MR。Estimate，一个工具箱，用于从子采样尖峰活动确定内在时间尺度
MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity
摘要：
在这里，我们介绍了我们的Python工具箱“MR. Estimate”，以可靠地估计来自重采样系统的电生理记录的内在时间尺度。我们的工具箱最初用于分析神经元尖峰活动的时间序列，可应用于各种系统，其中子采样-难以观察整个系统的全部细节-限制了我们的记录能力。应用范围从流行病传播到任何可以用自回归过程表示的系统。在神经科学的背景下，内在时间尺度可以被认为是任何扰动在网络内回响的持续时间;它被用作研究灵长类动物皮层功能层次的关键观察指标，并作为工作记忆的测量指标。它也是临界距离的代表，并量化了系统的动态工作点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
