Search

The excitatory autapse has been discovered recently by researchers in the pyramidal neurons, which could regulate the neuronal firing, while the spiking properties of the neuron with excitatory autapse (NWEA) have not been adequately studied. To further shorten such a gap, in this paper, a mathematical model is constructed for the NWEA to examine its spiking characteristics from two large scales: the external factors (which include the magnitude and location of the electrode current and the ambient temperature) and the internal factors (which consist of the location and maximum conductance of the excitatory autapse). The interspike interval (ISI), the spiking count (SC) and the spiking rhythm (SR) are used to analyse the spike trains. It is shown that the numerical results are coincident with the biological experiments, i.e. the excitatory autapse wholly enhances the bursting phenomenon and increases the neuronal responsiveness. In addition, as the maximum conductance of the excitatory autapse increases, the SC is gradually reduced but the SR is more active. With the variation of the current locations, the spike trains of the NWEA are more sensitive than those of the neuron without excitatory autapse. Furthermore, the ISI, SC and SR are all sensitive to the five types of parameters in various degrees. The results obtained in this paper may provide some instructions in regulating the firing of NWEA from the above five factors, and the proposed model for the NWEA provides a basis to perform more complex studies for the neurons with autapse.

This paper was concerned with the problem of filter design for the continuous-discrete system in the Takagi-Sugeno (T-S) fuzzy model. In a known finite frequency (FF) domain, an FF $ H_\infty $ performance was defined for the nonlinear continuous-discrete system. With the designed filter, sufficient conditions were then established for the filtering error system to be asymptotically stable and having a prescribed FF $ H_\infty $ performance. After that, a systematic method for the filter design was proposed. Finally, an example was provided to check effectiveness of the derived results.

Isoquinoline alkaloids represent an important class of molecules due to their broad range of pharmacology and clinical utility. Prospective development and use of these alkaloids as effective anticancer agents have elicited great interest. In this study, in order to reveal structure-activity relationship, we present the characterization of bioactive isoquinoline alkaloid-DNA triplex interactions, with particular emphasis on the sequence selectivity and preference of binding to the two types of DNA triplexes, by electrospray ionization mass spectrometry (ESI-MS) and various spectroscopic techniques. The six alkaloids, including coptisine, columbamine, epiberberine, berberrubine, jateorhizine, and fangchinoline, were selected to explore their interactions with the TC and TTT triplex DNA structures. Berberrubine, fangchinoline, coptisine, columbamine, and epiberberine have preference for TC rich DNA sequences compared to TTT rich DNA triplex based on affinity values in MS. The experimental results from different fragmentation modes in tandem MS, subtractive and hyperchromic effects in UV absorption spectra, fluorescence quenching and enhancement in fluorescence spectra, and strong conformational changes in circular dichroism (CD) hinted that the interaction between isoquinoline alkaloid-TC/TTT DNA had diverse mechanisms including at least two different binding modes: the electrostatic binding and the intercalation binding. Interestingly, columbamine, berberrubine, and fangchinoline can stabilize TTT triplex as inferred from optical thermal melting profiles, while it was not the case in TC triplex. These results provide new insights into binding of isoquinoline alkaloids to pyrimidine motif triplex DNA.

In this paper, the input-to-state stability (ISS), the pth moment exponential ISS and the stochastic input-to-state stability issues are investigated for the stochastic impulsive systems with time delays via the comparison principle method. Firstly, several general comparison principles in vector-version are proposed guaranteeing the existence, uniqueness and magnitude for solutions of the addressed system. Then based on these established comparison principles, the ISS-related properties are investigated for the stochastic impulsive delayed model. Finally, one example is given to illustrate effectiveness of the obtained results.

Great progress has been made in bifurcation control of systems described by ordinary differential equations. However, the control of Hopf bifurcation and Turing patterns is seldom reported in reaction-diffusion systems, which is formed by partial differential equations. In this paper, a hybrid control synthesis combining state feedback is firstly devised in the reaction-diffusion marine planktonic ecosystem. The Turing instability condition and Hopf bifurcation criterion are derived through carrying out the eigenvalue analysis of the controlled system. The numerical simulations show that the hybrid control strategy can not only suppress the formation of Turing patterns, but also delay or advance the Hopf bifurcation point. Therefore, the desired spatial dynamics behaviors can be generated by manipulate the control gain parameters, so as to achieve the purpose of maintaining the marine ecological balance.

This paper is devoted to the problems of exponential stability and stabilization for piecewise-homogeneous Markovian switching complex-valued neural networks with incomplete transition rates (TRs). Both the time-varying delays and the coefficient matrices are switched among finite modes governed by a piecewise-homogeneous Markov process, where the TRs of the two-level Markov processes are assumed to be time-varying during different intervals. On the basis of an appropriately chosen Lyapunov–Krasovskii functional, some mode-dependent sufficient conditions are presented to guarantee the unforced network to be exponentially mean-square stable. Then, by proposing certain mode-dependent state feedback controller, stabilization criteria are derived through strict mathematical proofs. At the end of the paper, numerical examples are provided to illustrate the effectiveness of the theoretical results.

In this paper, a new color image encryption algorithm based on a fractional-order hyperchaotic system is proposed. Firstly, four chaotic sequences are generated by a fractional-order hyperchaotic system. The parameters of such a system, together with the initial value, are regarded as the secret keys and the plain image is encrypted by performing the XOR and shuffling operations simultaneously. The proposed encryption scheme is described in detail with security analyses, including correlation analysis, histogram analysis, differential attacks, and key sensitivity analysis. Experimental results show that the proposed encryption scheme has big key space, and high sensitivity to keys properties, and resists statistical analysis and differential attacks, so it has high security and is suitable for color image encryption.

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.

This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM) neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.

In this paper, the exponential stability problems are addressed for a class of delayed Cohen-Grossberg neural networks which are also perturbed by some stochastic noises. By employing the Lyapunov method, stochastic analysis and some inequality techniques, sufficient conditions are acquired for checking the pth(p > 1) and the 1st moment exponential stability of the network. Finally, One example is given to show the effectiveness of the proposed results.