Neural Cellular Automaton

Neural Cellular Automata (NCA) combine the principles of cellular automata with deep learning, aiming to create adaptable and computationally efficient models capable of simulating complex systems and performing various tasks. Current research focuses on developing NCA architectures for image processing (segmentation, restoration, classification), multi-agent systems (pathfinding, environment generation), and modeling biological processes (fungal growth, morphogenesis). The resulting models offer advantages in terms of robustness, generalization, and explainability, with applications ranging from medical image analysis in resource-constrained settings to the design of more efficient and adaptable robotic systems.