基于重标度法的边界解：恢复神经元网络动力学的一阶和二阶统计量
Boundary solution based on rescaling method: recoup the first and second-order statistics of neuron network dynamics
摘要：
网络的大小和可用的计算资源之间有很强的联系，这可能会阻碍神经科学研究。与此同时，在保持网络行为的同时重新调整网络规模并不是一项微不足道的任务。此外，拓扑组织下的连接模式建模提出了一个额外的挑战：解决网络边界或与不希望的行为混合。例如，这种行为可能是由于环面解决方案引起的插入振荡;或者由于缺乏（或过量）连接而与不平衡神经元混合。我们详细介绍了Romaro等人（2018）中使用的能够维持行为统计的网络重新缩放方法，并提出了一种基于先前统计补偿思想的边界解决方法。
英文摘要：
There is a strong nexus between the network size and the computational resources available, which may impede a neuroscience study. In the meantime, rescaling the network while maintaining its behavior is not a trivial mission. Additionally, modeling patterns of connections under topographic organization presents an extra challenge: to solve the network boundaries or mingled with an unwished behavior. This behavior, for example, could be an inset oscillation due to the torus solution; or a blend with/of unbalanced neurons due to a lack (or overdose) of connections. We detail the network rescaling method able to sustain behavior statistical utilized in Romaro et al. (2018) and present a boundary solution method based on the previous statistics recoup idea.
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