synchrony.rst

Synchrony Metrics (synchrony)

Calculation

This function is providing a metric for the presence of synchronous spiking events across multiple spike trains.

The complexity is used to characterize synchronous events within the same spike train and across different spike trains. This way synchronous events can be found both in multi-unit and single-unit spike trains. Complexity is calculated by counting the number of spikes (i.e. non-empty bins) that occur at the same sample index, within and across spike trains.

Synchrony metrics can be computed for different synchrony sizes (>1), defining the number of simultaneous spikes to count.

Expectation and use

A larger value indicates a higher synchrony of the respective spike train with the other spike trains. Larger values, especially for larger sizes, indicate a higher probability of noisy spikes in spike trains.

Example code

import spikeinterface.qualitymetrics as sqm
# Make recording, sorting and wvf_extractor object for your data.
synchrony = sqm.compute_synchrony_metrics(waveform_extractor=wvf_extractor, synchrony_sizes=(2, 4, 8))
# synchrony is a tuple of dicts with the synchrony metrics for each unit

Links to original implementations

The SpikeInterface implementation is a partial port of the low-level complexity functions from Elephant - Electrophysiology Analysis Toolkit

References

.. automodule:: spikeinterface.qualitymetrics.misc_metrics

    .. autofunction:: compute_synchrony_metrics

Literature

Based on concepts described in [Gruen]_