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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Drug Distribution: Volume of Distribution

Drug Distribution: Volume of Distribution

The volume of distribution refers to the theoretical volume necessary to contain the entire amount of an administered drug at the same concentration observed in the blood plasma. The body's intracellular fluid compartment, which makes up two-thirds of the total body water, is contrasted with the extracellular fluid compartment—comprising plasma and interstitial fluid—that accounts for one-third. The volume of distribution can vary depending on the characteristics of the drug.

F Distribution

F Distribution

The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...

Combinatorial Gene Control

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

You might also read

Related Articles

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

Sort by

Same author

Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences.

bioRxiv : the preprint server for biology·2026

Same author

Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment.

Neural computation·2026

Same author

On the control of recurrent neural networks using constant inputs.

IEEE transactions on automatic control·2026

Same author

Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention.

Nature communications·2026

Same author

Multi-timescale Computation by Astrocytes.

bioRxiv : the preprint server for biology·2026

Same author

Two views of the brain are reconciled by a unifying principle of maximal information processing.

bioRxiv : the preprint server for biology·2025

Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026

Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026

Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026

Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026

Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026

Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 8, 2026

Installation Method to Enhance Quality Control for Fiber Reinforced Polymer Spike Anchors

Installation Method to Enhance Quality Control for Fiber Reinforced Polymer Spike Anchors

Published on: April 10, 2018

Spiking networks as efficient distributed controllers.

Fuqiang Huang¹, ShiNung Ching²

¹The Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA. fuqiang@wustl.edu.

Biological Cybernetics

|June 29, 2018

Summary

This summary is machine-generated.

This study reveals how neural networks achieve functional objectives. Optimizing neuronal spiking for control leads to efficient, robust, event-based brain controllers where neurons dynamically adjust activity for performance.

Keywords:

Decoding Event-based control Neural networks Spiking networks

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Related Experiment Videos

Last Updated: Feb 8, 2026

Installation Method to Enhance Quality Control for Fiber Reinforced Polymer Spike Anchors

Installation Method to Enhance Quality Control for Fiber Reinforced Polymer Spike Anchors

Published on: April 10, 2018

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Area of Science:

Computational Neuroscience
Control Theory
Systems Neuroscience

Background:

Neural networks generate activity decoded into perceptions and actions.
Understanding how neural dynamics support this decoding is a key scientific challenge.
The functional objective of neuronal spiking dynamics remains largely unknown.

Purpose of the Study:

To examine neuronal dynamics using a control-theoretic framework.
To investigate if optimizing spike production can lead to functional objectives.
To understand the emergent network architecture and properties from such optimization.

Main Methods:

Postulated an objective where neuronal spiking activity is decoded into a control signal.
Applied a theoretical neuroscience principle to optimize spike production for reference tracking.
Analyzed the resulting recurrent network architecture and its dynamics.

Main Results:

Optimization resulted in a recurrent network with integrative neuronal dynamics.
The network functions as an efficient, distributed, event-based controller.
Individual neurons spike if it enhances tracking performance, demonstrating inherent robustness.

Conclusions:

Neuronal spiking dynamics can be optimized to achieve functional objectives like reference tracking.
This optimization naturally yields robust, distributed, event-based control architectures.
The findings provide a first-principles explanation for the functional role of neural dynamics.