Population Spike Rate Histogram (PSRH) of CBGTC spike times

class analyseur.cbgtc.visual.popurate.PSRH(spiketimes_superset)

Bases: object

The Population Spike Rate Histogram (PSRH) Class is instantiated by passing

Parameters:

spiketimes_superset – Dictionary returned using LoadSpikeTimes

Methods	Return
plot()	matplotlib.pyplot.plot object
analytics()	dictionary of population dynamics from the population rates
plot_ratevar()	matplotlib.pyplot.plot object

• The instance must first invoke plot() before calling analytics() or plot_ratevar()

• psrh gives a collective dynamics of the population ensemble

Use Case:

1. Setup

from  analyseur.cbgtc.loader import LoadSpikeTimes
loadST = LoadSpikeTimes("/full/path/to/spikes_GPi.csv")
spiketimes_superset = loadST.get_spiketimes_superset()

from analyseur.cbgtc.visual.popurate import PSRH

my_psrh = PSRH(spiketimes_superset)

2. Population Spike Rate Histogram for the whole simulation window

my_psrh.plot()

3. PSRH for desired window and bin size

my_psrh.plot(window=(0,5), binsz=1)    # time unit in seconds
my_psrh.plot(window=(0,5), binsz=0.05)

4. Get the analytics

my_psrh.analytics()

5. View Firing Rate Variability

my_psrh.plot_ratevar()

---

analytics(stimulus_onset=0)

Extracts population dynamics from the population rates :param stimulus_onset: [OPTIONAL] default: 0 :return: dictionary

---

plot(binsz=0.05, window=(0, 10), nucleus=None, show=True)

Population Spiking Rate Histogram (PSRH)

Firing Rate
|
|      ~~~~~~~^^~~~~~^~~~~~~~
|     ~~~~~~~~~~~~~~~~~~~~~~~
|    ~~~~~~~~~~~~~~~~~~~~~~~~
|
+-----------------------------------------------> Time (s)
0        2        4        6        8        10

Continuous curve represents the time-varying
population firing rate across neurons.

Displays the Population Spike Rate Histogram (PSRH) of the given spike times (seconds) and returns the plot figure (to save if necessary).

Parameters:

• binsz – integer or float; defines the number of equal-width bins in the range [default: 50]

• window – 2-tuple; defines upper and lower range of the bins but ignore lower and upper outliers [default: (0,10000)]

• nucleus – string; [OPTIONAL] None or name of the nucleus

• show – boolean [default: True]

Returns:

object matplotlib.axes.Axes

• window controls the binning range as well as the spike counting window

• CBGT simulation was done in seconds so window (0, 10) signifies time 0 s to 10 s

---

plot_ratevar()

Population Rate Variability

Mean ± STD Firing Rate                 Coefficient of Variation
Firing Rate (Hz)                       CV
^                                      ^
|        ~~~~~~                        |      ~~~^~~~~~~
|      ~~~~~~~~~~~~                    |    ~~~~~~~~~~~~
|    ~~~~~~Mean~~~~~~                  |   ~~~~~~~~~~~~~
|   ~~   ± STD band  ~~                |
+-----------------------------> Time   +---------------------------> Time
0    2    4    6    8    10             0    2    4    6    8    10

Left : population firing rate with variability band (Mean ± STD)
Right: coefficient of variation of firing rates across neurons

Displays the Population Spike Rate Variability in terms of: Mean ± STD Variability and Coefficient of Variation.

Returns:

object matplotlib.axes.Axes

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