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iSTTC: A robust method for accurate estimation of intrinsic neural timescales from single-unit recordings

Fig 3

iSTTC is a better IT estimator than ACF on unsegmented data, particularly for low-firing rate and bursty units.

(A) Schematic illustration of the synthetic dataset generation (left), and the underlying parameters with corresponding representative spike train examples (right). (B) Definition of the relative estimation error (REE) metric. (C) Hexbin plot displaying the difference in REE between iSTTC and ACF method as a function of firing rate and excitation strength (left), and IT and excitation strength (right) (n = single units). Color codes for the median REE difference in each bin, with blue indicating better IT estimation for iSTTC. (D) Line plot displaying predicted REE values for iSTTC and ACF as a function of firing rate (left), excitation strength (middle), and IT (right) (n = single units). Shaded areas represent 95% confidence intervals. Y-axes are plotted on a scale. In (C) and (D), ACF parameters were: bin size = 50 ms, number of lags = 20; iSTTC parameters were: lag shift = 50 ms, dt = 25 ms, number of lags = 20. In (C), asterisks indicate a significant effect of the IT estimation method. In (D), asterisks indicate a significant effect of an interaction between method and firing rate (left), method and excitation strength (middle), and method and IT (right). * , *** Generalized linear model with interactions (C), (D).

doi: https://doi.org/10.1371/journal.pcbi.1013385.g003