Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Integration of Synaptic Events

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...

Graded Potential

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...

You might also read

Related Articles

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

Sort by

Same author

A Roadmap to Navigate the Future of Neural Engineering.

Journal of neural engineering·2026

Same author

Listening to the sound of your own brain waves enhances sleep quality and quantity.

Sleep medicine·2025

Same author

Engineering human stellate cells for beta cell replacement therapy promotes <i>in vivo</i> recruitment of regulatory T cells.

Materials today. Bio·2020

Same author

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models.

Journal of neurophysiology·2019

Same author

Pacemaker-Induced Coherence in Cortical Networks.

Neural computation·2019

Same author

Protective effects of agomelatine on testicular damage caused by bortezomib.

Biotechnic & histochemistry : official publication of the Biological Stain Commission·2017

Same journal

Gating effects of a Cav2.3 calcium channel variant linked to developmental and epileptic encephalopathy.

Neuroscience·2026

Same journal

Identification of polyamine metabolism-related gene signatures for prognostic and immunological stratification in glioma.

Neuroscience·2026

Same journal

Age- and sex-dependent effects of single prolonged stress on behavioral, hematological, and oxidative stress parameters in C57BL/6J mice.

Neuroscience·2026

Same journal

Accelerated long-term forgetting and relevant biomarkers for early detection in preclinical Alzheimer's disease.

Neuroscience·2026

Same journal

Deciphering key factors contributing to age-related decline in visuomotor tracking through the manipulation of target refresh rate and gaze contingency.

Neuroscience·2026

Same journal

LPI alleviates Alzheimer's disease pathology via the GPR55 receptor.

Neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 27, 2026

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Published on: May 16, 2013

Extracting synaptic conductances from single membrane potential traces.

M Pospischil¹, Z Piwkowska, T Bal

¹Integrative and Computational Neuroscience Unit (UNIC), UPR-2191, CNRS, Bat. 32-33, 1 Avenue de la Terrasse, 91198 Gif-sur-Yvette, France.

|November 26, 2008

Summary

This summary is machine-generated.

Researchers developed a new method to estimate synaptic conductances from single neuronal recordings. This technique, the VmT method, analyzes membrane potential (Vm) fluctuations to reveal complex neural activity patterns crucial for brain function.

More Related Videos

Evaluation of Synaptic Multiplicity Using Whole-cell Patch-clamp Electrophysiology

Evaluation of Synaptic Multiplicity Using Whole-cell Patch-clamp Electrophysiology

Published on: April 23, 2019

Related Experiment Videos

Last Updated: Jun 27, 2026

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Published on: May 16, 2013

Evaluation of Synaptic Multiplicity Using Whole-cell Patch-clamp Electrophysiology

Evaluation of Synaptic Multiplicity Using Whole-cell Patch-clamp Electrophysiology

Published on: April 23, 2019

Area of Science:

Neuroscience
Computational Neuroscience

Background:

Cortical neuronal activity is complex, often exhibiting irregular firing patterns.
Intracellular recordings capture information from thousands of neurons, reflecting synaptic activity.
Estimating synaptic conductances typically requires multiple membrane potential (Vm) levels, limiting single-trace analysis.

Purpose of the Study:

To develop a novel method for extracting excitatory and inhibitory synaptic conductances from single neuronal membrane potential (Vm) traces.
To enable the analysis of complex synaptic activity in scenarios where multiple Vm recordings are not feasible.

Main Methods:

The proposed "VmT method" utilizes maximum likelihood estimation.
It assumes synaptic conductances follow Gaussian stochastic processes and are integrated by a leaky membrane model.
The method was validated using computational models and in vitro dynamic-clamp experiments on guinea-pig visual cortex neurons.

Main Results:

Successfully extracted mean and variance of excitatory and inhibitory conductances from single Vm traces.
Demonstrated the method's efficacy in both simulated data and experimental recordings.
The VmT method provides a viable approach for analyzing synaptic dynamics from limited data.

Conclusions:

The VmT method offers a powerful tool for characterizing synaptic conductances using single-trial neuronal recordings.
This technique has significant potential for advancing in vivo neuroscience research by enabling detailed analysis of neural circuit function.