论文标题
基于重新缩放方法的边界解决方案：恢复神经元网络动力学的一阶和二阶统计
Boundary solution based on rescaling method: recoup the first and second-order statistics of neuron network dynamics
论文作者
论文摘要
网络规模和可用的计算资源之间存在很强的联系，这可能会阻碍神经科学研究。同时，在保持网络行为的同时重新缩放网络并不是一个微不足道的任务。此外，在地形组织下建模的连接模式提出了一个额外的挑战：解决网络边界或与未悬而未决的行为混合。例如，由于圆环解决方案，这种行为可能是插图的振荡。或由于缺乏（或过量）连接而与/不平衡神经元的混合物。我们详细介绍了能够维持Romaro等人使用的行为统计的网络重新缩放方法。 （2018）并根据先前的统计循环想法提供边界解决方案方法。
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.
