Sorting Components module

Spike sorting is comprised of several steps, or components. In the sortingcomponents module we are building a library of methods and steps that can be assembled to build full spike sorting pipelines.

This effort goes in the direction of modularization of spike sorting algorithms. Currently, spike sorters are shipped as full packages with all the steps needed to perform end-to-end spike sorting.

However, this might not be the best option. It is in fact very likely that a sorter has an excellent step, say the clustering, but another step is sub-optimal. Decoupling different steps as separate components would allow one to mix-and-match sorting steps from different sorters.

Another advantage of modularization is that we can accurately benchmark every step of a spike sorting pipeline. For example, what is the performance of peak detection method 1 or 2, provided that the rest of the pipeline is the same?

For now, we have methods for peak detection and peak localization. We are going to port methods for drift-correction, clustering, template-matching, and postprocessing/cleaning in the future.

Peak detection

Peak detection is usually the first step of spike sorting and it consists of finding peaks in the traces that could be actual spikes.

Peaks can be detected with the detect_peaks() function as follows:

import spikeinterface.sortingcomponents as scp

peaks = scp.detect_peaks(recording, method='by_channel',
                         peak_sign='neg', detect_threshold=5, n_shifts=2,

The output peaks is a numpy array with a length of the number of peaks found and the following dtype:

peak_dtype = [('sample_ind', 'int64'), ('channel_ind', 'int64'), ('amplitude', 'float64'), ('segment_ind', 'int64')]

Different methods are available with the method argument:

  • ‘by_channel’ (default): peaks are detected separately for each channel

  • ‘locally_exclusive’: peaks on neighboring channels within a certain radius are excluded (not counted multiple times)

Peak detection, as many sorting components, can be run in parallel.

Peak localization

Peak localization estimates the spike location on the probe. An estimate of location can be important to correct for drifts or cluster spikes into different units.

Currently, only the “center of mass” method is implemented.

Peak localization can be run as follows:

import spikeinterface.sortingcomponents as scp

peak_locations = scp.localize_peaks(recording, peaks, method='center_of_mass',
                                    local_radius_um=150, ms_before=0.3, ms_after=0.6,

The output peak_locations is a numpy array with dimension (num_spikes, 2), where the second dimension represent the x-y axis.

Drift correction




Template matching