Related Experiment Video
Updated: Apr 4, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
Published on: May 8, 2021
Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control
Wei Zhang1, Chuandong Li1, Tingwen Huang2
1College of Computer Science, Chongqing University, Chongqing 400044, PR China.
Abstract:
In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper.
Related Concept Videos
Neural Regulation
Time-Domain Interpretation of PD Control
Consider the example of control of motor torque. Initially, a positive...
Long-term Potentiation
Long-term Potentiation
Hebbian LTP
LTP can occur when...
Muscle Stimulation Frequency
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
Neural Circuits
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...

