Cellular Automata From First Principles 40: Randomize the Update Schedule

Mon, 10 Aug 2026 20:36:00 +0100

Cellular Automata From First Principles 40: Randomize the Update Schedule

Most cellular automata in this book have used synchronous updates:

all cells observe state_t
all cells update
state_t+1 appears

Neural cellular automata become more robust when we stop assuming perfect global synchronization.

Stochastic firing

Instead of applying every predicted update, sample a binary mask:

def stochastic_update(x, dx, fire_rate=0.5):
    mask = (
        torch.rand(
            x.shape[0], 1, x.shape[2], x.shape[3],
            device=x.device,
        ) <= fire_rate
    )
    return x + dx * mask

Half the cells update on average.

Asynchronous Updates on Programmer.ie: Modern AI programming

Cellular Automata From First Principles 40: Randomize the Update Schedule

Cellular Automata From First Principles 40: Randomize the Update Schedule

Stochastic firing