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

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.2K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
4.2K
Action Potential01:14

Action Potential

12.3K
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...
12.3K
Integration of Synaptic Events01:28

Integration of Synaptic Events

5.8K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
5.8K
Propagation of Action Potentials01:23

Propagation of Action Potentials

13.8K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
13.8K

You might also read

Related Articles

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

Sort by
Same author

A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data.

PLOS digital health·2026
Same author

Cardio-kidney-metabolic overlap in patients with severe heart failure: Data from the HELP-HF registry.

American heart journal·2026
Same author

Synergistic Short-Term Synaptic Plasticity Mechanisms for Working Memory.

Journal of cognitive neuroscience·2026
Same author

Effective correction of extreme capacitive artifacts in TMS-EEG via windowed detrending.

Journal of neural engineering·2026
Same author

Trip Detection Algorithms for Healthy and Amputee Individuals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

EEG recordings during visuo-attentive task reduce sex bias in Alzheimer's disease diagnosis.

Alzheimer's & dementia (New York, N. Y.)·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Mar 28, 2026

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

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

Published on: November 30, 2017

12.9K

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

Alberto Mazzoni1,2, Henrik Lindén3,4, Hermann Cuntz5,6,7

  • 1The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy.

Plos Computational Biology
|December 15, 2015
PubMed
Summary
This summary is machine-generated.

Researchers developed a simple formula to estimate the local field potential (LFP) from Leaky Integrate-and-Fire (LIF) network simulations. This method accurately predicts LFP signals, bridging computational models and in vivo recordings.

More Related Videos

Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.3K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.3K

Related Experiment Videos

Last Updated: Mar 28, 2026

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

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

Published on: November 30, 2017

12.9K
Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.3K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.3K

Area of Science:

  • Computational Neuroscience
  • Neural Modeling
  • Neurophysiology

Background:

  • Leaky Integrate-and-Fire (LIF) models simulate neural spiking dynamics.
  • Local Field Potentials (LFPs) measure mass neuronal activity in vivo.
  • LFPs cannot be directly computed from point-neuron LIF models.

Purpose of the Study:

  • To find the best approximation for predicting LFPs from LIF network models.
  • To develop a method for comparing computational models with experimental LFP data.

Main Methods:

  • Compared LFP predictions from LIF network outputs (firing rates, potentials, currents) with ground-truth LFPs.
  • Used a 3D multi-compartmental neuron network model with realistic morphology and spatial distributions.
  • Injected LIF network synaptic input currents into the 3D model to obtain ground-truth LFPs.

Main Results:

  • A specific linear combination of LIF synaptic currents accurately predicted LFPs.
  • This proxy accounted for most of the LFP time course variance in the 3D network.
  • The proxy performed well across various neuronal morphologies and conditions.

Conclusions:

  • A simple formula estimates LFPs from LIF network simulations when a single pyramidal population dominates.
  • Facilitates quantitative comparison between computational models and in vivo LFP recordings.
  • Enhances the utility of LIF models for studying neural dynamics.