Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches

[1] § Methods › Spike sorting pipeline steps › Job dispatch ↔ sample_dataset/create_test_spikeinterface.py, lines 19–63 · score 0.70 · recording_name, job dispatch, SpikeInterface Recording, probes
[2] § Results › An end-to-end pipeline for spike sorting large-scale electrophysiology data ↔ scripts/get_protocols.py, lines 1–15 · score 0.55 · Neural Dynamics, Allen Institute, Schema, AIND
[3] § Results › An end-to-end pipeline for spike sorting large-scale electrophysiology data ↔ src/aind_data_schema_models/organizations.py, lines 1170–1436 · score 0.53 · Neural Dynamics, Allen Institute, AIND, electrophysiology, Schema
[4] § Methods › NWB packaging ↔ sample_dataset/create_test_nwb.py, lines 1–35 · score 0.51 · NWB export step, Device, Electrodes

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 70 lines · 2.4 KB · MIT · 1 match

#!/usr/bin/env python3
"""
This script creates a 3-minute synthetic recording and saves it to SpikeInterface format for testing the pipeline.
Requirements:
- spikeinterface
"""
from argparse import ArgumentParser
import spikeinterface as si
from pathlib import Path
this_folder = Path(__file__).parent
si.set_global_job_kwargs(n_jobs=0.7)
SEED = 2308
def generate_spikeinterface(num_segments=1):
duration = 180
short_duration = 10
num_channels = 32
num_units = 20
output_folder = this_folder / "spikeinterface"
output_folder.mkdir(exist_ok=True)
recording_main, _ = si.generate_ground_truth_recording(
num_channels=num_channels,
num_units=num_units,
durations=[duration] * num_segments,
seed=SEED
)
# Also add one short recording that should be skipped
recording_short, _ = si.generate_ground_truth_recording(
num_channels=num_channels,
num_units=num_units,
durations=[short_duration] * num_segments,
seed=SEED+1
)
# Add unsigned electrical series
recording, _ = si.generate_ground_truth_recording(
num_channels=num_channels,
num_units=num_units,
durations=[duration] * num_segments,
seed=SEED+2
)
traces_list = []
for segment_index in range(recording.get_num_segments()):
traces = recording.get_traces(segment_index=segment_index)
# add offset
traces_unsigned = traces + 2**15
traces_unsigned = traces_unsigned.astype('uint16')
traces_list.append(traces_unsigned)
recording_unsigned = si.NumpyRecording(traces_list, sampling_frequency=recording.sampling_frequency)
recording_unsigned.set_probe(recording.get_probe(), in_place=True)
recording_unsigned.set_channel_gains(1)
recording_unsigned.set_channel_offsets(0)
# save spikeinterface recording zarr format for testing the job dispatch
for recording_name, recording in zip(["main", "short", "unsigned"], [recording_main, recording_short, recording_unsigned]):
recording.save(folder=output_folder / f"sample_recording_{recording_name}.zarr", format="zarr", overwrite=True)
parser = ArgumentParser()
parser.add_argument("--num-segments", type=int, default=1, help="Number of segments to generate for the recordings.")
if __name__ == '__main__':
args = parser.parse_args()
generate_spikeinterface(num_segments=args.num_segments)

create_test_spikeinterface.py at commit 80b38fe, under MIT · at the source

Overview

Authors: Alessio Paolo Buccino¹, Arjun Sridhar¹, David Feng¹, Karel Svoboda¹, Joshua H Siegle¹

ORCID iDs: Alessio Paolo Buccino, Arjun Sridhar, David Feng, Karel Svoboda, Joshua H Siegle

Allen Institute for Neural Dynamics Seattle United States

Institutions: Allen Institute for Neural Dynamics (United States)

Journal: eLife, volume 15, article RP110170

Dates: published online 5 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7554/elife.110170 · PMID 42554034 · PMCID PMC13441294 · OpenAlex W7127358730

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: extracellular electrophysiology (units, LFP) (modality), mouse (organism), methods / tools (subfield)

Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging

Keywords: spike sorting, electrophysiology, reproducibility, open-source software, Mouse

