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

Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

460
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
460

You might also read

Related Articles

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

Sort by
Same author

Commentary: Cost Savings Are Possible, But Success Is Not Guaranteed: Challenges of Bundled Payment in Canada.

Healthcare policy = Politiques de sante·2026
Same author

Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.

Nature communications·2026
Same author

Temporal interference stimulation of peripheral nerves induces functionally diverse limb movements revealed by automated pose estimation and unsupervised behavioral analysis.

Journal of neuroengineering and rehabilitation·2025
Same author

Energy-Efficient Adaptive Neural Stimulator With Waveform Prediction by Sub-Threshold Interrogation of the Electrode-Tissue Interface.

IEEE transactions on biomedical circuits and systems·2025
Same author

How much data do you need? An analysis of pelvic multi-organ segmentation in a limited data context.

Physical and engineering sciences in medicine·2025
Same author

The inherent adversarial robustness of analog in-memory computing.

Nature communications·2025
Same journal

Ultra-flexible wireless endovascular stimulator for cortical simulation.

Journal of neural engineering·2026
Same journal

Influence of frequency and pulse train duration on respiratory responses during transcutaneous phrenic nerve stimulation in humans.

Journal of neural engineering·2026
Same journal

Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization.

Journal of neural engineering·2026
Same journal

Unveiling subject-specific causal latency in motor imagery: a physiologically transparent BCI via Riemannian tangent space fusion.

Journal of neural engineering·2026
Same journal

Cross-subject decoding of human neural data for speech Brain Computer Interfaces.

Journal of neural engineering·2026
Same journal

Cognitive and brain function enhancement in Gen X group after personalized, AI supervised EEG-neurofeedback training.

Journal of neural engineering·2026
See all related articles

Related Experiment Video

Updated: Aug 5, 2025

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

11.2K

Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey.

Tim Zhang1, Mostafa Rahimi Azghadi2, Corey Lammie2

  • 1Department of Bioengineering, McGill University, Montreal H3A 0E9, Canada.

Journal of Neural Engineering
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

Spike sorting techniques are advancing with new algorithms and hardware, crucial for brain machine interfaces and neuroscience research. Co-optimizing hardware and algorithms is key for resource-constrained applications like wearable devices.

Keywords:
hardwaremachine learningneuromorphic engineeringspike sorting

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K
Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter
07:37

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

Published on: February 13, 2014

11.1K

Related Experiment Videos

Last Updated: Aug 5, 2025

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

11.2K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K
Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter
07:37

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

Published on: February 13, 2014

11.1K

Area of Science:

  • Neuroscience
  • Neural Engineering

Background:

  • Spike sorting analyzes neural recordings to identify individual neuron activity.
  • Advances in microelectrode arrays enable simultaneous recording of thousands of neurons.
  • Efficient spike sorting is vital for brain machine interfaces, prosthetics, and neurological monitoring.

Purpose of the Study:

  • Review recent advancements in spike sorting algorithms and hardware.
  • Identify optimal algorithm-hardware combinations for real-world applications.
  • Provide a roadmap for future spike sorting implementations in resource-constrained environments.

Main Methods:

  • Literature review of spike sorting hardware and algorithms.
  • Analysis of algorithm-hardware combinations and their applicability.
  • Discussion of challenges and future opportunities in spike sorting.

Main Results:

  • Shift from traditional '3-step' spike sorting to template matching and machine learning.
  • Exploration of innovative hardware: ASICs, FPGAs, and IMCs.
  • Identification of key considerations for co-designing neural recording systems.

Conclusions:

  • Co-optimization of hardware and algorithms is essential for resource-constrained spike sorting.
  • This review serves as a guide for selecting appropriate spike sorting techniques.
  • Advancements facilitate novel applications and progress in neural engineering research.