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Computer SystemsReproduction #1

Models of Computation via Cellular Automata

Fundamental Computer Systems · 2024

This work reproduces classic cellular automata — discrete models of computation known for their ability to imitate complex biological processes through simple, local rules.

Python1D & 2D Environments
1D / 2DDimensional supportElementary & Grid based
Rule 110Turing CompleteComplex universal logic
ChaosRule 30 & LifeBiological imitation
Theory

What are Cellular Automata?

🧬

Biological Imitation

Known for their ability to imitate complex biological processes starting from very simple local rules.

💾

Discrete Models

Discrete models of computation where the state of a cell is determined by its neighborhood in the previous step.

📈

Computational Logic

Includes examples like Wolfram’s Rule 110, which is Turing complete, proving simple rules can perform any possible computation.

Rules

Implemented Paradigms

Rule 301D Sequence

A chaotic elementary rule proposed by Wolfram. Famous for its complex, non-periodic behavior from a single center point.

Rule 1101D Sequence

Turing complete rule that exists on the boundary between stability and chaos. Capable of universal computation.

Game of Life2D Grid

Conway's classic model. Imitates population dynamics with birth, death, and survival rules.

Demos

Visual Evolution

2D Chaotic Game of Life

Game of Life Demo

1D Rule 30 History

Rule 30 History
Usage

Python API

Install
git clone git@github.com:MartinBraquet/cellular-automata.git
pip install -e "."
Requires Python
Execution
from cellular_automata import cellular_automaton, game_of_life

cellular_automaton(
  n_grid=60,
  rule=game_of_life,
  initial_setup='chaos'
)
Configurable frames, grid size, and interval