发表机构
Østfold University of Applied Sciences; Oslo Metropolitan University（东福尔应用科学大学; 奥斯陆都会大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过自复制神经细胞自动机模拟开放底物，提出粗粒度多样性度量，揭示表型与基因型多样性之间的权衡，并验证系统的持续性与谱系结构。补充材料已公开。
AI 中文摘要
我们研究了一种计算机模拟底物，其中二维通道细胞自动机网格的每个像素都携带一个微型神经网络（智能体），该网络感知其摩尔邻域。细胞仅通过自复制得以存续：一个活邻居被克隆，其权重受到均匀扰动的突变，从而表型（细胞状态）完全由基因型（网络权重）驱动。从少数种子创始者出发，系统生长为一个空间组织的生态系统，其中包含共存、竞争和主导的物种。我们的主要贡献是一组粗粒度多样性度量，使这种增长在两个尺度上可测量：四种基于细胞类型频率、熵和细胞方差的表型工具，以及两种基因型工具，通过全权重向量的哈希与稀疏随机权重探针的对比为每个智能体着色。在五次重复扫描的1680次小规模运行和24次长时间（1000代，200×200）运行中，底物在24个长时间配置中的20个中表现出持续性和自我维持性，并揭示了清晰的表型-基因型多样性权衡：提高表型多样性会降低基因型多样性，反之亦然。全基因组哈希着色进一步揭示了随机权重探针系统性遗漏的谱系结构。代码、数据和动画作为补充材料发布。
英文摘要
We study an in-silico substrate in which every pixel of a two-channel cellular-automata grid carries a tiny neural network (an agent) that senses its Moore neighborhood. A cell persists only by self-replication: a living neighbor is cloned and its weights are mutated by a uniform perturbation, so that phenotype (cell state) is driven entirely by genotype (network weights). From a handful of seeded founders the system grows into a spatially organized ecosystem of coexisting, competing and dominating species. Our main contribution is a battery of coarse-grained diversity metrics that make such growth measurable at two scales: four phenotypic tools based on cellular-type frequency, entropy and cell variance, and two genotypic tools that colour each agent by a hash of its full weight vector versus a sparse random-weight probe. Across a five-fold sweep of 1680 small runs and 24 long (1000-generation, 200 x 200) runs, the substrate is persistent and self-maintaining in 20 of the 24 long configurations and exposes a clear phenotype-genotype diversity trade-off: raising phenotypic diversity collapses genotypic diversity and vice versa. Full-genome hash colouring further reveals lineage structure that a random-weight probe systematically misses. Code, data and animations are released as supplementary material.
