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 Experiment Videos

An efficient algorithm for continuous time cross correlogram of spike trains.

Il Park1, António R C Paiva, Thomas B Demarse

  • 1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA. memming@cnel.ufl.edu

Journal of Neuroscience Methods
|December 7, 2007
PubMed
Summary
This summary is machine-generated.

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

Human-AI Cooperation in Healthcare and Rehabilitation.

Delaware journal of public health·2026
Same author

Training sparse convolutional deep predictive coding networks with attention.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

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

Nature computational science·2026
Same author

The Conditional Cauchy-Schwarz Divergence With Applications to Time-Series Data and Sequential Decision Making.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

A Standard-Cell-Based Neuro-Inspired Integrate-and-Fire Analog-to-Time Converter for Biological and Low-Frequency Signals - Comparison With Analog Version.

IEEE transactions on biomedical circuits and systems·2024
Same author

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction.

IEEE transactions on cybernetics·2024
Same journal

Pupil-DLC: an open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.

Journal of neuroscience methods·2026
Same journal

Time as the language of Behavior: events, sequences, patterns and meanings.

Journal of neuroscience methods·2026
Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026
Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026
Same journal

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Journal of neuroscience methods·2026
Same journal

Methods for measuring neural activity during voluntary wheel running.

Journal of neuroscience methods·2026
See all related articles

We developed an efficient algorithm to detect temporal relationships between spike trains by computing a smoothed correlogram. This continuous-time method precisely identifies effective delays, improving upon traditional histogram-based approaches.

Area of Science:

  • Computational Neuroscience
  • Signal Processing

Background:

  • Accurate detection of temporal relationships between neuronal spike trains is crucial for understanding neural circuit function.
  • Conventional methods often rely on histogram-based correlogram estimations, which can lack precision due to time binning.
  • Advancements in recording technologies provide higher temporal resolution of spike times, necessitating improved analysis techniques.

Purpose of the Study:

  • To introduce an efficient, continuous-time algorithm for computing smoothed correlograms.
  • To enhance the precision of detecting effective delays between two spike trains.
  • To leverage high-resolution spike timing data for more accurate neural analysis.

Main Methods:

  • Developed a novel algorithm for smoothed correlogram computation operating in continuous time.

Related Experiment Videos

  • Avoided binning of spike trains and the correlogram itself.
  • Utilized a Laplacian kernel for efficient smoothing and computation.
  • Provided statistical properties of the estimator and guidance on kernel size selection.

    • Main Results:

    • The proposed algorithm offers more precise detection of effective delays compared to histogram-based methods.
    • The continuous-time approach effectively utilizes the high temporal resolution of modern spike recording techniques.
    • Demonstrated the algorithm's efficacy in estimating effective delays in synthetic data and real neuronal recordings.

    Conclusions:

    • The efficient smoothed correlogram algorithm provides a precise and advantageous method for analyzing temporal relationships in spike trains.
    • This technique enhances the ability to uncover neural communication dynamics by accurately measuring inter-spike train delays.
    • The algorithm is well-suited for current high-resolution neural recording data and offers practical guidelines for implementation.