Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Altered Lighting Conditions Elicit Sex-Specific Circadian Behaviors in Diurnal Grass Rats.

bioRxiv : the preprint server for biology·2026
Same author

Emergence of Task-Related Motor Cortical Dysfunction in Mice with Progressive Parkinsonism.

bioRxiv : the preprint server for biology·2026
Same author

Experience-dependent maturation of somatosensory parvalbumin interneurons during social development in prairie voles.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same author

Neurotype matching in monogamous rodents is modulated by early-life sleep experience.

bioRxiv : the preprint server for biology·2025
Same author

Developmental time course of social touch, parvalbumin interneurons, perineuronal nets and Mef2c expression reveals a sensitive period of somatosensory cortex development in prairie voles.

bioRxiv : the preprint server for biology·2025
Same author

Desynchronization Increased in the Synchronized State: Subsets of Neocortical Neurons Become Strongly Anticorrelated during NonREM Sleep.

eNeuro·2025

Related Experiment Video

Updated: Jul 4, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

SpikeCleaner: An Algorithm to Label Unit Quality After Automated Spike Sorting.

Diksha Zutshi, Daniil Berezhnoi, Anjesh Ghimire

    Biorxiv : the Preprint Server for Biology
    |July 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Automated spike sorting algorithms are imperfect. This study introduces SpikeCleaner, a semi-automated pipeline achieving 97% accuracy in classifying neuronal units, significantly improving data quality and scalability for large electrophysiology datasets.

    More Related Videos

    Introductory Analysis and Validation of CUT&RUN Sequencing Data
    04:58

    Introductory Analysis and Validation of CUT&RUN Sequencing Data

    Published on: December 13, 2024

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
    11:20

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

    Published on: June 2, 2014

    Related Experiment Videos

    Last Updated: Jul 4, 2026

    A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
    10:31

    A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

    Published on: February 10, 2017

    Introductory Analysis and Validation of CUT&RUN Sequencing Data
    04:58

    Introductory Analysis and Validation of CUT&RUN Sequencing Data

    Published on: December 13, 2024

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
    11:20

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

    Published on: June 2, 2014

    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Data Science

    Background:

    • Automated spike sorting algorithms are crucial for analyzing extracellular recordings but often produce inaccurate results, requiring time-consuming manual curation.
    • The increasing volume of data from high-density probes necessitates scalable and automated solutions for accurate neuronal unit identification.

    Purpose of the Study:

    • To develop a semi-automated curation pipeline, SpikeCleaner, to standardize and improve the quality labeling of neuronal units after automated sorting.
    • To reduce subjectivity and enhance the scalability of data curation for large electrophysiology datasets.

    Main Methods:

    • Developed a semi-automated pipeline that standardizes criteria for labeling units as 'Noise', 'Multi-Unit Activity' (MUA), or 'Good Units'.
    • Utilized a combination of spike rate, spike timing metrics, and waveform-based physiological features for classification.
    • Algorithm output was benchmarked against expert manual curation, achieving high accuracy and F1 scores.

    Main Results:

    • SpikeCleaner achieved 97% accuracy and a 92% F1 score in classifying Single Units (SU) compared to expert labels.
    • The algorithm demonstrated 97% accuracy and 92% F1 score for full-category agreement (SU, MUA, Noise).
    • Achieved 97% accuracy and 95% F1 score in distinguishing Neuronal from Non-Neuronal units.

    Conclusions:

    • SpikeCleaner provides a scalable and accurate method for semi-automated quality labeling of neuronal units, aiding downstream analyses.
    • The pipeline standardizes unit classification, reducing subjectivity and improving the reliability of electrophysiology data.
    • This tool supports, rather than replaces, expert judgment, offering a valuable aid for researchers dealing with large datasets.