2018
Martínez,G. Adamatzky, A. Chen, B Chen F. Seck- J.
Abstract
Abstract We overview networks which characterise dynamics in cellular automata. These networks are derived from one-dimensional cellular automaton rules and global states of the automaton evolution: de Bruijn diagrams, subsystem diagrams,basins of attraction, and jump-graphs. These networks are used to understand properties of spatially-extended dynamical systems: emergence of non-trivial patterns, self-organisation, reversibility and chaos. Particular attention is paid to networksdetermined by travelling self-localisations, or gliders.
Unconventional invertible behaviors in reversible one-dimensional cellular automata.
Elementary cellular automaton Rule 110 explained as a block substitution system
Pair Diagram and Cyclic Properties Characterizing the Inverse of Reversible Automata
Modeling a Nonlinear Liquid Level System by Cellular Neural Networks
How to Make Dull Cellular Automata Complex by Adding Memory: Rule 126 Case Study
Reproducing the Cyclic Tag System Developed by Matthew Cook with Rule 110 Using the Phases f(i-)1.