This project delves into the fascinating world of Neural Cellular Automata (NCA), a class of models that learn to produce complex, dynamic patterns through local interactions. As part of my work with Monash DeepNeuron's advanced research division, I've been exploring how these systems, inspired by biological processes like morphogenesis, can model embryo development and generate intricate, life-like behaviors.
Trained various NCA models to generate diverse, complex, and aesthetically pleasing patterns and textures that emerge from simple local rules and interactions.
Explored emergent behavior where simple local cellular rules give rise to complex global order, mimicking biological morphogenesis and development processes.
Investigated applications for procedural content generation in games and simulations, creating dynamic environments and unique visual effects through cellular evolution.
Built a real-time web-based visualizer using WebGL to observe the growth, evolution, and regenerative capabilities of cellular automata systems.