用概率混合机制增强神经元胞自动机，模拟生物生长的随机性。
Mixtures of Neural Cellular Automata: A Stochastic Framework for Growth Modelling and Self-Organization
- 引入混合模型与内在噪声，让细胞自动机可随机选择规则。
- 在组织生长模拟中比传统方法更抗干扰、更贴近真实生物模式。
- 适合研究发育生物学、自组织系统或需要可解释规则的场景。
神经胞自动机（NCAs）是建模自组织过程的有前景方法，适用于生命科学领域。但其确定性特性限制了对真实生物与物理系统随机性的捕捉能力。本文提出混合神经胞自动机（MNCA），将混合模型思想融入NCA框架，通过概率化规则分配与内在噪声，实现多样局部行为建模，重现生物过程中观察到的随机动力学。我们在三个关键领域评估了MNCA的有效性：(1) 组织生长与分化合成模拟，(2) 图像形态发生鲁棒性，(3) 显微图像分割。结果表明，MNCA在扰动下具有更强鲁棒性，更准确复现真实生物生长模式，并提供可解释的规则分割。这些发现使MNCA成为建模随机动态系统和研究自生长过程的有力工具。
原文摘要 · Abstract (English)
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 segmentation. These findings position MNCAs as a promising tool for modeling stochastic dynamical systems and studying self-growth processes.
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