MeSH: Action Potentials*, Electrophysiological Phenomena*, Electrophysiology*, Algorithms, Animals, Reproducibility of Results, Software (* major topic)

Journal subjects: Neuroscience

Topic: Cardiac electrophysiology and arrhythmias (Cardiology and Cardiovascular Medicine, Medicine), according to OpenAlex

Funding: Harvard University

Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

The scale of in vivo electrophysiology has expanded in recent years, with simultaneous recordings across thousands of electrodes now becoming routine. These advances have enabled a wide range of discoveries, but they also impose substantial computational demands. Spike sorting, the procedure that extracts spikes from extracellular voltage measurements, remains a major bottleneck: a dataset collected in a few hours can take days to spike sort on a single machine, and the field lacks rigorous validation of the many spike sorting algorithms and preprocessing steps that are in use. Advancing the speed and accuracy of spike sorting is essential to fully realize the potential of large-scale electrophysiology. Here, we present an end-to-end spike sorting pipeline that leverages parallelization to scale to large datasets. The same workflow can run reproducibly on individual workstations, high-performance computing clusters, or cloud environments, with computing resources tailored to each processing step to reduce costs and execution times. In addition, we introduce a benchmarking pipeline, also optimized for parallel processing, that enables systematic comparison of multiple sorting pipelines. Using this framework, we show that Kilosort4, a widely used spike sorting algorithm, outperforms Kilosort2.5. We also show that 7× lossy compression, which substantially reduces the cost of data storage, has minimal impact on spike sorting performance. Together, these pipelines address the urgent need for scalable and transparent spike sorting of electrophysiology data, preparing the field for the coming flood of multi-thousand-channel experiments.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

AllenNeuralDynamics/aind-ephys-pipeline

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 80b38febf7807ecc97614952ae72412209fca43a, 21 September 2026

Languages: Python (7), Shell (7), JavaScript (2)

Size: 82 files, 16 scripts

Software Heritage: archived

Found in: “Data availability”

Holds: README, license file, environment (docs/requirements.txt, environment/requirements.txt), tests, continuous integration, documentation

Not found: CITATION.cff

Tools: Nextflow (4 files), SpikeInterface (4 files), Neurodata Without Borders (PyNWB, MatNWB) (2 files), NumPy (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

18 files

docs/source/conf.py — Python, 75 lines
environment/build_all.sh — Shell, 21 lines
environment/push_all.sh — Shell, 28 lines
params_app/ajv7.min.js — JavaScript, 9 lines
params_app/app.js — JavaScript, 437 lines
params_app/serve.py — Python, 35 lines
pipeline/slurm_submit.sh — Shell, 30 lines
pull_pipeline_images.sh — Shell, 118 lines
sample_dataset/create_test_nwb.py — Python, 153 lines, 1 match
sample_dataset/create_test_spikeinterface.py — Python, 70 lines, 1 match
tests/check_pipeline_results.py — Python, 396 lines
tests/check_sorter_params.py — Python, 93 lines
tests/test_pipeline_local_nwb.sh — Shell, 65 lines
tests/test_pipeline_local_si.sh — Shell, 47 lines
tests/test_pipeline_slurm.sh — Shell, 71 lines
tests/validate_params.py — Python, 34 lines
LICENSE — License, 21 lines
README.md — Text, 93 lines

At the source: github.com/AllenNeuralDynamics/aind-ephys-pipeline

AllenNeuralDynamics/aind-ephys-hybrid-benchmark

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 5c0c61df4eaf94f59a1c5be8d0d9f9dc458ea1af, 18 May 2026

Languages: Shell (4), Python (1)

Size: 22 files, 5 scripts

Software Heritage: not archived

Found in: the references

Holds: README, license file, tests, continuous integration

Not found: CITATION.cff, environment file, documentation

Tools: Nextflow (3 files), Neurodata Without Borders (PyNWB, MatNWB) (1 file), SpikeInterface (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

