Neo All - example 1¶

This example shows how to use create a NeoAll instance.

For instance, the Spyking Cirucs files may look like these :

Details of the files :

• The 000_AA.prb file is the probe file, it contains information about how contacts are organized on the electrodes, see here for more info. This file is needed for launching the spike-sorting. It is used in NeoAll for creating channel indexes and knowing the number of tetrodes.
• The .result.hdf5 file is used to store the spike times for each unit, at the end of the spike sorting procedure.
• The .template.hdf5 file contains the preffered contact of each unit.
• In order to generate the units template, three files are used : .basis.hdf5, .templates.hdf5 and .clusters.hdf5 files. Unit template is computed using the get_templates_from_spykingcircus_file() function.

First import the neoStructures :

from neoStructures import *
import matplotlib.pyplot as plt
from os.path import isdir, join

Import the data and create the NeoAll instance

data_dir = join('pySpikeAnalysis', 'sample_data') if isdir('pySpikeAnalysis') else join('..', '..', 'pySpikeAnalysis', 'sample_data')
spykingcircus_dir = r'SpykingCircus_results'
probe_filename = r'000_AA.prb'
results_filename = r'spykingcircusres'

neoAll = NeoAll(join(data_dir, spykingcircus_dir), results_filename, join(data_dir, probe_filename), save_fig=0)

See information about NeoAll

print(neoAll)

Out:

NeoAll Instance with 54 units. 1 Neo segment per unit. Each segment contains 1 Neo spiketrain
10 channel indexes

neoAll contains a list of Neo Segments (1 segment per unit)

print(type(neoAll.segments[0]))

Out:

<class 'neo.core.segment.Segment'>

Computing the mean firing rate is easy, e.g. for unit 3:

mean_spkrate_3 = len(neoAll.segments[3].spiketrains[0]) / (neoAll.segments[3].t_stop - neoAll.segments[3].t_start)
print(mean_spkrate_3)

Out:

2.410294521750985 1/s

The method neoStructures.NeoAll.plot_spikerate_evolution() can be used to visualize the firing-rate over time The firing rate is computed by kernel convolution. The kernel is gaussian and the standard deviation can be set using the sigma_gauss_kernel parameter. The package Elephant is used for the estimation.

neoAll.plot_spikerate_evolution(unit_pos=3, sigma_gauss_kernel=30*s)

The shape of each unit can be plotted using the plot_unit_shape method :

neoAll.plot_unit_shape(2)

As well as the ISI (Inter-Spike Interval), e.g. for unit 22 :

neoAll.plot_isi(unit_pos=22)

Zoom on the x-origin and increase the number of bins for the histogram

neoAll.plot_isi(22, bin_duration_ms=0.1, tmax_ms=15)

Total running time of the script: ( 0 minutes 0.740 seconds)

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