现有文献中定义的大多数神经元细胞自动机（NCA）均基于一个共同范式：采用规则网格结构及摩尔邻域（一跳邻居），未考虑大脑中存在的长程连接与更复杂的拓扑结构。本文提出BraiNCA——一种受脑启发的NCA，其引入注意力层、显式长程连接以及复杂拓扑结构。实验表明，在两项任务中，BraiNCA相较于标准NCA（Vanilla NCA）展现出更优的鲁棒性与更快的学习速度，证实了将基于注意力的消息选择机制与显式长程边相结合，可实现比纯局部、网格化更新规则更具样本效率且对损伤更具容忍性的自组织能力。这些结果支持如下假设：对于需在广阔时空尺度上实现分布式协同的任务，交互拓扑结构的选择以及动态信息路由能力将显著影响NCA的鲁棒性与学习速度。更广泛而言，BraiNCA提供了一种受脑启发的NCA建模框架，在保持去中心化局部更新原则的同时，更真实地反映非局部连接模式，因而成为研究生物现实网络结构下集体计算及演化认知基质的有力工具。

Most of the Neural Cellular Automata (NCAs) defined in the literature have a common theme: they are based on regular grids with a Moore neighborhood (one-hop neighbour). They do not take into account long-range connections and more complex topologies as we can find in the brain. In this paper, we introduce BraiNCA, a brain-inspired NCA with an attention layer, long-range connections and complex topology. BraiNCAs shows better results in terms of robustness and speed of learning on the two tasks compared to Vanilla NCAs establishing that incorporating attention-based message selection together with explicit long-range edges can yield more sample-efficient and damage-tolerant self-organization than purely local, grid-based update rules. These results support the hypothesis that, for tasks requiring distributed coordination over extended spatial and temporal scales, the choice of interaction topology and the ability to dynamically route information will impact the robustness and speed of learning of an NCA. More broadly, BraiNCA provides brain-inspired NCA formulation that preserves the decentralized local update principle while better reflecting non-local connectivity patterns, making it a promising substrate for studying collective computation under biologically-realistic network structure and evolving cognitive substrates.