7 files

pipeline/slurm_submit_sorters.sh — Shell, 30 lines
pull_pipeline_images.sh — Shell, 119 lines
sample_dataset/create_test_nwb.py — Python, 69 lines
tests/test_pipeline_lossy.sh — Shell, 36 lines
tests/test_pipeline_sorters.sh — Shell, 36 lines
LICENSE — License, 21 lines
README.md — Text, 264 lines

At the source: github.com/AllenNeuralDynamics/aind-ephys-hybrid-benchmark

hub.docker.com/r/nextflow

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: the link answers

Software Heritage: not checked

Found in: the text, “Limitations of our approach”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

At the source: hub.docker.com/r/nextflow/nextflow

Zenodo 19481268

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file, 0 scripts

Software Heritage: not checked

Found in: the references

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

At the source:

AllenNeuralDynamics/aind-data-schema-models

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: f86f589ccfd3fa6c64d90c3a33b15bcd468431f7, 19 August 2026

Languages: Python (68), Shell (1)

Size: 126 files, 69 scripts

Software Heritage: not archived

Found in: the appendix

Holds: README, license file, environment (pyproject.toml, setup.py), tests, continuous integration

Not found: CITATION.cff, documentation

Tools: pandas (5 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

71 files

doc_template/source/conf.py — Python, 54 lines
run_all.sh — Shell, 12 lines
scripts/__init__.py — Python, 1 line
scripts/get_developmental_stage_ontologies.py — Python, 70 lines
scripts/get_protocols.py — Python, 92 lines, 1 match
scripts/test_developmental_stage_integration.py — Python, 40 lines
scripts/test_genbank_integration.py — Python, 26 lines
scripts/test_mouse_anatomy_integration.py — Python, 26 lines
scripts/write_to_docdb.py — Python, 88 lines
setup.py — Python, 4 lines
src/aind_data_schema_models/__init__.py — Python, 3 lines
src/aind_data_schema_models/_generators/__init__.py — Python, 1 line
src/aind_data_schema_models/_generators/dev_utils.py — Python, 40 lines
src/aind_data_schema_models/_generators/generator.py — Python, 109 lines
src/aind_data_schema_models/_generators/update_harp_types.py — Python, 8 lines
src/aind_data_schema_models/atlas.py — Python, 10 lines
src/aind_data_schema_models/brain_atlas.py — Python, 5,085 lines
src/aind_data_schema_models/celegans_developmental_stage.py — Python, 902 lines
src/aind_data_schema_models/coordinates.py — Python, 70 lines
src/aind_data_schema_models/data_name_patterns.py — Python, 116 lines
src/aind_data_schema_models/devices.py — Python, 181 lines
src/aind_data_schema_models/drosophila_developmental_stage.py — Python, 332 lines
src/aind_data_schema_models/gene.py — Python, 54 lines
src/aind_data_schema_models/harp_types.py — Python, 432 lines
src/aind_data_schema_models/human_developmental_stage.py — Python, 361 lines
src/aind_data_schema_models/licenses.py — Python, 10 lines
src/aind_data_schema_models/modalities.py — Python, 231 lines
src/aind_data_schema_models/mouse_anatomy.py — Python, 3,622 lines
src/aind_data_schema_models/mouse_developmental_stage.py — Python, 256 lines
src/aind_data_schema_models/organizations.py — Python, 1,448 lines, 1 match
src/aind_data_schema_models/pid_names.py — Python, 25 lines
src/aind_data_schema_models/process_names.py — Python, 55 lines
src/aind_data_schema_models/protocols.py — Python, 904 lines
src/aind_data_schema_models/reagent.py — Python, 24 lines
src/aind_data_schema_models/registries.py — Python, 22 lines
src/aind_data_schema_models/slap2_acquisition_type.py — Python, 10 lines
src/aind_data_schema_models/species.py — Python, 227 lines
src/aind_data_schema_models/specimen_procedure_types.py — Python, 25 lines
src/aind_data_schema_models/stimulus_modality.py — Python, 16 lines
src/aind_data_schema_models/system_architecture.py — Python, 53 lines
src/aind_data_schema_models/units.py — Python, 173 lines
tests/__init__.py — Python, 7 lines
tests/test_atlas.py — Python, 18 lines
tests/test_brain_atlas.py — Python, 123 lines
tests/test_celegans_developmental_stage.py — Python, 91 lines
tests/test_coordinates.py — Python, 18 lines
tests/test_data_name_patterns.py — Python, 145 lines
tests/test_dev_utils.py — Python, 186 lines
tests/test_devices.py — Python, 18 lines
tests/test_drosophila_developmental_stage.py — Python, 91 lines
tests/test_gene.py — Python, 68 lines
tests/test_generator.py — Python, 158 lines
tests/test_human_developmental_stage.py — Python, 91 lines
tests/test_licenses.py — Python, 18 lines
tests/test_modalities.py — Python, 34 lines
tests/test_models.py — Python, 143 lines
tests/test_mouse_anatomy.py — Python, 87 lines
tests/test_mouse_developmental_stage.py — Python, 91 lines
tests/test_organizations.py — Python, 28 lines
tests/test_pid_names.py — Python, 23 lines
tests/test_process_names.py — Python, 18 lines
tests/test_protocols.py — Python, 27 lines
tests/test_reagent.py — Python, 18 lines
tests/test_registries.py — Python, 18 lines
tests/test_slap2_acquisition_type.py — Python, 18 lines
tests/test_specimen_procedure_types.py — Python, 18 lines
tests/test_stimulus_modality.py — Python, 18 lines
tests/test_system_architecture.py — Python, 16 lines
tests/test_units.py — Python, 22 lines
LICENSE — License, 21 lines
README.md — Text, 44 lines

