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Cellular Automata (CA) have long been foundational in simulating dynamicalsystems computationally. With recent innovations, this model class has beenbrought into the realm of deep learning by parameterizing the CA's update ruleusing an artificial neural network, termed Neural Cellular Automata (NCA). Thisallows NCAs to be trained via gradient descent, enabling them to evolve intospecific shapes, generate textures, and mimic behaviors such as swarming.However, a limitation of traditional NCAs is their inability to exhibitsufficiently complex behaviors, restricting their potential in creative andmodeling tasks. Our research explores enhancing the NCA framework byincorporating multiple neighborhoods and introducing structured noise for seedstates. This approach is inspired by techniques that have historicallyamplified the expressiveness of classical continuous CA. All code and examplevideos are publicly available on https://github.com/MagnusPetersen/MNNCA.

Exploring Multiple Neighborhood Neural Cellular Automata (MNNCA) for Enhanced Texture Learning