论文标题
先生。估算器，一种工具箱，用于确定从子采样活动中确定内在的时间尺度
MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity
论文作者
论文摘要
在这里，我们介绍了我们的Python工具箱“估算器先生”，以可靠地估算大量下采样系统的电生理记录中的内在时间尺度。我们的工具箱最初是为了分析神经元尖峰活动的时间序列，适用于广泛的系统，在这些系统中，在该系统中进行了采样（难以详细观察整个系统的困难 - 限制了我们记录的能力。应用程序的范围从流行病扩散到可以自回归过程代表的任何系统。 在神经科学的背景下，可以将内在的时间尺度视为任何扰动在网络中回荡的持续时间。它已被用作可观察到的关键，以研究灵长类动物皮层的功能层次结构并用作工作记忆的衡量标准。它也是临界距离并量化系统动态工作点的代理。
Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling -- the difficulty to observe the whole system in full detail -- limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.
