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

Action Potential

Action Potential

Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...

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...

Propagation of Action Potentials

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...

Postsynaptic Potential (PSP)

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...

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...

You might also read

Related Articles

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

Sort by

Same author

Recent progress and challenges in gene therapy for pharmacoresistant focal epilepsy.

Revue neurologique·2025

Same author

An adaptable, reusable, and light implant for chronic Neuropixels probes.

bioRxiv : the preprint server for biology·2023

Same author

Preparation of dissociated mouse primary neuronal cultures from long-term cryopreserved brain tissue.

Journal of neuroscience methods·2019

Same author

Changes in the severity and subtype of Guillain-Barré syndrome admitted to a specialist Neuromedical ICU over a 25 year period.

Journal of neurology·2017

Same author

Knockout of NMDA-receptors from parvalbumin interneurons sensitizes to schizophrenia-related deficits induced by MK-801.

Translational psychiatry·2016

Same author

Genetic neurological channelopathies: molecular genetics and clinical phenotypes.

Journal of neurology, neurosurgery, and psychiatry·2015

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

One-channel Cell-attached Patch-clamp Recording

One-channel Cell-attached Patch-clamp Recording

Published on: June 9, 2014

Applications of the expectation-maximization algorithm to quantal analysis of postsynaptic potentials.

¹University Laboratory of Physiology, Oxford, U.K.

Journal of Neuroscience Methods

|December 1, 1989

Summary

The expectation-maximization (EM) algorithm offers robust parameter estimation for noisy biological data. This study extends EM methods for synaptic transmission analysis, addressing noise and quantal variability challenges.

More Related Videos

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

Focal Macropatch Recordings of Synaptic Currents from the Drosophila Larval Neuromuscular Junction

Focal Macropatch Recordings of Synaptic Currents from the Drosophila Larval Neuromuscular Junction

Published on: September 25, 2017

Related Experiment Videos

Last Updated: Jul 10, 2026

One-channel Cell-attached Patch-clamp Recording

One-channel Cell-attached Patch-clamp Recording

Published on: June 9, 2014

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

Focal Macropatch Recordings of Synaptic Currents from the Drosophila Larval Neuromuscular Junction

Focal Macropatch Recordings of Synaptic Currents from the Drosophila Larval Neuromuscular Junction

Published on: September 25, 2017

Area of Science:

Computational Neuroscience
Biophysics
Statistical Modeling

Background:

The expectation-maximization (EM) algorithm is crucial for parameter estimation in incomplete data distributions.
Its application in neuroscience is vital for analyzing noisy postsynaptic potentials.
Existing EM methods require specific equations for different signal and noise conditions.

Purpose of the Study:

To enhance the utility of the expectation-maximization (EM) algorithm for analyzing synaptic transmission.
To develop generalized recursion equations applicable to various noise and signal conditions.
To incorporate constraints for modeling transmitter release variability.

Main Methods:

Developed generalized recursion equations for the EM algorithm.
Addressed non-Gaussian noise processes, quantal fluctuations, and quantal variability.
Incorporated binomial models for transmitter release.

Main Results:

The enhanced EM algorithm provides a unified approach for analyzing synaptic transmission under diverse conditions.
Demonstrated the algorithm's effectiveness in resolving trial-to-trial amplitude fluctuations in noisy recordings.
Monte Carlo simulations illustrated the advantages of the generalized methods.

Conclusions:

The generalized expectation-maximization (EM) algorithm offers a more versatile tool for neurophysiological data analysis.
This approach improves the estimation of synaptic parameters in the presence of complex noise and release patterns.
Facilitates a deeper understanding of synaptic transmission dynamics.