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. 2018 Oct 5;8:14851. doi: 10.1038/s41598-018-33064-z

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Dependencies between sequential ISIs. (A) Box plot of SRC for consecutive ISI pairs (SRC(1)) for original unsorted data sets and shuffled data set. (B) Visualizations of the renewal quartile matrix q_ij showing frequencies for an ISI at a given quartile (horizontal axis) to be followed by an ISI at another quartile (vertical axis). Three qualitatively different mappings can be seen for data sets with positive correlation, zero correlation and negative correlations. (C) Boxplot of entries of renewal quartile matrices across all data sets. Compared to an equal likelihood for any ISI to fall in any quartile (q_ij = 1/16 shown as dashed line), we saw overall increases in q₁₁ (short followed by short) and q₄₄ (long-long), while q₁₄ (short-long) and q₄₁ (long-short) were reduced. (D) Groupings of long and short ISIs that occur consecutively in sequence over time. (E) Fraction of ISIs that are found within long and short sequences for data sets with negative, zero (uncorrelated), and positive SRC(1) (each dot represent a dataset).