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

Propagation of Action Potentials01:23

Propagation of Action Potentials

9.9K
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...
9.9K
Action Potential01:14

Action Potential

11.6K
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...
11.6K

You might also read

Related Articles

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

Sort by
Same author

Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations.

Frontiers in computational neuroscience·2026
Same author

Cross-population amplitude coupling in high-dimensional oscillatory neural time series.

Frontiers in computational neuroscience·2026
Same author

A Population Coupling Model Identifies Reduced Propagation from V1 to Higher Visual Areas During Locomotion.

bioRxiv : the preprint server for biology·2026
Same author

Deviation from Nash mixed equilibrium in repeated rock-scissors-paper reflect individual traits.

Scientific reports·2025
Same author

Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations.

bioRxiv : the preprint server for biology·2025
Same author

Oscillating neural circuits: Phase, amplitude, and the complex normal distribution.

The Canadian journal of statistics = Revue canadienne de statistique·2024

Related Experiment Video

Updated: Feb 21, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.9K

Inferring oscillatory modulation in neural spike trains.

Kensuke Arai1,2, Robert E Kass1,2,3

  • 1Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Plos Computational Biology
|October 7, 2017
PubMed
Summary

The Latent Oscillatory Spike Train (LOST) model analyzes neural oscillations and spike timing in brain recordings. This new framework reveals how neural firing rates are modulated by oscillations, even with weak signals.

More Related Videos

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.7K
Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.5K

Related Experiment Videos

Last Updated: Feb 21, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.9K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.7K
Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.5K

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Neurophysiology

Background:

  • Neural oscillations, such as electroencephalogram (EEG) and local field potential (LFP), are prevalent in brain activity.
  • Single neuron spiking often synchronizes with global oscillations, but analyzing this spike-field coherence is challenging due to irregular firing patterns and stimulus-independent modulations.
  • Existing methods struggle to capture complex temporal dynamics in spike trains, especially under varied experimental conditions.

Purpose of the Study:

  • To introduce a flexible point-process framework, the Latent Oscillatory Spike Train (LOST) model, for analyzing neural spike trains.
  • To decompose instantaneous firing rates into biologically relevant factors including refractoriness, event-locked modulation, and trial-to-trial variability.
  • To investigate trial-to-trial variability in spike-field coherence with the LFP theta rhythm in rat motor cortex.

Main Methods:

  • Developed the Latent Oscillatory Spike Train (LOST) model, a point-process framework.
  • Incorporated spiking refractoriness, event-locked firing rate non-stationarity, and baseline offset into the model.
  • Included a stochastic oscillatory modulation term and a latent stochastic auto-regressive term to capture neural dynamics.

Main Results:

  • The LOST model successfully decomposes neural firing rates into key biological and behavioral factors.
  • Extended LOST model detected trial-to-trial variability in spike-field coherence between single neuron spikes and the theta rhythm.
  • LOST can detect oscillations even with low firing rates, weak modulation, and broad spectral peaks in the modulating signal.

Conclusions:

  • The LOST model provides a robust framework for studying oscillatory modulation in neural spike trains.
  • This approach enhances the understanding of how neural oscillations influence single neuron activity and spike-field coherence.
  • LOST offers a powerful tool for analyzing complex neural data across diverse experimental paradigms.