What to try
- Load a Blinker or Block, focus a cell and read its neighborhood.
- Step one generation, then toggle cells to change the initial arrangement.
What to look for: The local inspector applies B3/S23 to the previous generation. Small board dots preview which cells will be alive next; every cell updates together.
How it works
In a Life-like binary automaton, a cell’s next state depends on whether it is alive and how many nearby cells are alive. All cells should read the previous generation. Updating cells in place would let later decisions read already-changed neighbors and implement a different system.
The Lab can generate an initial seeded population or evolve an existing image after interpreting its values as cell states. Cell size determines the display footprint of a logical cell; generations determine how far the rule is applied.
Use it in the Lab
Use automata for evolving marks or patterned fields. Keep cell size and threshold in mind when translating an existing raster into states; the interpretation of that input is part of the recipe.
Limits & distinctions
The example and Lab use bounded simulations with explicit rules. A still frame does not establish long-term stability, and the generator is not a general scientific simulation environment.
Further reading
Rules of the Grid
Cellular automata consist of a grid of cells, each in one of a finite number of states. At each time step, every cell updates simultaneously based on a rule that considers the cell's current state and its immediate neighbors.
Game of Life Rules
Conway's Game of Life rules (B3/S23): • Birth: a dead cell with exactly 3 live neighbors becomes alive • Survival: a live cell with 2 or 3 live neighbors survives • Death: all other live cells die (overcrowding or isolation)
function step(grid: boolean[][]) {
const next = grid.map(row => [...row]);
for (let y = 0; y < H; y++)
for (let x = 0; x < W; x++) {
const n = countNeighbors(grid, x, y);
next[y][x] = n === 3 || (grid[y][x] && n === 2);
}
return next;
}Emergent Complexity
From these minimal rules emerge gliders (patterns that translate across the grid), oscillators (patterns that cycle through states), and even self-replicating structures. Conway's Game of Life is actually Turing-complete — capable of universal computation.
Beyond Life
Many rule variants exist beyond B3/S23. Wolfram's elementary automata are 1D systems with 256 possible rules — Rule 30 generates chaos from order, while Rule 110 is Turing-complete. In algorithmic art, custom rule sets are designed to produce specific visual textures and patterns.
Try modifying the birth/survival rules to discover new patterns. B36/S23 (HighLife) produces a self-replicating "replicator" pattern. B1357/S1357 creates beautiful organic growth.
Related artworks
Related Lab categories
Sources & references
Guide updated