MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

PLoS One. 2021 Apr 29;16(4):e0249447. doi: 10.1371/journal.pone.0249447. eCollection 2021.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Electrophysiology / methods*
  • Models, Neurological
  • Neurons / cytology*
  • Time Factors

Grants and funding

FPS and JD were funded by the Volkswagen Foundation through the SMARTSTART Joint Training Program Computational Neuroscience. JZ is supported by the Joachim Herz Stiftung. All authors acknowledge funding by the Max Planck Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.