Structural plasticity of the brain describes the creation of new and the deletion of old synapses over time. Rinke et al. (JPDC 2018) introduced a scalable algorithm that simulates structural plasticity for up to one billion neurons on current hardware using a variant of the Barnes-Hut algorithm. They demonstrate good scalability and prove a runtime complexity of $O(n \log^2 n)$. In this comment paper, we show that with careful consideration of the algorithm and a rigorous proof, the theoretical runtime can even be classified as $O(n \log n)$.

翻译：大脑结构的可塑性描述了新突触的创造和逐渐删除。 Rinke等人(JPDC 2018)引入了一种可缩放的算法,该算法使用Barnes-Hut算法的一种变体,模拟现有硬件上高达10亿神经元的结构可塑性。这些算法显示了良好的可缩放性,并证明运行时间的复杂性为$O(n\log2 n).在本评论文件中,我们表明,在仔细考虑算法和严格证据的情况下,理论运行时间甚至可以被归类为$O(n\log n) 。