Abstract
We explore emergent properties of Isotropic Neural Cellular Automata models (IsoNCA) trained on two simple problems, and evaluate them on static image outputs. The first task involves pixel-perfect reconstruction of fixed target images starting from a fixed initial state. The other task requires NCA to synthesize a pattern that, when fed into a separate Imagenet-pretrained convolutional network, triggers a strong response in the feature channels. In order to satisfy the given objectives and model constraints, optimization arrives at solutions that display intriguing life-like dynamics and mimic well known phenomena, such as swarming of Boids, Voronoi cell formation and network-like structures. Finally we discuss the multi-scale hierarchical nature of the solutions that arise in NCA.