viziphant.asset.plot_synchronous_events

viziphant.asset.plot_synchronous_events(spiketrains, sse, title=None, **kwargs)

Reorder and plot the spiketrains according to a series of synchronous events sse obtained with the ASSET analysis. Spike trains that do not participate in the chosen group will be shown at the top in a different color.

Parameters:

spiketrainslist of neo.SpikeTrain

ASSET input spiketrains.

ssedict

One entry of the output dict from elephant.asset.ASSET.extract_synchronous_events().

titlestr or None, optional

User-defined title string. If None, it’ll be set to an automatic description. Default: None

**kwargs

Additional arguments to viziphant.rasterplot.rasterplot()

Returns:

axesmatplotlib.Axes.axes

See also

viziphant.patterns.plot_patterns

plot patterns repeated in time

Examples

In this example we

• simulate two noisy synfire chains;

• shuffle the neurons to destroy visual appearance;

• run ASSET analysis to recover the original neurons arrangement.

import neo
import numpy as np
import quantities as pq
import matplotlib.pyplot as plt

import viziphant
from elephant import asset

np.random.seed(10)
spiketrain = np.linspace(0, 50, num=10)
np.random.shuffle(spiketrain)
spiketrains = np.c_[spiketrain, spiketrain + 100]
spiketrains += np.random.random_sample(spiketrains.shape) * 5
spiketrains = [neo.SpikeTrain(st, units='ms', t_stop=1 * pq.s)
               for st in spiketrains]
asset_obj = asset.ASSET(spiketrains, bin_size=3 * pq.ms)

imat = asset_obj.intersection_matrix()
pmat = asset_obj.probability_matrix_analytical(imat,
                                               kernel_width=50 * pq.ms)
jmat = asset_obj.joint_probability_matrix(pmat, filter_shape=(5, 1),
                                          n_largest=3)
mmat = asset_obj.mask_matrices([pmat, jmat], thresholds=.9)
cmat = asset_obj.cluster_matrix_entries(mmat, max_distance=11,
                                        min_neighbors=3, stretch=5)
sses = asset_obj.extract_synchronous_events(cmat)

viziphant.asset.plot_synchronous_events(spiketrains, sse=sses[1], s=10)
plt.show()

(Source code, png, hires.png, pdf)

Refer to ASSET tutorial for real-case scenario.