Narrow Results

Electromagnetic induction plays a key role in regulating the electrical activity, excitability, and bistable structure of neurons. In this paper, a discontinuous control strategy with membrane potential as the threshold is introduced to the HR neuron model under the influence of electromagnetic field (EMFN model), and then we establish a Filipov EMFN neuron model to realize the regulation effect of electromagnetic field on the neuron system. Specific work of this paper reads as follows: first, the existence and stability of equilibrium points of two subsystems are analyzed by using MatCont software; second, the bistable region and its internal mechanism are discussed in detail by two-parameter bifurcation analysis; third, the mechanism of bistability and a series of complex sliding mode dynamics including sliding segment and sliding bifurcations are further revealed with the help of the fast–slow variable dissection method; finally, based on Hamilton energy, the regulation of threshold on chaotic discharge in the Filippov EMFN neuron model is explored. The numerical simulation results show that the EMFN neuron model can produce the corresponding sliding limit cycle and sliding bursting behaviors under the influence of the threshold control strategy, meanwhile, the chaotic behavior of the new system can be controlled effectively within a certain range. The results provide ideas for controlling the effects of electromagnetic fields on the biological neuronal system and exploring the regulation mechanism of neurological diseases.

By applying nonlinear dynamics to the dense storage of information, we demonstrate how a single nonlinear dynamical element can store M items, where M is variable and can be large. This provides the capability for naturally storing data in different bases or in different alphabets and can be used to implement multilevel logic. Further we show how this method of storing information can serve as a preprocessing tool for (exact or inexact) pattern matching searches. Since our scheme involves just a single procedural step, it is naturally set up for parallel implementation and can be realized with hardware currently employed for chaos-based computing architectures.

Considering the existence of magnetic induction effect with different intensities in the process of subthreshold and suprathreshold oscillations of bioelectrical activities, a non-smooth feedback strategy for memristive current with time delay is proposed, and then a four-dimensional Filippov Hindmarsh–Rose (HR) neuron model is established. The local stability and bifurcation patterns of delayed subsystems are qualitatively analyzed. Accordingly, the discriminant formula for the direction and stability of periodic solutions generated by Hopf bifurcation is obtained on the center manifold. Importantly, the stability of subsystems has switching behavior, which is accompanied by abundant hidden electrical activities under the effect of time delay. The theoretical analysis clarifies that the proposed feedback strategy leads to complex sliding mode dynamics, including sliding segments, various equilibrium points and sliding bifurcations. Meanwhile, the analytical conditions for motions of grazing, sliding, and crossing are developed and verified based on the flow switching theory. Moreover, the mechanism and evolutive rule of the self-excited and hidden sliding electrical activities are revealed by the fast-slow variable dissection method. Finally, it is verified that the time delay can not only induce bistable structures composed of the quiescent state and periodic bursting, but also eliminate the hidden sliding dynamics.

In order to study AR model and wavelet decomposition's effects on EMG pattern recognition rate, we decided to analyze the EMG signal with the above two different methods and then compared the results. First, we detected the action period of collected EMG signals, so as to obtain effective data for analysis. Then extracted the feature of EMG signal with two methods of AR parameter model and wavelet decomposition. Enhanced the feature separability by taking the feature space dimensionality reduction method and identifying artificial neural network as a pattern classifier. Finally, given the experimental results. We can know that wavelet decomposition method has a higher recognition rate and has more application value from the experiment. What's more, the information fusion based AR model and wavelet decomposition has laid for the improvement of EMG pattern recognition rate.