As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph （EEG） has potential clinical application in monitoring the acupuncture effects. In this paper, a method composed of the mutual information method and Lempel-Ziv complexity method （MILZC） is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs. In the experiments, eight subjects are manually acupunctured at ＇Zusanli＇ acupuncture point （ST-36） with different frequencies （i.e., 50, 100, 150, and 200 times/min） and the EEGs are recorded simultaneously. First, MILZC values are compared in general. Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies. Finally, significance index P values are used to study the spatiality of the acupuncture effect on the brain. Three main findings are obtained： （i） MILZC values increase during the acupuncture; （ii） manual acupunctures （MAs） with 100 times/rain and 150 times/min are more effective than with 50 times/min and 200 times/rain; （iii） contralateral hemisphere activation is more prominent than ipsilateral hemisphere＇s. All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.
Luo Xi-LiuWang JiangHan Chun-XiaoDeng BinWei Xi-LeBian Hong-Rui
The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture （MA） manipulations are taken at the ＇Zusanli＇ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval （ISI） statistical histogram is fitted by the gamma distribution, which has two parameters： one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate- independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture.
Neuronal networks in the brain exhibit the modular （clustered） property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization in a clustered neuronal network. A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons, while on the spiking time scale, they behave asynchronously. This synchronization transition can be induced by the variations of inter- and intra coupling strengths, as well as the probability of random links between different subnetworks. Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain, we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network, Simulation results show a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even in the presence of external driving. Hence, effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.
Manual acupuncture （MA）, as a mechanical action, can be equivalent to an external stimulus to the neural sys- ...
Chunxiao Han 1,2 ,Jiang Wang 3 , Shigang Cui 1,2 ,Li Zhao 1,2 , Yanqiu Che 1,2 1. Tianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin, 300222, China2. School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China3. School of Electrical and Automation Engineering, Tianjin University, Tianjin,300072, China
In this paper we present a combined algorithm for the synchronization control of two gap junction coupled chaotic FitzHugh-Nagumo （FHN） neurons in an external electrical stimulation. The controller consists of a combination of dynam- ical sliding mode control and adaptive backstepping control. The combined algorithm yields an adaptive dynamical sliding mode control law which has the advantage over static sliding mode-based controllers of being chattering-free, i.e., a suffi- ciently smooth control input signal is generated. It is shown that the proposed control scheme can not only compensate for the system uncertainty, but also guarantee the stability of the synchronized error system. In addition, numerical simulations are also performed to demonstrate the effectiveness of the proposed adaptive controller.
