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)

compute_waveforms (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
compute_waveforms (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 299.62it/s]

noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 265.07it/s]

Fitting PCA:   0%|          | 0/10 [00:00<?, ?it/s]
Fitting PCA: 100%|██████████| 10/10 [00:00<00:00, 138.08it/s]

Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 1338.07it/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  13.090788                   0.0  ...     0.811594      0.025148
1  11.025825                   0.0  ...     0.781295      0.023390
2  49.486408                   0.0  ...     0.948000      0.004030
3  22.017705                   0.0  ...     0.886897      0.011896
4  16.043165                   0.0  ...     0.849333      0.025970
5  14.933948                   0.0  ...     0.828369      0.016012
6   8.375579                   0.0  ...     0.755263      0.024963
7  14.482635                   0.0  ...     0.833918      0.015618
8  32.088456                   0.0  ...     0.924675      0.009880
9  21.040992                   0.0  ...     0.854667      0.011940

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

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): 8 units - 1 segments - 25.0kHz

NumpyFolder (NumpyFolderSorting): 8 units - 1 segments - 25.0kHz

Unit IDs

['0' '2' '3' '4' '5' '7' '8' '9']

Annotations

• name : GroundTruthSorting

Properties

gt_unit_locations
[[ 1.5084618 -5.586387 40.53633 ] [21.39987 4.9219947 6.016091 ] [19.171072 2.5502763 47.012783 ] [ 2.6011252 9.156831 26.63745 ] [-2.5032432 -6.751782 5.7826333] [27.991325 29.75752 12.382597 ] [21.248518 19.10948 35.810043 ] [20.084871 -4.4097075 28.004942 ]]
main_channel_id
['0' '2' '2' '0' '0' '3' '3' '2']

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