  Reaction-Diffusion (RD) systems provide a computational framework that
governs many pattern formation processes in nature. Current RD system design
practices boil down to trial-and-error parameter search. We propose a
differentiable optimization method for learning the RD system parameters to
perform example-based texture synthesis on a 2D plane. We do this by
representing the RD system as a variant of Neural Cellular Automata and using
task-specific differentiable loss functions. RD systems generated by our method
exhibit robust, non-trivial 'life-like' behavior.
