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Action Potential

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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.
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Related Experiment Video

Updated: Aug 11, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

A primer on entropy in neuroscience.

Erik D Fagerholm¹, Zalina Dezhina¹, Rosalyn J Moran¹

¹Department of Neuroimaging, King's College London, United Kingdom.

Neuroscience and Biobehavioral Reviews

|February 3, 2023

Summary

Entropy quantifies hidden information in systems due to observer limitations. This statistical mechanics approach uses neural models to explain entropy

Keywords:

Entropy Information theory Neuroscience Statistical mechanics

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Area of Science:

Statistical mechanics
Neuroscience
Information theory

Background:

Entropy is traditionally viewed as a system property.
Observer limitations can influence perceived entropy.
Understanding entropy is crucial for interpreting complex systems like the brain.

Purpose of the Study:

To provide a first-principles account of entropy in statistical mechanics.
To link entropy to hidden information in neural systems.
To explain how neuroimaging limitations affect entropy calculations.

Main Methods:

Utilized toy models of neural systems with binary-state neurons.
Described microstates and macrostates for discrete and continuous systems.
Applied coarse-graining operations in phase space to simulate neuroimaging resolution limits.

Main Results:

Defined entropy as a measure of hidden information arising from observer limitations.
Derived the mathematical form of entropy from neuronal rearrangements.
Demonstrated that limitations in neuroimaging resolution increase entropy over time, consistent with the second law of thermodynamics.

Conclusions:

Entropy is fundamentally linked to the observer's information.
The study provides a framework for understanding entropy in neural systems.
This work supports the use of entropy in analyzing neuroimaging data and inferring brain states.