Run spike sorting algorithms

This example shows the basic usage of the sorters module of spikeinterface

import spikeinterface.extractors as se
import spikeinterface.sorters as ss

First, let’s create a toy example:

recording, sorting_true = se.example_datasets.toy_example(duration=10, seed=0)

Check available sorters

print(ss.available_sorters())

Out:

['combinato', 'hdsort', 'herdingspikes', 'ironclust', 'kilosort', 'kilosort2', 'kilosort2_5', 'kilosort3', 'klusta', 'mountainsort4', 'spykingcircus', 'tridesclous', 'waveclus', 'yass']

This will list the sorters available through SpikeInterface. To see which sorters are installed on the machine you can run:

print(ss.installed_sorters())

Out:

['klusta', 'mountainsort4', 'tridesclous']

Change sorter parameters

default_ms4_params = ss.Mountainsort4Sorter.default_params()
print(default_ms4_params)

Out:

{'detect_sign': -1, 'adjacency_radius': -1, 'freq_min': 300, 'freq_max': 6000, 'filter': True, 'whiten': True, 'curation': False, 'num_workers': None, 'clip_size': 50, 'detect_threshold': 3, 'detect_interval': 10, 'noise_overlap_threshold': 0.15}

Parameters can be changed either by passing a full dictionary, or by passing single arguments.

# Mountainsort4 spike sorting
default_ms4_params['detect_threshold'] = 4
default_ms4_params['curation'] = False

# parameters set by params dictionary
sorting_MS4 = ss.run_mountainsort4(recording=recording, **default_ms4_params,
                                   output_folder='tmp_MS4')

Out:

Warning! The recording is already filtered, but Mountainsort4 filter is enabled. You can disable filters by setting 'filter' parameter to False

# parameters set by params dictionary
sorting_MS4_10 = ss.run_mountainsort4(recording=recording, detect_threshold=10,
                                      output_folder='tmp_MS4')

Out:

Warning! The recording is already filtered, but Mountainsort4 filter is enabled. You can disable filters by setting 'filter' parameter to False

print('Units found with threshold = 4:', sorting_MS4.get_unit_ids())
print('Units found with threshold = 10:', sorting_MS4_10.get_unit_ids())

Out:

Units found with threshold = 4: [1, 2, 3, 4, 5, 6, 7, 8, 9]
Units found with threshold = 10: [1, 2, 3, 4]

Run other sorters

# SpyKING Circus spike sorting
# sorting_SC = ss.run_spykingcircus(recording, output_folder='tmp_SC')
# print('Units found with Spyking Circus:', sorting_SC.get_unit_ids())

# KiloSort spike sorting (KILOSORT_PATH and NPY_MATLAB_PATH can be set as environment variables)
# sorting_KS = ss.run_kilosort(recording, output_folder='tmp_KS')
#  print('Units found with Kilosort:', sorting_KS.get_unit_ids())

# Kilosort2 spike sorting (KILOSORT2_PATH and NPY_MATLAB_PATH can be set as environment variables)
# sorting_KS2 = ss.run_kilosort2(recording, output_folder='tmp_KS2')
#  print('Units found with Kilosort2', sorting_KS2.get_unit_ids())

# Klusta spike sorting
#  sorting_KL = ss.run_klusta(recording, output_folder='tmp_KL')
# print('Units found with Klusta:', sorting_KL.get_unit_ids())

# IronClust spike sorting (IRONCLUST_PATH can be set as environment variables)
# sorting_IC = ss.run_ironclust(recording, output_folder='tmp_IC')
# print('Units found with Ironclust:', sorting_IC.get_unit_ids())

# Tridesclous spike sorting
# sorting_TDC = ss.run_tridesclous(recording, output_folder='tmp_TDC')
# print('Units found with Tridesclous:', sorting_TDC.get_unit_ids())