Abstract Body

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.

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 find a functional hierarchy across the primate cortex and serves as a measure of active working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.

Given the relevance of intrinsic timescales for a variety of neuroscience questions and the ubiquitous subsampling problem, our toolbox should prove of great interest to the neuroscience community. Moreover, the estimator is very data efficient; it only requires a few minutes of recordings from tens of neurons for a reliable estimate.