Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Regulation01:37

Neural Regulation

34.8K
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.
34.8K
Electrostatic Boundary Conditions01:16

Electrostatic Boundary Conditions

1.2K
Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...
1.2K
Boundary Conditions for Current Density01:25

Boundary Conditions for Current Density

1.5K
Current density becomes discontinuous across an interface of materials with different electrical conductivities. The normal component of the current density is continuous across the boundary.
1.5K
Propagation of Action Potentials01:23

Propagation of Action Potentials

15.4K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
15.4K
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.1K
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
Multimachine Stability01:25

Multimachine Stability

698
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
698

You might also read

Related Articles

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

Sort by
Same author

State estimation for stochastic delayed neural networks with diffusion terms: A two-step estimation method.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Impulsive Control Under Event-Triggered Mechanism for Reaction-Diffusion Systems With Impulsive Disturbances.

IEEE transactions on cybernetics·2025
Same author

Asynchronous Boundary Stabilization of Stochastic Markovian Reaction-Diffusion Neural Networks With Mode-Dependent Delays.

IEEE transactions on neural networks and learning systems·2025
Same author

Relationship between Key Environmental Factors and the Architecture of Fruit Shape and Size in Near-Isogenic Lines of Cucumber (<i>Cucumis sativus</i> L.).

International journal of molecular sciences·2022
Same author

Should responder analyses be conducted on continuous outcomes?

Pharmaceutical statistics·2022
Same author

Low doses of fumonisin B1 exacerbate ochratoxin A-induced renal injury in mice and the protective roles of heat shock protein 70.

Chemico-biological interactions·2022

Related Experiment Video

Updated: May 4, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

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

Published on: June 24, 2015

11.5K

Observer-Based Asynchronous Boundary Stabilization for Stochastic Markovian Reaction-Diffusion Neural Networks.

Xin-Xin Han, Kai-Ning Wu, Xin Yuan

    IEEE Transactions on Cybernetics
    |August 23, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study stabilizes stochastic neural networks using an asynchronous boundary observer, even with parameter uncertainties and disturbances. The method ensures stability using only boundary measurements for robust control.

    More Related Videos

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

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

    Published on: March 2, 2015

    10.3K
    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
    05:19

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

    Published on: November 12, 2019

    7.0K

    Related Experiment Videos

    Last Updated: May 4, 2026

    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
    08:08

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

    Published on: June 24, 2015

    11.5K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

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

    Published on: March 2, 2015

    10.3K
    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
    05:19

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

    Published on: November 12, 2019

    7.0K

    Area of Science:

    • Control theory
    • Applied mathematics
    • Computational neuroscience

    Background:

    • Stochastic Markovian reaction-diffusion neural networks are complex systems susceptible to exogenous disturbances and parameter uncertainties.
    • Observer-based control is crucial for stabilizing such systems, but asynchronous operation presents unique challenges.
    • Boundary measurements offer a limited but potentially valuable data source for observer design.

    Purpose of the Study:

    • To develop an observer-based asynchronous boundary stabilization method for stochastic Markovian reaction-diffusion neural networks.
    • To address parameter uncertainties in the drift term and exogenous disturbances.
    • To design a nonfragile asynchronous observer-based boundary controller utilizing only boundary measurements.

    Main Methods:

    • Introduction of a hidden Markov model for asynchronous observer and system modes.
    • Design of a nonfragile asynchronous observer-based boundary controller.
    • Application of inequality techniques and stochastic analysis for stability criteria derivation.
    • Derivation of asynchronous boundary observer and controller gains.

    Main Results:

    • A sufficient criterion for input-to-state exponentially mean-square stability is established.
    • The asynchronous boundary observer/controller gains are explicitly derived.
    • The synchronous observer-based boundary stabilization is presented as a special case.
    • Numerical simulations validate the effectiveness of the proposed method.

    Conclusions:

    • The proposed asynchronous observer-based boundary stabilization is effective for stochastic Markovian reaction-diffusion neural networks with uncertainties.
    • The method demonstrates robustness against exogenous disturbances by utilizing boundary measurements.
    • The derived criteria and gains provide a practical framework for controller design and stability analysis.