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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: Apr 6, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

A novel framework for feature extraction in multi-sensor action potential sorting.

Shun-Chi Wu¹, A Lee Swindlehurst², Zoran Nenadic³

¹Department of Engineering and System Science, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC.

Journal of Neuroscience Methods

|July 19, 2015

Summary

This summary is machine-generated.

This study introduces a new Matched Subspace Detector (MSD) framework for action potential (AP) feature extraction. The MSD method enhances neural signal analysis by providing more discriminatory features for unsupervised AP sorting.

Keywords:

Dimensionality reduction Feature extraction Spike detection Spike sorting

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Last Updated: Apr 6, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

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Published on: February 10, 2017

Multi-layer Cortical Ca2+ Imaging in Freely Moving Mice with Prism Probes and Miniaturized Fluorescence Microscopy

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Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

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Published on: August 1, 2011

Area of Science:

Neuroscience
Computational Neuroscience

Background:

Extracellular recordings of multi-unit neural activity are crucial in neuroscience.
Action potential (AP) detection and classification are initial steps in analyzing neural recordings.
Feature extraction reduces data dimensionality and noise for efficient source clustering.

Purpose of the Study:

To introduce a novel framework for multi-sensor AP feature extraction.
To generalize standard single-sensor algorithms for improved neural data analysis.

Main Methods:

Proposed a novel framework using the Matched Subspace Detector (MSD).
MSD is a generalization of standard single-sensor algorithms for multi-sensor AP feature extraction.

Main Results:

The proposed MSD approach yielded discriminatory features for AP sorting.
Demonstrated promising results using both simulated and real AP recordings from locust antennal lobe.

Conclusions:

The MSD algorithm finds joint spatio-temporal feature vectors without needing forward models or AP templates.
The MSD approach offers more discriminatory features for unsupervised AP sorting applications.