This work simulates the developmental process of cortical neurogenesis, initiating from a single stem cell and governed by gene regulatory rules derived from mouse single-cell transcriptomic data. The developmental process spontaneously generates a heterogeneous population of 5,000 cells, yet yields only 85 mature neurons - merely 1.7% of the total population. These 85 neurons form a densely interconnected core of 200,400 synapses, corresponding to an average degree of 4,715 per neuron. At iteration zero, this minimal circuit performs at chance level on MNIST. However, after a single epoch of standard training, accuracy surges to over 90% - a gain exceeding 80 percentage points - with typical runs falling in the 89-94% range depending on developmental stochasticity. The identical circuit, without any architectural modification or data augmentation, achieves 40.53% on CIFAR-10 after one epoch. These findings demonstrate that developmental rules sculpt a domain-general topological substrate exceptionally amenable to rapid learning, suggesting that biological developmental processes inherently encode powerful structural priors for efficient computation.

翻译：本研究模拟了皮层神经发生的发育过程，从单个干细胞起始，受源自小鼠单细胞转录组数据的基因调控规则驱动。发育过程自发产生了包含5000个细胞的异质群体，但仅形成85个成熟神经元——仅占总数的1.7%。这85个神经元形成了密集互连的核心结构，包含200,400个突触，对应每个神经元平均连接度为4,715。在零迭代时，该最小回路在MNIST上的表现仅为随机水平。然而，经过单轮标准训练后，准确率骤升至90%以上——提升超过80个百分点——典型运行结果根据发育随机性落在89-94%区间内。相同回路在无任何架构修改或数据增强的情况下，经单轮CIFAR-10训练后达到40.53%的准确率。这些发现表明，发育规则塑造了一种高度适应快速学习的域通用拓扑基底，提示生物发育过程天然编码了高效计算的强大结构先验。