Marker Plots (Scatter, Raster, etc.) of CBGTC spike times¶

Marker Plotting¶

Functions	Purpose
`plot_raster()`	plots Coefficient of Variations of all the neurons in a population
`plot_ratechange()`	draws the Coefficient of Variations of all the neurons into a given matplotlib.pyplot.axis
`plot_ratechange_in_ax()`	plots Local Coefficient of Variations of all the neurons in a population

Raster Plot of Spike Times¶

1. Pre-requisites¶

1.1. Import Modules¶

from analyseur.cbgtc.loader import LoadSpikeTimes
from analyseur.cbgtc.visual.markerplot import plot_raster

1.2. Load file and get spike times¶

loadST = LoadSpikeTimes("spikes_GPi.csv")
spiketimes_superset = loadST.get_spiketimes_superset()

2. Cases¶

2.1. Raster for all the neurons¶

plot_raster(spiketimes_superset)

2.2. Raster for first 50 neurons¶

plot_raster(spiketimes_superset, neurons=range(50))

2.3. Raster for second 50 neurons¶

plot_raster(spiketimes_superset, neurons=range(50, 100))

2.4. Create the plot for customization¶

This is for power users who for instance want to insert the raster plot in their collage of subplots.

import matplotlib.pyplot as plt
from analyseur.cbgtc.visual.markerplot import plot_raster_in_ax

fig, (ax1, ax2) = plt.subplots(1, 2)
fig.suptitle('Horizontally stacked subplots')

ax1 = plot_raster_in_ax(ax1, spiketimes_superset)
ax2 = plot_raster_in_ax(ax2, spiketimes_superset)

plt.show()

NOTE: This example shows plot_raster_in_ax() in default setting but this function works like plot_raster() therefore all the cases 2.1, 2.2 and 2.3 are applicable for plot_raster_in_ax().

Plot Rate Change Scatter¶

1. Pre-requisites¶

1.1. Import Modules¶

from analyseur.cbgtc.loader import LoadSpikeTimes
from analyseur.cbgtc.visual.markerplot import plot_ratechange

1.2. Load file and get spike times¶

loadST = LoadSpikeTimes("spikes_GPi.csv")
spiketimes_superset = loadST.get_spiketimes_superset()

2. Cases¶

2.1. Plot Rate Change Scatter for all the neurons¶

plot_ratechange(spiketimes_superset)

analyseur.cbgtc.visual.markerplot.plot_raster(spiketimes_superset, colors=False, neurons=None, nucleus=None, alpha=True)[source]¶

Visualize Raster plot for the given neuron population using plot_raster_in_ax().

Parameters:: spiketimes_superset – Dictionary returned using LoadSpikeTimes

OPTIONAL parameters

Parameters:

colors – False [default] or True
neurons – “all” [default] or range(a, b) or list of neuron ids like `[2, 3, 6, 7]
nucleus – string; name of the nucleus
alpha – True [default]

Returns:

object ax with Raster plotting done into it

analyseur.cbgtc.visual.markerplot.plot_raster_in_ax(ax, spiketimes_superset, window=None, colors=False, neurons=None, nucleus=None, alpha=True)[source]¶

Raster representation:

neuron ↑
n3 | . .   .    . .
n2 |   . .     .
n1 | .    .   .   .

    └──────────── time →

Draws the Rasterplot (matplotlib.pyplot.eventplot) on the given matplotlib.pyplot.axis

Parameters:

ax – object matplotlib.pyplot.axis`
spiketimes_superset – Dictionary returned using LoadSpikeTimes

OPTIONAL parameters

Parameters:

window – Tuple in the form (start_time, end_time); (0, 10) [default]
colors – False [default] or True
neurons – “all” [default] or range(a, b) or list of neuron ids like `[2, 3, 6, 7]
nucleus – string; name of the nucleus
alpha – True [default]

Returns:

object ax with Raster plotting done into it

analyseur.cbgtc.visual.markerplot.plot_ratechange(spiketimes_superset, stimulus_onset=None, window=None, neurons=None, nucleus=None, mode=None, alpha=True)[source]¶

Visualize Rate Change Scatter of the given neuron population using plot_ratechange_in_ax().

Parameters:: spiketimes_superset – Dictionary returned using LoadSpikeTimes

OPTIONAL parameters

Parameters:

stimulus_onset – float
window – 2-tuple; defines upper and lower range of the bins
neurons – “all” or list: range(a, b) or [1, 4, 5, 9]
nucleus – string; name of the nucleus
mode – “portrait” or None/landscape [default]
alpha – True [default]

Returns:

object ax with Rate Distribution plotting done into it

analyseur.cbgtc.visual.markerplot.plot_ratechange_in_ax(ax, spiketimes_superset, stimulus_onset=None, window=None, neurons=None, nucleus=None, mode=None, alpha=True)[source]¶

Each point represents one neuron.

y-axis : response firing rate (Hz)
x-axis : baseline firing rate (Hz)

Points above the diagonal → increased firing after stimulus
Points below the diagonal → decreased firing after stimulus

 response ↑
          |
     ↑    |        *
 increase |      *      *
          |    *
          |  *
----------+----------------------→ baseline
          |        *
 decrease |            *
     ↓    |   *
          |
          +----------------------

          dashed line = no change
          (response rate = baseline rate)

Draws the Population Rate Change Scatter on the given matplotlib.pyplot.axis

Parameters:

ax – object matplotlib.pyplot.axis`
spiketimes_superset – Dictionary returned using analyseur.cbgtc.stats.isi.InterSpikeInterval.compute()

OPTIONAL parameters

Parameters:

stimulus_onset – float; 0 [default]
window – 2-tuple; (0, 10) [default]
neurons – “all” [default] or list: range(a, b) or [1, 4, 5, 9]
nucleus – string; name of the nucleus
mode – “portrait” or None/landscape [default]
alpha – True [default]

Returns:

object ax with Rate Distribution plotting done into it