Related Experiment Video

Updated: Apr 16, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

704

Synchronization in Networks of Linearly Coupled Dynamical Systems via Event-Triggered Diffusions

Wenlian Lu, Yujuan Han, Tianping Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |March 10, 2015
    PubMed
    Summary

    This study introduces event-triggered coupling for synchronizing dynamical systems. These novel strategies ensure synchronization is achievable, mirroring continuous coupling methods for improved efficiency.

    More Related Videos

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments05:19

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

    Published on: November 12, 2019

    7.7K
    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond08:08

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

    Published on: June 24, 2015

    12.2K

    Related Concept Videos

    Linear time-invariant Systems01:23

    Linear time-invariant Systems

    A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
    The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
    1.1K
    Transmission-Line Differential Equations01:26

    Transmission-Line Differential Equations

    Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
    Line Section Model
    A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured...
    1.1K
    Linear Approximation in Time Domain01:21

    Linear Approximation in Time Domain

    Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
    For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
    427
    BIBO stability of continuous and discrete -time systems01:24

    BIBO stability of continuous and discrete -time systems

    System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
    To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
    1.1K
    Linear Differential Equations01:27

    Linear Differential Equations

    The integrating factor method provides a systematic way to solve first-order linear differential equations, especially those that cannot be handled by separation of variables. This method is particularly useful in modeling time-dependent physical systems influenced by both constant inputs and resistive forces. A common example is the motion of a car subjected to a constant engine force while experiencing air resistance proportional to its velocity.In such scenarios, Newton’s second law...
    250
    Modeling with Differential Equations01:25

    Modeling with Differential Equations

    Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
    286