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

Fig 5

Relative estimation errors are higher for epoched than unsegmented spiking data.

(A) Comparison of IT estimation accuracy across four methods. Ridgeline plot displaying the distribution of REE. Only IT estimates with REE between 0% and 100% are shown. Percentages indicate the proportion of IT estimates within this interval for each method (left). Scatter plot displaying the percentage of IT estimates with REE falling within progressively narrower intervals (middle). Violin plot displaying the full distribution of REE values for each method. (right). (B) Line plot displaying predicted REE values for ACF and iSTTC as a function of signal length (n = single units per signal length). Shaded areas represent 95% confidence intervals. Y-axes are plotted on a scale. (C) Heatmap displaying the percentage of spike trains with REE within specific intervals for ACF (left) and iSTTC (middle) across varying signal lengths. Color codes for the proportion of spike trains, with warmer colors indicating higher percentages of spike trains. (Right) Heatmap displaying the difference in performance between methods, computed as the difference between ACF and iSTTC. Negative values indicate better performance (lower REE) for iSTTC. Color codes for the magnitude of the difference in percentages. (D) Same as (B) for PearsonR and iSTTC. (E) Same as (C) for PearsonR and iSTTC. In (A)–(D), ACF/PearsonR 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 (A) right, data is presented as median, 25th, 75th percentile, and interquartile range, with the shaded area representing the probability density distribution of the variable. In (B), asterisks indicate a significant effect of interaction between method and signal length. In (D), asterisks indicate a significant effect of an interaction between method and the number of trials. *** . Generalized linear model with interactions (B), (D).

doi: https://doi.org/10.1371/journal.pcbi.1013385.g005