Internal sorters

spikeinterface.sortingcomponents implement algorithms to break a sorting pipeline into individual components. With this components it is easy to develop a new sorter.

These components and sorters havs been benchmarked [here](https://github.com/samuelgarcia/sorting_components_benchmark_paper).

At the moment, there are 4 internal sorters implemented in spikeinterface:

  • lupin

  • spykingcircus2

  • tridesclous2

  • simple

Lupin

Lupin is components-based sorters, it combine components that give the best reults on benchmarks for each steps. It is theorically the “best” sorter that spikeinterface can offer internally.

Lupin components are:

  • preprocessing (filtering, CMR, whitening)

  • the DREDGE motion correction algorithm (optional)

  • peak detection with matched filtering

  • iterative splits for clustering Iter-ISOPLIT

  • augmented matching pursuit for the spike deconvolution with Wobble

Some notes on this algorithm and related parameters:

  • waveforms size is different for clustering and template matching: clustering_ms_before, clustering_ms_after, ms_before, ms_after

  • the filtering is quite smooth by default to filter out high-frequency noise: freq_max=7000

  • n_pca_features can impact the clustering step

  • there is a cleaning step before the template matching using template_sparsify_threshold, template_min_snr_ptp, template_max_jitter_ms, and min_firing_rate. This step can have a substantial impact on the result.

  • Lupin is a bit slower than tridesclous2 and spkykingcircus2, but more accurate!

SpyKING-CIRCUS 2

This is an updated version of SpyKING-CIRCUS [Yger2018] based on the modular components. In summary, this spike sorting pipeline uses optionaly the DREDGE motion correction algorithm before filtering and whitening the data. On these whitened data, the chains of components that are used are: matched filtering for peak detection, iterative splits for clustering (Iter-HDBSCAN), and orthogonal matching pursuit for template reconstruction (Circus-OMP).

SpyKING-CIRCUS 2 components are:

  • preprocessing (filtering, CMR, whitening)

  • the DREDGE motion correction algorithm (optional)

  • peak detection with matched filtering

  • iterative splits for clustering Iter-HDBSCAN

  • orthogonal matching pursuit for the spike deconvolution with Circus-OMP

TriDesClous 2

This is an updated version of TriDesClous based on the modular components. It is not as good as Lupin in terms of performance, but it’s way faster. This is sorter is a good choice for a very fast exploration of a dataset.

TriDesClous 2 components are:

  • preprocessing (filtering, CMR) but no whitening

  • the DREDGE motion correction algorithm (optional)

  • peak detection with locally_exlusive

  • iterative splits for clustering Iter-ISOPLIT

  • fast template matching using the TDC-peeler

Simple

This is a simple sorter that does not use the template matching. It can be seen as an “old school” sorter with only peak detection, feature reduction (svd) and clustering. Using this sorter can be very useful on mono channel and tetrode datasets. Very often on 1-4 channel dataset when the SNR is too small then template matching is an overkill feature that gives worse results.

The clustering step is quite flexible and several algorithms can be tested (k-means, isosplit, hdbscan, …)