Jieqiong Xu

In this paper, a three-dimensional subspace method is proposed, in which the search direction is generated by minimizing the approximation model of the objective function in a three-dimensional subspace. The approximation model of the objective function is not unique, and alternatives can be chosen between a symmetric quadratic model and a conic model by specific criteria. Moreover, the idea of a WLY conjugate gradient method is applied to characterize the change of gradient direction between adjacent iteration points. The strategy of initial stepsize and nonmonotone line search are adopted, and the global convergence of the presented algorithm is established under mild assumptions. In numerical experiments, we use a collection of 80 unconstrained optimization test problems to show the competitive performance of the presented method.

In general, inhibitory stimuli are thought to inhibit neuronal firing, but they may actually enhance firing sometimes, such as post-inhibitory rebound spike (PIR spike) and post-inhibitory facilitation (PIF) phenomena, which play an important role in human neuronal activities. We study responses to inhibitory pulse in a classical neuron model (Quartic adaptive Integrate-and-fire model) well known to reproduce a number of biologically realistic behaviors. The three phenomena that we study are PIR, in which a neuron fires after an inhibitory pulse, and PIF, in which a subthreshold excitatory input can induce a spike if it is applied with proper timing after an inhibitory pulse, as well as period firing after inhibitory pulse. When the system features focus and saddle two equilibriums, the three phenomena will be occurred under the inhibitory pulse, while all three phenomena will not be induced when the system features node and saddle two equilibriums. Using dynamical systems theory, we explain the threshold mechanism of enhancement of neural firing response induced by inhibitory pulse and analyze the origin of these phenomena from several factors. We also describe the geometric characterization of dynamical structures of these three phenomena. This study therefore enrich the paradoxical phenomena that induced by inhibitory input and advance our understanding of its role.

Pre-Bötzinger complex (PBC) neurons located in mammalian brain are the necessary conditions to produce respiratory rhythm, which has been widely verified experimentally and numerically. At present, one of the two different types of bursting mechanisms found in PBC mainly depends on the calcium-activated of non-specific cation current (I_CaN). In order to study the influence of I_CaN and stimulus current I_exc in PBC inspiratory neurons, a single compartment model was simplified, and firing patterns of the model was discussed by using stability theory, bifurcation analysis, fast, and slow decomposition technology combined with numerical simulation. Under the stimulation of different somatic applied currents, the firing behavior of neurons are studied and exhibit multiple mix bursting patterns, which is helpful to further understand the mechanism of respiratory rhythms of PBC neurons.

We proposed a three-term gradient descent method that can be well applied to address the optimization problems in this article. The search direction of the obtained method is generated in a specific subspace. Specifically, a quadratic approximation model is applied in the process of generating the search direction. In order to reduce the amount of calculation and make the best use of the existing information, the subspace was made up of the gradient of the current and prior iteration point and the previous search direction. By using the subspace-based optimization technology, the global convergence result is established under Wolfe line search. The results of numerical experiments show that the new method is effective and robust.

Pre‐Bötzinger complex (PBC) is a necessary condition for the generation of respiratory rhythm. Due to the existence of synaptic gaps, delay plays a key role in the synchronous operation of coupled neurons. In this study, the relationship between synchronization and correlation degree is established for the first time by using ISI bifurcation and correlation coefficient, and the relationship between synchronization and correlation degree is discussed under the conditions of no delay, symmetric delay, and asymmetric delay. The results show that the phase synchronization of two coupling PBCs is closely related to the weak correlation, that is, the weak phase synchronization may occur under the condition of incomplete synchronization. Moreover, the time delay and coupling strength are controlled in the modified PBC network model, which not only reveals the law of PBC firing transition but also reveals the complex synchronization behavior in the coupled chaotic neurons. Especially, when the two coupled neurons are nonidentical, the complete synchronization will disappear. These results fully reveal the dynamic behavior of the PBC neural system, which is helpful to explore the signal transmission and coding of PBC neurons and provide theoretical value for further understanding respiratory rhythm.

The stability of grazing bifurcation is lost in three ways through the local analysis of the near-grazing dynamics using the classical concept of discontinuity mappings in the two-degree-of-freedom vibroimpact system with symmetrical constraints. For this instability problem, a control strategy for the stability of grazing bifurcation is presented by controlling the persistence of local attractors near the grazing trajectory in this vibroimpact system with symmetrical constraints. Discrete-in-time feedback controllers designed on two Poincare sections are employed to retain the existence of an attractor near the grazing trajectory. The implementation relies on the stability criterion under which a local attractor persists near a grazing trajectory. Based on the stability criterion, the control region of the two parameters is obtained and the control strategy for the persistence of near-grazing attractors is designed accordingly. Especially, the chaos near codimension-two grazing bifurcation points was controlled by the control strategy. In the end, the results of numerical simulation are used to verify the feasibility of the control method.

In this paper, a one-parameter class of three-term conjugate gradient methods is proposed for solving large-scale unconstrained optimization problem. Based on eigenvalue study, an adaptive choice for the embedded parameter and the corresponding three-term conjugate gradient method is obtained. In addition, the convergence properties of the obtained method are studied for both convex and general function. Numerical experiments show that the proposed method has efficient and robust performance.