Plugins

This section describes the configuration options of the plugins that are included with Spyke Viewer. All included plugins create plots. For information on how to create your own plugins, see Analysis plugins.

Signal Plot

Shows the selected analog signals. A number of options enable to include additional information in the plot.

_images/plugin-signals.png
Use Subplots
Determines whether multiple subplots are used or all signals are shown in one large plot.
Show subplot names
Only valid when subplots are used. Determines if each subplot has a title with the signal name (if available) or the recording channel name.
Included signals
This option can be used to tune which type of signals are shown: AnalogSignal objects, AnalogSignalArray objects or both. In most cases, a file will only include one of the signal types, so the default option of including both will work well (you probably never need to change it if you do not know the difference between the signal objects).
Show events
When this is checked, events in the selected trial will be shown in the plot.
Show epochs
When this is checked, periods in the selected trial will be shown in the plot.
One plot per segment
When this is not checked, only one plot with signals from the first selected segment is created. Otherwise, one plot for each selected segment is created.
Show spikes

Determines whether spikes are included in the plot. The following options are used to select from what data how the spikes are displayed:

Display as
Spikes can be shown as their waveform overlaid on the analog signal or a vertical line marking their occurrence.
Included data
Determines whether to include spikes from SpikeTrain objects, Spike objects, or both.
Use first spike as template
This option can be used for a special case: All spikes in the SpikeTrain objects have the same waveform (e.g. because they use the same template from spike sorting). If this option is checked, the plugin assumes that each unit has a SpikeTrain and a single Spike. The waveform from the Spike object is used for every spike in the SpikeTrain. The data in the example file is structured in this way.

Spectrogram

Shows spectrograms of the selected analog signals.

_images/plugin-spectrogram.png
Interpolate
Determines whether the dipslayed spectrogram is interpolated.
Show color bar
If this is checked, a colorbar will be shown with each plot, illustrating the logarithmic power represented by the colors.
FFT samples
The number of signal samples used in each FFT window.
Included signals
This option can be used to tune which type of signals are shown: AnalogSignal objects, AnalogSignalArray objects or both. In most cases, a file will only include one of the signal types, so the default option of including both will work well (you probably never need to change it if you do not know the difference between the signal objects).

Spike Waveform Plot

Shows waveforms of selected spikes.

_images/plugin-waveforms.png
Antialiased lines
Determines if antialiasing (smoothing) is used for the plot. If you want to display thousands of spikes or more, unchecking this option will improve the plotting performance considerably.
Included spikes
Determines whether to include spikes from SpikeTrain objects, Spike objects, or both.
Plot type
Three different plot types can be selected: “One plot per channel” creates a subplot for each channel, “One plot per unit” creates a subplot for each unit and “Single plot” creates one plot containing all channels and units.
Split channels
Multichannel waveforms can be split either horizontally or vertically.

Correlogram

Creates auto- and crosscorrelograms for selected spike trains.

_images/plugin-correlogram.png
Bin size (ms)
The bin size used in the calculation of the correlograms.
Cut off (ms)
The maximum time lag for which the correlogram will be calculated and displayed.
Data source

The plugin supports two ways of organizing the data from which the correlograms are created: If “Units” is selected, the spike trains for each currently selected unit are treated as a dataset. For example, if two units are selected, the plugin creates three subplots: one autocorrelogram for each unit and a cross-correlogram between them.

If “Selections” are chosen, spike trains from each saved selection are treated as a dataset. Note that the plot can only be created if all selections contain the same number of spike trains.

Border correction
Determines if an automatic correction for less data at higher timelags is applied.

Interspike Interval Histogram

Creates an interspike interval histogram for one or more units.

_images/plugin-isi.png
Bin size (ms)
The bin size used in the calculation of the histogram.
Cut off (ms)
The maximum interspike interval that is displayed.
Type
Determines the type of histogram. If “Bar” is selected, only the histogram for the first selected unit is displayed. If “Line” is selected, all selected units are included in the plot.

Peristimulus Time Histogram

Creates a peristimulus time histogram (PSTH) for one or multiple units.

_images/plugin-psth.png
Bin size (ms)
The bin size used in the calculation of the histogram.
Start time (ms)
An offset from the alignment event or start of the spike train. Calculation of the PSTH begins at this offset. Negative values are allowed (this can be useful when using an alignment event).
Stop time
A fixed stop time for calculation of the PSTH. If this is not activated, the smallest stop time of all included spike trains is used. If the smallest stop time is smaller than the value entered here, it will be used instead.
Alignment event
An event (identified by label) on which all spike trains are aligned before the PSTH is calculated. After alignment, the event is a time 0 in the plot. The event has to be present in all selected segments that include spike trains for the PSTH.
Type
Determines the type of histogram. If “Bar” is selected, only the histogram for the first selected unit is displayed. If “Line” is selected, all selected units are included in the plot.

Raster Plot

Creates a raster plot from multiple spiketrains.

_images/plugin-rasterplot.png
Domain
The raster plot can either be created from multiple units and one segment (“Units”) or one unit over multiple segments (“Segments”).
Show lines
Determines if a small horizontal black line is displayed for each spike train.
Show events
When this is checked, events in the selected trial will be shown in the plot. If the selected domain is “Segments”, events from all selected segments are included.
Show epochs
When this is checked, periods in the selected trial will be shown in the plot. If the selected domain is “Segments”, epochs from all selected segments are included.

Spike Density Estimation

Creates a spike density estimation (SDE) for one or multiple units. Optionally computes the best kernel width for each unit.

_images/plugin-sde.png
Kernel size (ms)
The width of the kernel used for the plot. If kernel width optimization is enabled, this parameter is not used.
Start time (ms)
An offset from the alignment event or start of the spike train. Calculation of the SDE begins at this offset. Negative values are allowed (this can be useful when using an alignment event).
Stop time
A fixed stop time for calculation of the SDE. If this is not activated, the smallest stop time of all included spike trains is used. If the smallest stop time is smaller than the value entered here, it will be used instead.
Alignment event
An event (identified by label) on which all spike trains are aligned before the SDE is calculated. After alignment, the event is a time 0 in the plot. The event has to be present in all selected segments that include spike trains for the SDE.
Kernel width optimization

When this option is enabled, the best kernel width for each unit is determined using the algorithm from [1].

Minimum kernel size (ms)
The minimum kernel width that the algorithm should try.
Maximum kernel size (ms)
The maximum kernel width that the algorithm should try.
Kernel size steps
The number of steps from minimum to maximum kernel size that the algorithm should try. The steps are distributed equidistant on a logarithmic scale.
[1]Shimazaki, Shinomoto. (2010). Kernel bandwidth optimization in spike rate estimation. Journal of Computational Neuroscience, 29, 171-182.

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