Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Action Potential

Action Potential

Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...

You might also read

Related Articles

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

Sort by

Same author

A Roadmap to Navigate the Future of Neural Engineering.

Journal of neural engineering·2026

Same author

A generative spike prediction model using behavioral reinforcement for re-establishing neural functional connectivity.

Nature computational science·2026

Same author

Audio-induced medial prefrontal cortical dynamics enhances coadaptive learning in brain-machine interfaces.

Journal of neural engineering·2023

Same author

Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy.

Entropy (Basel, Switzerland)·2023

Same author

Long-Term Postoperative Pain Prediction Using Higher-Order Singular Value Decomposition of Intraoperative Physiological Responses: Prospective Cohort Study.

JMIR perioperative medicine·2022

Same author

Spike prediction on primary motor cortex from medial prefrontal cortex during task learning.

Journal of neural engineering·2022

Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026

Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026

Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026

Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026

Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026

Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 9, 2026

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

Extracting synchronized neuronal activity from local field potentials based on a marked point process framework.

Yifan Huang¹, Xiang Zhang¹, Xiang Shen¹

¹Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, People's Republic of China.

Journal of Neural Engineering

|August 3, 2022

Summary

This summary is machine-generated.

This study introduces a new method to extract brain signal features from local field potentials (LFPs) that accurately predict synchronized neuronal firing for brain-machine interfaces (BMIs). This approach enhances movement decoding accuracy for individuals with paralysis.

Keywords:

Hilbert transform4 local field potential (LFP)1 marked point process5 synchronized neuronal activity2 temporal-spatial features3

More Related Videos

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

Construction of Local Field Potential Microelectrodes for in vivo Recordings from Multiple Brain Structures Simultaneously

Construction of Local Field Potential Microelectrodes for in vivo Recordings from Multiple Brain Structures Simultaneously

Published on: March 14, 2022

Related Experiment Videos

Last Updated: Jun 9, 2026

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

Construction of Local Field Potential Microelectrodes for in vivo Recordings from Multiple Brain Structures Simultaneously

Construction of Local Field Potential Microelectrodes for in vivo Recordings from Multiple Brain Structures Simultaneously

Published on: March 14, 2022

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Brain-machine interfaces (BMIs) aim to restore motor function in paralysis by translating neural activity into commands.
Local field potentials (LFPs) offer a promising, stable signal for long-term BMIs compared to single neuronal spikes.
Extracting precise neuronal activity from LFPs, which reflect synchronized synaptic currents, remains a challenge for current methods.

Purpose of the Study:

To develop and validate a novel framework for extracting transient LFP neuromodulations correlated with synchronized neuronal activity.
To assess the utility of these extracted LFP features as a reliable data source for movement decoding in BMIs.

Main Methods:

A feature extraction and validation framework was designed using primary motor cortex LFP recordings from rats performing a lever-press task.
Key LFP frequency bands were selected, and a marked point process (MPP) methodology was employed to extract transient neuromodulations.
Validation involved correlating extracted LFP features with synchronized neuronal firing probabilities and evaluating decoding performance.

Main Results:

Gamma band (30-80 Hz) LFP neuromodulations showed significant correlation with synchronized neuronal firings.
The MPP method provided higher temporal resolution and fewer false alarms compared to traditional spectrogram methods.
Decoding performance using LFP-derived synchronized firings reached 90% accuracy, comparable to using entire neuronal ensembles.

Conclusions:

The proposed framework effectively extracts sparse LFP neuromodulations that accurately identify temporal synchronized neuronal spikes.
This LFP-based approach demonstrates high decoding performance, suggesting its potential as an effective modality for long-term BMI applications.
The findings highlight the value of LFPs for robust neural decoding in restorative neurotechnology.