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

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

The Role of Ion Channels in Neuronal Computation

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....

You might also read

Related Articles

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

Sort by

Same author

Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.

Nature communications·2026

Same author

AI Agents as Universal Task Solvers.

Entropy (Basel, Switzerland)·2026

Same author

Brain-computer interface control with artificial intelligence copilots.

Nature machine intelligence·2025

Same author

Neural basis of cooperative behavior in biological and artificial intelligence systems.

Science (New York, N.Y.)·2025

Same author

Inter-brain neural dynamics in biological and artificial intelligence systems.

Nature·2025

Same author

Voice EHR: introducing multimodal audio data for health.

Frontiers in digital health·2025

Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026

Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026

Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026

Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026

Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026

Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 25, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Redundant Information Neural Estimation.

Michael Kleinman¹, Alessandro Achille², Stefano Soatto³

¹Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA.

Entropy (Basel, Switzerland)

|August 6, 2021

Summary

This summary is machine-generated.

We developed the Redundant Information Neural Estimator (RINE) for efficiently estimating shared information. This method works for complex, continuous data, unlike prior approaches limited to small, discrete datasets.

Keywords:

Partial Information Decomposition redundant information usable information

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Related Experiment Videos

Last Updated: Oct 25, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

Information theory
Machine learning
Computational neuroscience

Background:

Estimating shared information is crucial for understanding complex systems.
Existing methods for information decomposition are limited to discrete variables and small datasets.
High-dimensional and continuous data present significant challenges for information estimation.

Purpose of the Study:

To introduce an efficient method for estimating redundant information.
To extend information decomposition to high-dimensional and continuous variables.
To demonstrate the applicability of the new method in practical tasks.

Main Methods:

Introduced the Redundant Information Neural Estimator (RINE).
Recast existing definitions of redundant information as an optimization problem over functions.
Developed an approach to approximate redundant information for high-dimensional and continuous predictors by optimizing over functions.

Main Results:

RINE allows efficient estimation of redundant information.
The function optimization approach overcomes limitations of previous methods.
Successfully applied RINE to high-dimensional image classification and motor-neuroscience tasks.

Conclusions:

RINE provides a scalable and efficient solution for estimating redundant information.
The method enables novel analyses in domains with complex data.
This work advances the field of information decomposition and its applications.