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

Detection of spontaneous postsynaptic potentials

P J Franaszczuk1, G K Bergey, P Kudela

  • 1Maryland Epilepsy Center, Department of Neurology, University of Maryland School of Medicine and Medical Center, Baltimore 21201, USA.

Computers and Biomedical Research, an International Journal
|October 1, 1995
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

Analysis of damage localisation results in GFRP panel for different configurations of air-coupled transducers.

Ultrasonics·2023
Same author

Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles.

Journal of neural engineering·2018
Same author

Tumor lysate-loaded Bacterial Ghosts as a tool for optimized production of therapeutic dendritic cell-based cancer vaccines.

Vaccine·2018
Same author

Ictal propagation of high frequency activity is recapitulated in interictal recordings: effective connectivity of epileptogenic networks recorded with intracranial EEG.

NeuroImage·2014
Same author

Applying EEG phase synchronization measures to non-linearly coupled neural mass models.

Journal of neuroscience methods·2014
Same author

Comparing parametric and nonparametric methods for detecting phase synchronization in EEG.

Journal of neuroscience methods·2012
Same journal

Wavelet domain nonlinear filtering for evoked potential signal enhancement.

Computers and biomedical research, an international journal·2001
Same journal

Time-frequency analysis of the RT and RR variability to stratify hypertrophic cardiomyopathy patients.

Computers and biomedical research, an international journal·2001
Same journal

Controlled auxotonic twitch in papillary muscle: a new computer-based control approach.

Computers and biomedical research, an international journal·2001
Same journal

Controlling for chance agreement in the validation of medical expert systems with no gold standard: PNEUMON-IA and RENOIR revisited.

Computers and biomedical research, an international journal·2001
Same journal

Artificial neural networks compared to factor analysis for low-dimensional classification of high-dimensional body fat topography data of healthy and diabetic subjects.

Computers and biomedical research, an international journal·2000
Same journal

On the parallelization of linkmap from the LINKAGE/FASTLINK package.

Computers and biomedical research, an international journal·2000
See all related articles

This study introduces a new algorithm for detecting spontaneous postsynaptic potentials (PSPs) using signal derivatives. The method accurately identifies neural network activity changes in experimental and simulated data.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Postsynaptic potentials (PSPs) carry crucial information about synaptic events.
  • Detecting spontaneous PSPs is essential for understanding neural network dynamics.
  • Current methods may require refinement for accurate and efficient detection.

Purpose of the Study:

  • To develop and validate a novel algorithm for detecting spontaneous postsynaptic potentials (PSPs).
  • To assess the algorithm's performance using both simulated and experimental neural data.
  • To provide a tool for investigating dynamic changes in neural network activity.

Main Methods:

  • Algorithm development based on computing approximations of first and second derivatives of recorded signals.

Related Experiment Videos

  • Testing the algorithm on computer-simulated postsynaptic potentials.
  • Validation using experimental data from dissociated mouse spinal cord neurons in tissue culture.
  • Evaluation of detection performance using receiver operating characteristics (ROC) analysis.

    • Main Results:

    • The algorithm successfully detects spontaneous PSPs in both simulated and experimental datasets.
    • Derivative-based signal analysis provides an effective approach for PSP detection.
    • Receiver operating characteristics confirmed the algorithm's detection capabilities.
    • The method demonstrates robustness in identifying synaptic events.

    Conclusions:

    • The developed algorithm offers a reliable method for detecting spontaneous PSPs.
    • This technique can be applied to study dynamic alterations in neural network function.
    • The findings contribute to advancing computational neuroscience tools for analyzing neural data.