In my PhD, I am working on Neural Cellular Automata (NCA) in medical imaging, i.e. neural adaptation of Cellular Automata such as ‘Game of Life’, which has been shown to be Turing-complete. NCA is a recently emerging field of machine learning, where a model is only one cell in size and can solely communicate with its immediate neighbours. Global knowledge is gained by iteratively applying this model to each cell, e.g. of an image. In this sense, NCAs differ from typical deep learning models, have been shown to be robust in image generation tasks, and generally have fewer than 100k parameters. Despite their small size, they can achieve similar performance to models thousands of times larger in tasks such as segmentation.