生长神经细胞自动机发育动力学中的瞬态重组与细胞分化
Transient State Reorganization and Cell Differentiation in the Developmental Dynamics of Growing Neural Cellular Automata
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中文总结 AI 辅助
研究生长神经细胞自动机（GNCA）的发育，通过追踪其完整发育轨迹，采用通道分析、几何分析及社区检测等方法，揭示其发育过程是瞬态状态的重组，能提取离散细胞类型，识别不同阶段细胞群落。
中文摘要 AI 辅助
生长神经细胞自动机（GNCA）通过共享局部规则从单个种子细胞发展出复杂形态，但其内部动力学仍知之甚少。为研究GNCA的生长过程，追踪了训练后GNCA模型的完整发育轨迹。细胞状态发展轨迹显示形态收敛常通过瞬态中间构型非单调进行。通道分析表明隐藏通道与可见形式并行自组织成模块组。细胞状态空间几何分析表明细胞状态在低维平滑流形内多样化。通过对细胞的ε邻域网络进行社区检测，成功从连续空间中提取离散细胞类型，识别出早期发育中的瞬态细胞类型群落以及成熟形态中对应空间连贯区域的稳定、细粒度类型。这些现象在多个独立测量中的时间协调表明，GNCA的发育过程是瞬态状态的重组而非渐进细化。
英文摘要
Growing Neural Cellular Automata (GNCA) develop complex morphologies from a single seed cell through shared local rules, yet the internal dynamics of this process remain poorly understood. To investigate how GNCA grows, the full developmental trajectory of trained GNCA models was traced. The trajectory of cell state development revealed that morphological convergence often proceeds non-monotonically through transient intermediate configurations. In addition, channel-wise analysis showed that the hidden channels self-organize into modular groups in parallel with the visible form. Furthermore, geometric analysis of the cell state space indicated that cell states diversify within a low-dimensional, smooth manifold. To examine cell development in more detail, community detection on an $ε$-neighbour network of cells was conducted. This analysis successfully extracted discrete cell types from this continuous space, and identified transient cell-type communities during early development and stable, finer-grained types corresponding to spatially coherent regions of the mature morphology. The temporal coordination of these phenomena across multiple independent measures indicates that the developmental process of GNCA is a reorganization of transient states rather than incremental refinement.
