  We propose a new non-equilibrium model for spatial pattern formation on the
basis of local information transfer. Unlike standard models of pattern
formation it is not based on the Turing instability. Information is transmitted
through the system via particle-like excitations whose collective dynamics
result in pattern formation and control. Here, a simple problem of domain
formation is addressed by this model in an implementation as stochastic
cellular automata. One observes stable pattern formation, even in the presence
of noise and cell flow. Noise stabilizes the system through the production of
quasi-particles that control the position of the domain boundary.
Self-organized boundaries become sharp for large system with fluctuations
vanishing with a power of the system size. Pattern proportions are
scale-independent with system size. Pattern formation is stable over large
parameter ranges with two phase transitions occurring at vanishing noise and
increased cell flow.
