Mixtures of Neural Cellular Automata: A Stochastic Frame- work for Growth Modelling and Self-Organization

TMLR Paper5093 Authors

Abstract:

Neural Cellular Automata (NCAs) are a promising new approach to model self-organizing
processes, with potential applications in life science. However, their deterministic nature limits
their ability to capture the stochasticity of real-world biological and physical systems.

We propose the Mixture of Neural Cellular Automata (MNCA), a novel framework incorporating
the idea of mixture models into the NCA paradigm. By combining probabilistic rule
assignments with intrinsic noise, MNCAs can model diverse local behaviors and reproduce
the stochastic dynamics observed in biological processes.

We evaluate the effectiveness of MNCAs in three key domains: (1) synthetic simulations of
tissue growth and differentiation, (2) image morphogenesis robustness, and (3) microscopy
image segmentation. Results show that MNCAs achieve superior robustness to perturbations,
better recapitulate real biological growth patterns, and provide interpretable rule segmenta-
tion.

These findings position MNCAs as a promising tool for modeling stochastic dynamical
systems and studying self-growth processes.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Ole_Winther1

Submission Number: 5093