Function Reference by Module

Raster and event plots of spike times

eventplot(spiketrains[, axes, ...])

Spike times eventplot with an additional histogram.

rasterplot(spiketrains[, axes, ...])

Simple and fast raster plot of spike times.

rasterplot_rates(spiketrains[, key_list, ...])

This function plots the dot display of spike trains alongside its population histogram and the mean firing rate (or a custom function).

Spike train statistics plots

plot_isi_histogram(spiketrains[, axes, ...])

Create a simple histogram plot to visualise an inter-spike interval (ISI) distribution of spike trains.

plot_time_histogram(histogram[, axes, units])

This function plots a time histogram, such as the result of elephant.statistics.time_histogram().

plot_instantaneous_rates_colormesh(rates[, ...])

Plots a colormesh of instantaneous firing rates.

Spike train correlation plots

plot_corrcoef(corrcoef_matrix[, axes, ...])

Plots a cross-correlation matrix returned by elephant.spike_train_correlation.correlation_coefficient() function with a color bar.

plot_cross_correlation_histogram(cch[, ...])

Plot a cross-correlation histogram returned by elephant.spike_train_correlation.cross_correlation_histogram(), rescaled to seconds.

Spike train synchrony plots

plot_spike_contrast(trace[, spiketrains, ...])

Plot Spike-contrast synchrony measure (Ciba et al., 2018).

Adding time events to axes plot

add_event(axes, event[, key, rotation, exclude])

Add event(s) to axes plot.

Spike patterns plots

Visualizes detected spike patterns returned by elephant.spade.spade() or elephant.cell_assembly_detection.cell_assembly_detection() functions.

plot_patterns(spiketrains, patterns[, ...])

Raster plot with one or more chosen SPADE or CAD patterns ot top shown in color.

Spike patterns statistics plots


Create a histogram plot to visualise all patterns statistics of SPADE analysis output.


Create a histogram of neural participation in patterns.


Create a histogram of pattern occurrences.

plot_patterns_statistics_size(patterns[, axes])

Create a histogram of pattern sizes.

plot_patterns_statistics_lags(patterns[, axes])

Create a histogram of pattern lags.

plot_patterns_hypergraph(patterns[, num_neurons])

Hypergraph visualization of spike patterns.

Gaussian Process Factor Analysis (GPFA) plots

Visualizes transformed trajectories output from elephant.gpfa.gpfa.GPFA

plot_dimensions_vs_time(returned_data, ...)

This function plots all latent space state dimensions versus time.

plot_trajectories(returned_data, gpfa_instance)

This function allows for 2D and 3D visualization of the latent space variables identified by the GPFA.

plot_trajectories_spikeplay(spiketrains, ...)

This function allows for 2D and 3D visualization of the latent space variables identified by the GPFA.


This function plots the cumulative shared covariance.

plot_transform_matrix(loading_matrix[, cmap])

This function visualizes the loading matrix as a heatmap.

Unitary Event Analysis (UEA) plots

Standard plot function for pairwise unitary event analysis results resembling the original publication. The output is assumed to be from elephant.unitary_event_analysis.jointJ_window_analysis() function.

plot_ue(spiketrains, Js_dict[, ...])

Plots the results of pairwise unitary event analysis as a column of six subplots, comprised of raster plot, peri-stimulus time histogram, coincident event plot, coincidence rate plot, significance plot and unitary event plot, respectively.

Analysis of Sequences of Synchronous EvenTs (ASSET) plots

Visualizes the output of elephant.asset.ASSET analysis.

plot_synchronous_events(spiketrains, sse[, ...])

Reorder and plot the spiketrains according to a series of synchronous events sse obtained with the ASSET analysis.


[Ciba, 2018] (1,2)

M. Ciba, T. Isomura, Y. Jimbo, A. Bahmer, and C. Thielemann. Spike-contrast: a novel time scale independent and multivariate measure of spike train synchrony. J. Neurosci. Meth., 293:136–143, 2018. doi:10.1016/j.jneumeth.2017.09.008.

[Yu, 2009]

Byron M Yu, John P Cunningham, Gopal Santhanam, Stephen I Ryu, Krishna V Shenoy, and Maneesh Sahani. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol., 102(1):614–635, jul 2009. doi:10.1152/jn.90941.2008.