论文标题
模拟大脑的结构可塑性比预期的更可扩展
Simulating Structural Plasticity of the Brain more Scalable than Expected
论文作者
论文摘要
大脑的结构可塑性描述了随着时间的推移的创建和旧突触的删除。 Rinke等。 （JPDC 2018）推出了一种可扩展的算法，该算法使用Barnes-Hut算法的变体模拟了当前硬件上多达十亿个神经元的结构可塑性。他们表现出良好的可扩展性，并证明了$ O（n \ log^2 n）$的运行时复杂性。在这篇评论的论文中，我们表明，通过仔细考虑算法和严格的证明，理论运行时甚至可以归类为$ O（n \ log n）$。
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 $O(n \log n)$.
