcement loop amplifies tiny initial differences and the resulting structure changes. Real slime mold does the same, which is why an experiment on a single specimen proves nothing.

Where it departs from the real thing

The model deliberately leaves out several factors that matter a great deal in practice: the cytoplasmic oscillations whose 100 to 120 second cycle drives all transport, the slime trail left on already-explored ground, the avoidance of light, and sensitivity to temperature and humidity.

There is no substitute for a real culture if you want to observe those. Protocols are in experiments to run at home, equipment in growing slime mold, and the mechanics of movement in how slime mold moves.