Related Experiment Video
Updated: Mar 28, 2026

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
Published on: March 8, 2024
Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by
Wenlian Lu1, Ren Zheng2, Tianping Chen3
1Department of Radiology, JinLing Hospital of Nanjing, China; Jinling Hospital-Fudan University Computational Translational Medicine Center, Centre for Computational Systems Biology, and School of Mathematical Sciences, Fudan University, China; School of Mathematics, Fudan University, 200433, Shanghai, China.
Abstract:
In this paper, we discuss outer-synchronization of the asymmetrically connected recurrent time-varying neural networks. By using both centralized and decentralized discretization data sampling principles, we derive several sufficient conditions based on three vector norms to guarantee that the difference of any two trajectories starting from different initial values of the neural network converges to zero. The lower bounds of the common time intervals between data samples in centralized and decentralized principles are proved to be positive, which guarantees exclusion of Zeno behavior. A numerical example is provided to illustrate the efficiency of the theoretical results.
Related Concept Videos
Sampling Continuous Time Signal
In the...
Sampling Methods: Overview
In analytical chemistry, the choice of...
Sampling Theorem
Random Sampling Method
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...
