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

You might also read

Related Articles

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

Sort by
Same author

[Predictors of short-term antimicrobial treatment outcomes in patients with liver failure complicated by spontaneous bacterial peritonitis: A retrospective study].

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences·2026
Same author

Evaluation of Gd-EOB-DTPA MRI With Diffusion and Clinicopathologic Features for Predicting Microvascular Invasion in Hepatocellular Carcinoma.

Journal of computer assisted tomography·2026
Same author

Diagnosis and treatment strategies for airway involvement in relapsing polychondritis: a comprehensive review.

Frontiers in immunology·2026
Same author

Investigating the mechanisms underlying saccade generation in the frontal eye fields using multisite microstimulation.

Journal of neurophysiology·2026
Same author

Vesicle3D: An Integrative Platform for 3D Segmentation and Analysis of Vesicles in Cryo-electron Tomograms.

Neuroscience bulletin·2026
Same author

Brain-wide arousal signals are segregated from movement planning in the superior colliculus of the macaque.

eLife·2026

Related Experiment Video

Updated: Mar 31, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

12.4K

Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

Pengcheng Zhou1, Shawn D Burton2, Adam C Snyder3

  • 1Program for Neural Computation, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.

Plos Computational Biology
|October 15, 2015
PubMed
Summary

Neural synchrony, or neurons firing together, can be explained by network oscillations. This study integrates oscillatory field potentials into a statistical framework to link oscillations with neural synchrony in simulations and recordings.

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.1K
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

9.6K

Related Experiment Videos

Last Updated: Mar 31, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

12.4K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.1K
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

9.6K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neurons often exhibit synchronized firing (synchrony) within milliseconds.
  • The causes of spike synchrony range from chance to specific neural computations or network activity.
  • Network oscillations are hypothesized to be a key mechanism for information flow and may drive neural synchrony.

Purpose of the Study:

  • To develop a statistical framework linking network-wide oscillations to neural spike synchrony.
  • To extend existing point process regression models to incorporate oscillatory field potentials.
  • To rigorously demonstrate the relationship between oscillatory field potentials and spike synchrony.

Main Methods:

  • Integrated oscillatory field potentials into a point process regression framework (generalized linear models).
  • Extended a prior model of spike-field association to analyze phase relationships.
  • Applied the framework to simulated neurons, in vitro hippocampal slices, and in vivo neocortical recordings.

Main Results:

  • Successfully recovered phase relationships between oscillatory field potentials and neuronal firing rates.
  • Demonstrated a statistically significant link between oscillatory field potentials and spike synchrony.
  • Validated the framework across different experimental preparations and recording types.

Conclusions:

  • The developed framework provides a rigorous method for statistically linking network oscillations and neural synchrony.
  • This approach advances our understanding of how network oscillations influence neural communication and computation.
  • The findings have implications for interpreting neural data and understanding information processing in the brain.