Distribution Plotting of CBGTC spike times¶

Distribution Plotting¶

Functions
`plot_ratedist()`
`plot_ratedist_in_ax()`
`plot_latencydist()`
`plot_latencydist_in_ax()`
`plot_spike_counts_distrib()`
`plot_spike_counts_distrib_in_ax()`
`plot_spike_density_distrib()`
`plot_spike_density_distrib_line_in_ax()`
`plot_spike_density_distrib_stacked_in_ax()`
`plot_isi_distrib()`

1. Pre-requisites¶

1.1. Import Modules¶

from analyseur.cbgtc.loader import LoadSpikeTimes
from analyseur.cbgtc.visual.distribution import <desired_method>

1.2. Load file and get spike times¶

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

2. Cases¶

2.1. Standard plot¶

<desired_method>(spiketimes_set)

2.2. Create the plot for customization¶

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

import matplotlib.pyplot as plt
from analyseur.cbgtc.visual.distribution import plot_ratedist_in_ax

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

ax1 = plot_ratedist_in_ax(ax1, spiketimes_set)
ax2 = plot_ratedist_in_ax(ax2, spiketimes_set)

plt.show()

analyseur.cbgtc.visual.distribution.plot_isi_distrib(spiketimes_set, n_bins=50)[source]¶

Interspike Interval Distributions

┌─────────────────────────────────────┐   ┌─────────────────────────────────────┐
│ Neuron ISI Distributions            │   │ Population ISI Distributions        │
│                                     │   │                                     │
│ Density                             │   │ Density                             │
│ ^                                   │   │ ^                                   │
│ |      ████                         │   │ |       ████                        │
│ |    ████████                       │   │ |     ████████                      │
│ |   ███████████                     │   │ |   ███████████                     │
│ |    █████████                      │   │ |     █████████                     │
│ |      █████                        │   │ |       █████                       │
│ |                                   │   │ |                                   │
│ +---------------------------------> │   │ +---------------------------------> │
│     Interspike Interval (s)         │   │     Interspike Interval (s)         │
└─────────────────────────────────────┘   └─────────────────────────────────────┘

Left: overlapping ISI histograms for individual neurons.
Right: histogram of ISIs pooled across the entire population.

Visualize Distribution of Inter-Spike Interval of the given neuron population.

Parameters:: spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param n_bins:

50 [default]
return:

object ax with ISI Distribution plotting done into it

analyseur.cbgtc.visual.distribution.plot_latencydist(spiketimes_set, stimulus_onset=None, binsz=None, window=None, neurons=None, nucleus=None, orient=None)[source]¶

Visualize Latency Distribution of the given neuron population using plot_latencydist_in_ax()

Parameters:: spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param stimulus_onset:

float
param binsz:

integer or float; defines the number of equal-width bins in the range
param window:

2-tuple; defines upper and lower range of the bins
param neurons:

“all” or list: range(a, b) or [1, 4, 5, 9]
param nucleus:

string; name of the nucleus
param orient:

“horizontal” or None [default]
return:

object ax with Rate Distribution plotting done into it

analyseur.cbgtc.visual.distribution.plot_latencydist_in_ax(ax, spiketimes_set, stimulus_onset=None, binsz=None, window=None, neurons=None, nucleus=None, orient=None)[source]¶

Response Latency Distribution

Number of Neurons
^
|            █
|          ████
|        ███████
|      ███████████
|    ███████████████
|      ███████████
|            │
|            │  Mean
+-------------------------------------------------> Response Latency (s)
0.000   0.005   0.010   0.015   0.020   0.025

Histogram shows the distribution of response latencies
across neurons. The vertical marker indicates the
mean response latency.

Draws the Population Latency Distribution on the given matplotlib.pyplot.axis

Parameters:

ax – object matplotlib.pyplot.axis`
spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param stimulus_onset:

float; 0 [default]
param binsz:

integer or float; 0.01 [default]
param window:

2-tuple; (0, 10) [default]
param neurons:

“all” [default] or list: range(a, b) or [1, 4, 5, 9]
param nucleus:

string; name of the nucleus
param orient:

“horizontal” or None [default]
return:

object ax with Rate Distribution plotting done into it

analyseur.cbgtc.visual.distribution.plot_ratedist(spiketimes_set, binsz=None, window=None, neurons=None, nucleus=None, orient=None)[source]¶

Visualize Rate Distribution of the given neuron population using plot_ratedist_in_ax()

Parameters:: spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param binsz:

integer or float; defines the number of equal-width bins in the range
param window:

2-tuple; defines upper and lower range of the bins
param neurons:

“all” or list: range(a, b) or [1, 4, 5, 9]
param nucleus:

string; name of the nucleus
param orient:

“horizontal” or None [default]
return:

object ax with Rate Distribution plotting done into it

analyseur.cbgtc.visual.distribution.plot_ratedist_in_ax(ax, spiketimes_set, binsz=None, window=None, neurons=None, nucleus=None, orient=None)[source]¶

Population Rate Distribution

Number of Neurons
^
|                █
|              ████
|            ███████
|          ███████████
|        ███████████████
|      ███████████████████
|            │
|            │  Mean
+-------------------------------------------------> Firing Rate (Hz)
65     70     75     80     85     90     95    100

Histogram shows the distribution of firing rates
across neurons. The vertical marker indicates the
mean population firing rate.