At the source: github.com/AllenNeuralDynamics/aind-data-schema-models

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
90 scripts, each with its path and the digest of its content;
4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

hub.docker.com/u/spikeinterface — at hub.docker.com; found in the text, “Enabling technologies”

Data availability

Hybrid data for evaluating spike sorters and lossy compression are available at the following URL: https://registry.opendata.aws/allen-nd-ephys-hybrid-evaluation/. The code for the presented pipelines is available on GitHub2022; Spike sorting pipeline: https://github.com/AllenNeuralDynamics/aind-ephys-pipeline (Buccino et al., 2026a); Evaluation pipeline: https://github.com/AllenNeuralDynamics/aind-ephys-hybrid-benchmark (Buccino, 2026b). The figures from the results section can have been generated with the following Code Ocean capsule: https://codeocean.allenneuraldynamics.org/capsule/9554286/tree/v4. The spike sorting pipeline is also available in the CodeOcean Public Collections: https://codeocean.allenneuraldynamics.org/capsule/4364160/tree/v9.

The following dataset was generated:

Allen Institute 2026Allen Institute for Neural Dynamics - Extracellular Electrophysiology Hybrid Evaluation BenchmarkRegistry of Open Data on AWSallen-nd-ephys-hybrid-evaluation

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

Funding: added Harvard University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 7 MeSH terms, 58 references.

Cite

This paper

Buccino, A. P., Sridhar, A., Feng, D., Svoboda, K., & Siegle, J. H. (2026). Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data. eLife, 15, RP110170. https://doi.org/10.7554/elife.110170

BibTeX

@article{buccino2026efficient, author = {Buccino, Alessio Paolo and Sridhar, Arjun and Feng, David and Svoboda, Karel and Siegle, Joshua H}, title = {{Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data}}, journal = {eLife}, year = {2026}, month = aug, volume = {15}, pages = {RP110170}, publisher = {eLife Sciences Publications, Ltd}, issn = {2050-084X}, doi = {10.7554/elife.110170}, url = {https://doi.org/10.7554/elife.110170}, pmid = {42554034}, pmcid = {PMC13441294} }

RIS

TY - JOUR AU - Buccino, Alessio Paolo AU - Sridhar, Arjun AU - Feng, David AU - Svoboda, Karel AU - Siegle, Joshua H TI - Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data T2 - eLife J2 - eLife PY - 2026 DA - 2026/08/05 VL - 15 SP - RP110170 SN - 2050-084X PB - eLife Sciences Publications, Ltd DO - 10.7554/elife.110170 UR - https://doi.org/10.7554/elife.110170 LA - en ER -

