Wuyin Jin

Stochastic Synchronization and Aperiodic Stochastic Resonance of a Unidirectionally Coupled Single-mode Optical System

The variational method is applied to a unidirectionally coupled single-mode optical system for the general characteristics of aperiodic stochastic resonance (ASR) and stochastic synchronization in the linear response background. It is shown that ASR in the low-frequency domain exists in all stable cases by means of coherence function, but the stochastic synchronization in the weak noise level exists only when the drive subsystem is bistable or multistable. However, the curve of correlation coefficient vs. the noise intensity exhibits ASR-like behavior only when the drive subsystem is monostable. The relaxation property of the drive subsystem is found to be responsible for these different characteristics.

Synchronization in Two Uncoupled Chaotic Neurons

Using the membrane potential of a chaotic neuron as stimulation signal to synchronize two uncoupled Hindmarsh-Rose (HR) neurons under different initial conditions is discussed. Modulating the corresponding parameters of two uncoupled identical HR neurons, full synchronization is realized when the largest condition lyapunov exponent (LCLE) becomes negative at the threshold of stimulation strength. Computing the interspiks interval (ISI) sequence shows synchronized chaotic response of modulated neurons is different from stimulation signal. Modulating input currents of two uncoupled HR neurons with parameters mismatch, phase synchronization is obtained when the LCLEs of two systems change to both negative, and synchronized response of two systems is in phase synchronization with stimulation signal.

Study on Synchronization of Two Identical Uncoupled Neurons Induced by Noise

In this paper, noise-induced synchronization between two identical uncoupled neurons is investigated by using Hodgkin–Huxley (H − H) and FHN models with sinusoidal stimulations. The numerical results show that the value of the critical noise intensity for synchronizing two neurons is much less than the magnitude of mean size of the attractor of the original system, the deterministic dynamics structure of the original attractor is not swamped under noise, and the deterministic feature of the attractor of the original system is affected by noise slightly for FHN neurons. This finding is significantly different from the previous work (Phys Rev E 67:027201, 2003).

Prediction of Epileptic Seizure from Nonlinear Time Series of Recording

Prediction of epileptic seizure onset, even in a short-term, would provide with time for preventive treatment to minimally keep the risk of seizure and ultimately to improve quality of life. In the present work, the electrocorticogram (ECoG) recordings from Sprague Dawley rat are performed for reconstructing sliding segment firstly, then by means of cross-correlation integral, comparing the ECoG dynamics from sliding segments with a reference epoch (non epileptic). One minute before seizure, the similarity index between the reference and the test epoch approaches to around 1; during the epileptic seizure the index drops to low values and fluctuates. An anticipation time Ta is proposed based on these findings. This technique is helpful to predict epileptic seizure in real practice, and it is a potential and useful strategy in clinical application.

Controlling neuronal spike by ion channels with weak periodic perturbation

The effects of applying the weak periodic perturbation to the ion channels of Hodgkin-Huxley neuron are examined in this paper. Numerical results indicate that the weak perturbation to different ion channel results in totally different behavior of neuronal spike. And the weak periodic perturbation can also control the neuronal spike from period to parabolic bursting, from chaos to period.