Complete synchronization of three-layer Rulkov neuron network coupled by electrical and chemical synapses.

Chaos

School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, People's Republic of China.

Published: April 2024


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

This paper analyzes the complete synchronization of a three-layer Rulkov neuron network model connected by electrical synapses in the same layers and chemical synapses between adjacent layers. The outer coupling matrix of the network is not Laplacian as in linear coupling networks. We develop the master stability function method, in which the invariant manifold of the master stability equations (MSEs) does not correspond to the zero eigenvalues of the connection matrix. After giving the existence conditions of the synchronization manifold about the nonlinear chemical coupling, we investigate the dynamics of the synchronization manifold, which will be identical to that of a synchronous network by fixing the same parameters and initial values. The waveforms show that the transient chaotic windows and the transient approximate periodic windows with increased or decreased periods occur alternatively before asymptotic behaviors. Furthermore, the Lyapunov exponents of the MSEs indicate that the network with a periodic synchronization manifold can achieve complete synchronization, while the network with a chaotic synchronization manifold can not. Finally, we simulate the effects of small perturbations on the asymptotic regimes and the evolution routes for the synchronous periodic and the non-synchronous chaotic network.

Download full-text PDF

Source
http://dx.doi.org/10.1063/5.0177771DOI Listing

Publication Analysis

Top Keywords

synchronization manifold
16
complete synchronization
12
synchronization three-layer
8
three-layer rulkov
8
rulkov neuron
8
neuron network
8
chemical synapses
8
master stability
8
network
7
synchronization
6

Similar Publications

This article addresses the fixed-time leaderless cluster synchronization of spatiotemporal community networks (SCNs) characterized by nonidentical node dynamics and reaction-diffusion feature. First, a signed SCN with reaction-diffusion effect is formulated, where the sign-based coupling is introduced to capture the dynamics of coopetition interactions among different communities. Second, to ensure the invariance of the synchronous manifold, an improved interdegree balance condition is proposed as a prerequisite for achieving cluster synchronization of the community network.

View Article and Find Full Text PDF

HC-SPA: Hyperbolic Cosine-Based Symplectic Phase Alignment for Fusion Optimization.

Sensors (Basel)

August 2025

College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China.

In multimodal collaborative learning, the gradient dynamics of heterogeneous modalities face significant challenges due to the curvature heterogeneity of parameter manifolds and mismatches in phase evolution. Traditional Euclidean optimization methods struggle to capture the complex interdependencies between heterogeneous modalities on non-Euclidean or geometrically inconsistent parameter manifolds. Furthermore, static alignment strategies often fail to suppress bifurcations and oscillatory behaviors in high-dimensional gradient flows, leading to unstable optimization trajectories across modalities.

View Article and Find Full Text PDF

We present an optical technique for suppressing relaxation in alkali-metal spins using a single off-resonant laser beam. The method harnesses a physical mechanism that synchronizes Larmor precession in the two hyperfine manifolds, protecting magnetic coherence from relaxation caused by spin-exchange and other hyperfine-changing collisions. We experimentally demonstrate up to a ninefold reduction in decoherence of warm cesium vapor, achieving simultaneous protection from both spin-exchange relaxation and partial depolarization from coated cell walls.

View Article and Find Full Text PDF

Neural signals are high-dimensional, noisy, and dynamic, making it challenging to extract interpretable features linked to behavior or disease. We introduce , a framework that encodes neural activity as latent trajectories shaped by spatial and temporal structure. At each timepoint, signals are represented on a graph capturing spatial relationships, with a learnable attention mechanism highlighting important regions.

View Article and Find Full Text PDF

Adaptive finite-time synchronized control of multi-robotic fiber placement system with model uncertainties and disturbances.

ISA Trans

September 2025

School of Robotics, Hunan University, Changsha, 410082, Hunan, China; National Engineering Laboratory of Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, Hunan, China.

The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process.

View Article and Find Full Text PDF