Draws the Population Rate Distribution on the given matplotlib.pyplot.axis

Parameters:

ax – object matplotlib.pyplot.axis`
spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param binsz:

integer or float; 0.01 [default]
param window:

2-tuple; (0, 10) [default]
param neurons:

“all” [default] or list: range(a, b) or [1, 4, 5, 9]
param nucleus:

string; name of the nucleus
param orient:

“horizontal” or None [default]
return:

object ax with Rate Distribution plotting done into it

analyseur.cbgtc.visual.distribution.plot_spike_counts_distrib(spiketimes_set, neurons=None, nucleus=None, orient=None)[source]¶

Visualize Spike Count Distribution of the given neuron population using plot_spike_counts_distrib_in_ax()

Parameters:: spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param neurons:

“all” or list: range(a, b) or [1, 4, 5, 9]
param nucleus:

string; name of the nucleus
param orient:

“horizontal” or None [default]
return:

object ax with Rate Distribution plotting done into it

analyseur.cbgtc.visual.distribution.plot_spike_counts_distrib_in_ax(ax, spiketimes_set, neurons=None, nucleus=None, orient=None)[source]¶

Cumulative Spike Count
^
|                             ███████████████████
|                        █████
|                   █████
|              █████
|         █████
|    █████
| █████
|
+-------------------------------------------------> Time (s)
0          2          4          6          8         10

Thin step curves represent cumulative spike counts
for individual neurons.

The thicker curve represents the total cumulative
spike count of the entire neuron population.

Draws the Spike Count Distribution on the given matplotlib.pyplot.axis

Parameters:

ax – object matplotlib.pyplot.axis`
spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param neurons:

“all” [default] or list: range(a, b) or [1, 4, 5, 9]
param nucleus:

string; name of the nucleus
param orient:

“horizontal” or None [default]
return:

object ax with Rate Distribution plotting done into it

analyseur.cbgtc.visual.distribution.plot_spike_density_distrib(spiketimes_set, window=(0, 10), bandwidth=0.1, plot_type='line')[source]¶

Visualize Spike Density Distribution of the given neuron population using plot_spike_density_distrib_line_in_ax() or plot_spike_density_distrib_stacked_in_ax() (depending on the plot_type).

Parameters:: spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param window:

2-tuple; defines upper and lower range of the bins
param bandwidth:

0.1 [default]
param plot_type:

“line” [default] or “stacked”
return:

object ax with spike density distribution plot done into it

analyseur.cbgtc.visual.distribution.plot_spike_density_distrib_line_in_ax(ax, spiketimes_set, window=(0, 10), bandwidth=0.1)[source]¶

Population Spike Density

Density
^
|                ________
|             __--      --__
|           _--            --_
|         _--                --_
|       _--                    --_
|______--                        --______
|
+-------------------------------------------------> Time (s)
0          2          4          6          8         10

Smooth red curve represents the population spike
density estimated using kernel density estimation
(KDE) over all spikes in the neuron population.

Visualize Spike Density Distribution of the given neuron population.

Parameters:: spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param window:

2-tuple; defines upper and lower range of the bins
param bandwidth:

0.1 [default]
return:

object ax with Spike Density Distribution plot done into it

analyseur.cbgtc.visual.distribution.plot_spike_density_distrib_stacked_in_ax(ax, spiketimes_set, window=(0, 10), bandwidth=0.1, max_neurons=20)[source]¶

Density + Offset
^

|      ───────────── population density
|
|      ~~~~~~~~ neuron 10
|      ~~~~~~~~ neuron 9
|         :       :    :
|      ~~~~~~~~ neuron 3
|      ~~~~~~~~ neuron 2
|      ~~~~~~~~ neuron 1
|
+------------------------------------> Time

Visualize Spike Density Distribution of the given neuron population and plot max_neurons such that plot of one neuron is stacked over another.

Parameters:: spiketimes_set – Dictionary returned using get_spiketimes_superset()

or using get_spiketimes_subset()

OPTIONAL parameters

param window:

2-tuple; defines upper and lower range of the bins
param bandwidth:

0.1 [default]
param max_neurons:

20 [default]
return:

object ax with Spike Density Distribution plot done into it