给神经元胞自动机加个身份层，让人工生物长得更稳还可能动起来。
Identity Increases Stability in Neural Cellular Automata
- 引入简单约束的身份层提升生长稳定性
- 单个身份值即可显著改善结构完整性
- 多身份值催生自组织运动，适合研究细胞级交互
神经元胞自动机（NCAs）可从单个种子细胞模拟二维人工生物的生长。然而，传统NCAs常因边界崩溃、出现类肿瘤生长或无法保持预期形态而缺乏稳定性。本文提出通过在训练中引入带有简单约束的'身份'层，显著提升生长稳定性。实验表明，近距离生长的NCAs在新方法下表现更优；且仅需单一身份值即可实现稳定增长。观察到稳定生物体出现自发运动，多身份值模型中该现象更为普遍。本工作为研究人工生物间的相互作用奠定基础，推动细胞层面社会行为的探索。代码与视频见：https://github.com/jstovold/ALIFE2025
原文摘要 · Abstract (English)
Neural Cellular Automata (NCAs) offer a way to study the growth of two-dimensional artificial organisms from a single seed cell. From the outset, NCA-grown organisms have had issues with stability, their natural boundary often breaking down and exhibiting tumour-like growth or failing to maintain the expected shape. In this paper, we present a method for improving the stability of NCA-grown organisms by introducing an 'identity' layer with simple constraints during training. Results show that NCAs grown in close proximity are more stable compared with the original NCA model. Moreover, only a single identity value is required to achieve this increase in stability. We observe emergent movement from the stable organisms, with increasing prevalence for models with multiple identity values. This work lays the foundation for further study of the interaction between NCA-grown organisms, paving the way for studying social interaction at a cellular level in artificial organisms. Code/Videos available at: https://github.com/jstovold/ALIFE2025
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。