Curation Tutorial

After spike sorting and computing quality metrics, you can automatically curate the spike sorting output using the quality metrics that you have calculated.

Import the modules and/or functions necessary from spikeinterface

import spikeinterface.core as si

Let’s generate a simulated dataset, and imagine that the ground-truth sorting is in fact the output of a sorter.

recording, sorting = si.generate_ground_truth_recording()
print(recording)
print(sorting)

GroundTruthRecording (InjectTemplatesRecording): 4 channels - 25.0kHz - 1 segments
                      250,000 samples - 10.00s - float32 dtype - 3.81 MiB
GroundTruthSorting (NumpySorting): 10 units - 1 segments - 25.0kHz

Create SortingAnalyzer

For this example, we will need a SortingAnalyzer and some extensions to be computed first

analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording, format="memory")
analyzer.compute(["random_spikes", "waveforms", "templates", "noise_levels"])

analyzer.compute("principal_components", n_components=3, mode="by_channel_local")
print(analyzer)

estimate_sparsity (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
estimate_sparsity (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 432.75it/s]

compute_waveforms (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
compute_waveforms (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 299.20it/s]

noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 280.18it/s]

Fitting PCA:   0%|          | 0/10 [00:00<?, ?it/s]
Fitting PCA: 100%|██████████| 10/10 [00:00<00:00, 137.07it/s]

Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 1288.30it/s]
SortingAnalyzer: 4 channels - 10 units - 1 segments - memory - sparse - has recording
Loaded 5 extensions: random_spikes, waveforms, templates, noise_levels, principal_components

Then we compute some quality metrics:

metrics_ext = analyzer.compute("quality_metrics", metric_names=["snr", "isi_violation", "nearest_neighbor"])
metrics = metrics_ext.get_data()
print(metrics)

         snr  isi_violations_ratio  ...  nn_hit_rate  nn_miss_rate
0   9.940900                   0.0  ...     0.762238      0.028337
1  15.289981                   0.0  ...     0.842105      0.012162
2  41.397940                   0.0  ...     0.917568      0.005689
3  15.355858                   0.0  ...     0.857718      0.013783
4   0.587292                   0.0  ...     0.736774      0.025282
5  26.623136                   0.0  ...     0.902013      0.015880
6  17.911261                   0.0  ...     0.860465      0.014170
7  32.920304                   0.0  ...     0.897778      0.010823
8   4.771326                   0.0  ...     0.767857      0.036778
9   6.513491                   0.0  ...     0.725641      0.032681

[10 rows x 5 columns]

We can now threshold each quality metric and select units based on some rules.

The easiest and most intuitive way is to use boolean masking with a dataframe.

Then create a list of unit ids that we want to keep

keep_mask = (metrics["snr"] > 7.5) & (metrics["isi_violations_ratio"] < 0.2) & (metrics["nn_hit_rate"] > 0.80)
print(keep_mask)

keep_unit_ids = keep_mask[keep_mask].index.values
keep_unit_ids = [unit_id for unit_id in keep_unit_ids]
print(keep_unit_ids)

0    False
1     True
2     True
3     True
4    False
5     True
6     True
7     True
8    False
9    False
dtype: bool
['1', '2', '3', '5', '6', '7']

And now let’s create a sorting that contains only curated units and save it.

curated_sorting = sorting.select_units(keep_unit_ids)
print(curated_sorting)


curated_sorting.save(folder="curated_sorting", overwrite=True)

GroundTruthSorting (UnitsSelectionSorting): 6 units - 1 segments - 25.0kHz

NumpyFolder (NumpyFolderSorting): 6 units - 1 segments - 25.0kHz

Unit IDs

['1' '2' '3' '5' '6' '7']

Annotations

• name : GroundTruthSorting

Properties

gt_unit_locations
[[23.92269 -8.527548 30.431932 ] [20.82694 14.120619 5.301797 ] [-5.2041607 -4.3588405 13.920237 ] [26.604748 11.033228 46.74994 ] [-0.68769926 27.929218 28.502337 ] [14.685245 12.127082 26.214289 ]]

We can also save the analyzer with only theses units

clean_analyzer = analyzer.select_units(unit_ids=keep_unit_ids, format="zarr", folder="clean_analyzer")

print(clean_analyzer)

SortingAnalyzer: 4 channels - 6 units - 1 segments - zarr - sparse - has recording
Loaded 6 extensions: random_spikes, waveforms, templates, noise_levels, principal_components, quality_metrics