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

Long-term Potentiation01:35

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

You might also read

Related Articles

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

Sort by
Same author

GULP1 enhances GLUT4 translocation by counteracting ACAP1-ARF6 inhibition.

Nutrition & diabetes·2026
Same author

Associations between intrinsic capacity, subjectively perceived environmental support and functional status among rural disabled older adults in China: guided by the healthy ageing framework.

BMC public health·2026
Same author

Sparse component analysis: A method that uncovers separable computations within neural population activity.

Neuron·2026
Same author

Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.

Restorative neurology and neuroscience·2026
Same author

The Simons Collaboration on Ecological Neuroscience: Studying how the brain interacts with the world.

Neuron·2026
Same author

[Current status of intrauterine adhesion treatment and research progress in stem cell therapy].

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

Related Experiment Video

Updated: May 28, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

An L₁-regularized logistic model for detecting short-term neuronal interactions.

Mengyuan Zhao1, Aaron Batista, John P Cunningham

  • 1Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA. mez25@pitt.edu

Journal of Computational Neuroscience
|November 1, 2011
PubMed
Summary

A new L(1)-regularized logistic regression (L(1)L) method enhances detection of short-term neuronal interactions in multi-electrode recordings. This method offers improved sensitivity and specificity over traditional techniques for analyzing neural signal processing.

More Related Videos

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings
10:24

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

Published on: January 10, 2015

Multichannel Extracellular Recording in Freely Moving Mice
08:59

Multichannel Extracellular Recording in Freely Moving Mice

Published on: May 26, 2023

Related Experiment Videos

Last Updated: May 28, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings
10:24

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

Published on: January 10, 2015

Multichannel Extracellular Recording in Freely Moving Mice
08:59

Multichannel Extracellular Recording in Freely Moving Mice

Published on: May 26, 2023

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Data Analysis

Background:

  • Neuronal interactions are crucial for neural signal processing.
  • Existing methods for analyzing neural data lack sensitivity and specificity.
  • Multi-electrode recordings offer rich data for studying neuronal interactions.

Purpose of the Study:

  • To introduce a novel L(1)-regularized logistic regression (L(1)L) method for detecting short-term neuronal interactions.
  • To compare the performance of the L(1)L method against traditional analysis techniques like the covariogram.
  • To assess the L(1)L method's robustness and applicability to real-world neural data.

Main Methods:

  • Development of an L(1)-regularized logistic regression model (L(1)L) for analyzing neuronal spike train data.
  • Parameter estimation using a coordinate descent algorithm.
  • Optimal tuning parameter selection via a Bayesian Information Criterion.

Main Results:

  • The L(1)L method demonstrated superior sensitivity and specificity compared to the covariogram method in simulation studies.
  • The L(1)L method effectively detects both excitatory and inhibitory neuronal interactions, even with small magnitudes and high baseline firing rates.
  • The method shows robustness to partially observed neural networks and false positives can be reduced by thresholding.

Conclusions:

  • The L(1)L method provides a more sensitive and specific approach for detecting short-term neuronal interactions in multi-electrode recordings.
  • The L(1)L method is applicable to real neural data, revealing condition-dependent interactions in the monkey dorsal premotor cortex.
  • This advanced analysis technique aids in understanding complex neural signal processing.