Abstract
Spike trains are difficult to analyze and compare because they are point processes, for which relatively few methods of time series analysis exist. Recently, several distance measures between pairs of spike train windows (segments) have been proposed. Such distance measures allow one to draw recurrence plots, two-dimensional graphs for visualizing dynamical changes of time series data, which in turn allows investigation of many spike train properties, such as serial dependence, chaos, and synchronization. Here, we review some definitions of distances between windows of spike trains, explain methods developed on recurrence plots, and illustrate how these plots reveal spike train properties by analysis of simulated and experimental data.
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Hirata, Y., Lang, E.J., Aihara, K. (2014). Recurrence Plots and the Analysis of Multiple Spike Trains. In: Kasabov, N. (eds) Springer Handbook of Bio-/Neuroinformatics. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30574-0_42
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DOI: https://doi.org/10.1007/978-3-642-30574-0_42
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