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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...

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...

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Related Experiment Video

Updated: May 31, 2026

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

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

Published on: March 25, 2014

Neuronal spike train analysis in likelihood space.

Yousef Salimpour¹, Hamid Soltanian-Zadeh, Sina Salehi

¹School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

|July 9, 2011

Summary

This summary is machine-generated.

This study introduces a novel technique for analyzing neural spike trains, integrating both firing rate and temporal coding. This new method enhances stimulus discrimination by creating a more accurate representation of stimulus space.

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Last Updated: May 31, 2026

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Published on: May 29, 2017

Area of Science:

Computational Neuroscience
Systems Neuroscience

Background:

Conventional spike train analysis relies on firing rate, often neglecting temporal coding.
Integrating rate and temporal information in a single framework is a significant challenge.

Purpose of the Study:

To develop an innovative technique for spike train analysis that incorporates both rate and temporal information.
To improve the representation of stimulus space and neural discrimination.

Main Methods:

Utilized a point process modeling approach with an extended Kalman filter for parameter estimation.
Employed a marked point process strategy to extend analysis from single neurons to neuronal populations.
Transformed spike trains into binary vectors projected onto a likelihood space, integrating temporal and rate information.

Main Results:

The likelihood space integrates temporal and rate information, improving classifier performance.
Stimulus-specific information in the likelihood space serves as a distance metric.
Analysis of macaque monkey inferior temporal cortex neurons demonstrated improved performance compared to firing rate-based methods.

Conclusions:

The likelihood space provides a more accurate representation of stimulus space by integrating rate and temporal neural information.
This framework offers potential for understanding neuronal mechanisms in visual object categorization.