Evidence map›Paper›PMID 34788137›Full record

ArticleJournal of neurophysiology2021

Evaluation and resolution of many challenges of neural spike sorting: a new sorter.

Nathan J Hall, David J Herzfeld, Stephen G Lisberger

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in Journal of neurophysiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
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  3. Strategies to Decipher Neuron Identity from Extracellular Recordings in Behaving Nonhuman Primates.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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  16. Rate versus synchrony codes for cerebellar control of motor behavior.bioRxiv : the preprint server for biology · 2023
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  17. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Nathan J HallDepartment of Neurobiology, Duke University School of Medicine, Durham, North Carolina.ORCID 0000-0003-3690-7395
David J HerzfeldDepartment of Neurobiology, Duke University School of Medicine, Durham, North Carolina.ORCID 0000-0001-9910-0658
Stephen G LisbergerDepartment of Neurobiology, Duke University School of Medicine, Durham, North Carolina.ORCID 0000-0001-7859-4361
Duke University · US

Funding

Canonical computations for motor learning by the cerebellar cortex micro-circuitR01NS112917 · NINDS · DUKE UNIVERSITY · PI LISBERGER, STEPHEN G, MEDINA, JAVIER F · 2019 to 2023
$6.4M
Neural Basis for Cerebellar Motor LearningR01NS092623 · NINDS · DUKE UNIVERSITY · PI LISBERGER, STEPHEN G · 2016 to 2020
$2.5M
Principles of operation of a neural learning circuitK99EY030528 · NEI · DUKE UNIVERSITY · PI HERZFELD, DAVID JAMES · 2020 to 2021
$213k
NEI NIH HHS K99 EY030528NINDS NIH HHS R01 NS092623NINDS NIH HHS R01 NS112917
6 · The paper itself

Abstract

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth data sets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth data sets and suggest postsorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses.

Indexed as

ElectrodiagnosisNeurophysiologySignal Processing, Computer-AssistedAction PotentialsAnimalsCerebellumElectrodes, ImplantedNeuronsbinary pursuitcerebellummulti-contact electrodesspike sorterspike sorting

Identifiers

PMID34788137
PMCPMC8715051
OpenAlexW3212905967

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.