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...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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.

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

Simplified Synchronous Machine Model

Simplified Synchronous Machine Model

The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...

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

Combining machine learning and physiological network models for sepsis prediction.

Frontiers in network physiology·2026

Same author

A systematic delayed feedback control approach for unstable periodic orbits in chaotic systems with unknown parameters.

Chaos (Woodbury, N.Y.)·2025

Same author

Locality blended next-generation reservoir computing for attention accuracy.

Chaos (Woodbury, N.Y.)·2025

Same author

Optical chaotic signal recovery in turbulent environments using a programmable optical processor.

Light, science & applications·2025

Same author

Macroscopic Stochastic Model for Economic Cycle Dynamics.

Physical review letters·2025

Same author

Chaos from a free-running broad-area VCSEL.

Optics letters·2025

Search research articles

Related Experiment Video

Updated: May 13, 2026

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

Control of synchronization patterns in neural-like Boolean networks.

David P Rosin¹, Damien Rontani, Daniel J Gauthier

¹Department of Physics, Duke University, Durham, North Carolina 27708, USA. rosin@phy.duke.edu

Physical Review Letters

|March 26, 2013

Summary

This summary is machine-generated.

Synchronization patterns in excitable systems change with refractory time. Adjusting driver nodes locally controls these network dynamics, impacting biological neural networks.

More Related Videos

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Related Experiment Videos

Last Updated: May 13, 2026

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

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Area of Science:

Complex systems
Nonlinear dynamics
Network science

Background:

Excitable systems exhibit threshold dynamics.
Boolean networks model discrete state interactions.
Synchronization is crucial in coupled dynamical systems.

Purpose of the Study:

Investigate synchronization patterns in time-delayed directed Boolean networks.
Determine the effect of refractory time on network dynamics.
Identify control mechanisms for synchronization.

Main Methods:

Experimental study of Boolean networks.
Manipulation of refractory time in excitable systems.
Analysis of network dynamics and synchronization patterns.
Identification of driver nodes based on in-degree.

Main Results:

Refractory time influences cluster synchronization.
Synchronization transitions occur when refractory time matches link time delays.
Synchronization patterns are suppressed by refractory time heterogeneity.
Local control of synchronization is possible via driver nodes.

Conclusions:

Refractory time is a critical parameter for synchronization in these networks.
Driver nodes offer targeted control over network synchronization.
Findings inform understanding of synchronization in biological neural networks.