CSL-JSON

{ "id": "10.7554/elife.110170", "type": "article-journal", "title": "Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data", "container-title": "eLife", "author": [ { "family": "Buccino", "given": "Alessio Paolo" }, { "family": "Sridhar", "given": "Arjun" }, { "family": "Feng", "given": "David" }, { "family": "Svoboda", "given": "Karel" }, { "family": "Siegle", "given": "Joshua H" } ], "container-title-short": "eLife", "volume": "15", "page": "RP110170", "DOI": "10.7554/elife.110170", "PMID": "42554034", "PMCID": "PMC13441294", "ISSN": "2050-084X", "publisher": "eLife Sciences Publications, Ltd", "URL": "https://doi.org/10.7554/elife.110170", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 5 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.7554/elife.110588 [code]: Opening the black box toward a modular approach to spike sorting.
Journal: eLife
In common: SpikeInterface, Neurodata Without Borders (PyNWB, MatNWB), pandas, 1 other tool, extracellular electrophysiology (units, LFP), methods / tools, mouse, 18 references, author Alessio Paolo Buccino
[2] doi:10.1038/s41592-026-03076-z [code]: Neuropixels Opto: combining high-resolution electrophysiology and optogenetics.
Journal: Nature methods
In common: SpikeInterface, Neurodata Without Borders (PyNWB, MatNWB), pandas, 1 other tool, extracellular electrophysiology (units, LFP), mouse, 7 references, 2 authors
[3] doi:10.1523/jneurosci.1594-25.2026 [code]: KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: extracellular electrophysiology (units, LFP), methods / tools, mouse, 15 references
[4] doi:10.1016/j.patter.2026.101590 [code]: Density-based longitudinal neuron tracking in high-density electrophysiological recordings.
Journal: Patterns (New York, N.Y.)
In common: SpikeInterface, pandas, NumPy, extracellular electrophysiology (units, LFP), 8 references
[5] doi:10.1038/s41593-026-02232-0 [code]: Entorhinal cortex represents task-relevant remote locations independently of CA1.
Journal: Nature neuroscience
In common: SpikeInterface, Neurodata Without Borders (PyNWB, MatNWB), pandas, 1 other tool, mouse, 4 references
[6] doi:10.1016/j.crmeth.2026.101421 [code]: EthoPy provides an accessible platform for reproducible behavioral neuroscience.
Journal: Cell reports methods
In common: Neurodata Without Borders (PyNWB, MatNWB), pandas, NumPy, methods / tools, mouse, 5 references
[7] doi:10.1371/journal.pcbi.1014283 [code]: Spatial richness of neural magnetic fields.
Journal: PLoS computational biology
In common: SpikeInterface, NumPy, 5 references
[8] doi:10.1080/26941899.2026.2619222: Neurodatascience: Past, Present, and Future.
Journal: Data science in science
In common: 7 references
[9] doi:10.1038/s41467-026-76939-w [code]: HIPPIE: a generative model for electrophysiological analysis across species, technologies, and modalities.
Journal: Nature communications
In common: SpikeInterface, Neurodata Without Borders (PyNWB, MatNWB), pandas, 1 other tool, methods / tools, mouse, 2 references
[10] doi:10.1038/s41586-026-10448-0 [code]: Plasticity and language in the anaesthetized human hippocampus.
Journal: Nature
In common: SpikeInterface, NumPy, extracellular electrophysiology (units, LFP), 4 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.

Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data.

Code ↔ Paper

Paper

The authors' code

Overview

Abstract

Repositories

AllenNeuralDynamics/aind-ephys-pipeline

AllenNeuralDynamics/aind-ephys-hybrid-benchmark

hub.docker.com/r/nextflow

Zenodo 19481268

AllenNeuralDynamics/aind-data-schema-models

Tracing map

Data

Datasets cited

Data availability

Versions

Version 3, 28 September 2026

Version 1, 27 September 2026: the first record

Cite

This paper

Similar papers

Contribute

Claim this paper

Correct its record

Validate its tracing map

Add the badge to its README

Request its removal

Discussion, reproductions, activity