Simulations of cochlear nucleus neural circuitry: excitatory-inhibitory response-area types I-IV.

Several circuitry schemes have been explored among model stellate and fusiform cochlear nucleus neurons in an effort to reproduce excitatory-inhibitory response-area (EIRA scheme) types I-IV. Single cell models incorporated known nonlinear membrane properties and spike-discharge characteristics, as described in previous modeling and intracellular recording. In addition, a unique method of implementing dendritic electrotonic distance processing was developed that provides greater computational efficiency, but with results similar to compartmental models. As an initial simple case, results were examined for a kHz pure tone. Auditory nerve (AN) population responses across characteristic frequencies from 200 Hz to 50 kHz based on actual single unit recordings were incorporated into the model as input. The findings and conclusions are (1) relatively simple inhibitory connections among stellate and fusiform cells, all of which receive AN excitatory inputs, can account for the salient features of EIRA-scheme types I-IV; (2) both types III and IV may be obtained using fusiform cells with small adjustments in the anatomical connections; (3) if stellate cells laterally inhibit their own neighbors, they can create inhibitory sidebands, but may have difficulty avoiding multiple sidebands; (4) in the model, type II cells are not responsive to broadband noise but rather to pure tones, and the reason for this was partly because the type II cells were inhibited by other CN units, and partly because the simulated AN fiber response to broadband noise was near their threshold; and (5) the type IV complex response areas may actually arise not necessarily because of elaborate circuitry, but as a result of a complex AN fiber population profile at high stimulus levels in conjunction with the type II inhibitory input to the type IV cells.     

A model of neural control of the heart rate

A simple model of heart rate regulation is proposed, using the assumption that the nervous system regulates the generation of pulses of the pacemaker. Previous values of intervals between heart beats (RR intervals) are used for this regulation, which is described by a nonlinear feedback loop with time delay. The conductance of the excitation in the heart is phenomenologically described by one nonlinear function (recovery curve). The model reproduces time series of RR intervals. Different known patterns of heart rate variability are observed, depending on the type of control and the parameter values.     

Learning and control with large dynamic neural networks

This paper is a presentation of neuronal control systems in the terms of the dynamical systems theory, where (1) the controller and its surrounding environment are seen as two co-dependent controlled dynamical systems (2) the behavioral transitions that take place under adaptation processes are analyzed in terms of phase-transitions. We present in the second section a generic method for the construction of multi-population random recurrent neural networks. The third section gives an overview of the various phase transitions that take place under an external forcing signal, or under internal parametric changes. The section 4 presents some applications in the domain of sequence identification and active perception modeling. The section 5 presents some applications in the domain of closed-loop control systems and reinforcement learning.     

Synchronization control of stochastic delayed neural networks

In this paper, synchronization control of stochastic neural networks with time-varying delays has been considered. A novel control method is given using the Lyapunov functional method and linear matrix inequality (LMI) approach. Several sufficient conditions have been derived to ensure the global asymptotical stability in mean square for the error system, and thus the drive system synchronize with the response system. Also, the estimation gains can be obtained. With these new and effective methods, synchronization can be achieved. Simulation results are given to verify the theoretical analysis in this paper.     

A chemical strategy to control the shape of oxide nanoparticles

A new strategy, epoxide-assisted precipitation route presented in this work, allows the shape control synthesis of Co₃O₄ nanoparticles. The shape of the nanoparticles is determined by the nature of the precursor cobalt salts (Co(NO₃)₂ · 6H₂O, CoCl₂ · 6H₂O) used for the preparation of the particles. The different reaction dynamics of the two salts in ethanolic and aqueous solutions with propylene oxide result in precursor particles with different structures, which lead to the formation of oxide nanoparticles with different shapes during the heat treatment. Spherical particles of about 20 nm are obtained from the ethanolic solution of Co(NO₃)₂ · 6H₂O; cubic-shaped particles of about 30 nm can be prepared from the ethanolic solution of CoCl₂ · 6H₂O; whereas platelet-like particles of more than 100 nm are synthesized from the aqueous solution of the mixture of Co(NO₃)₂ · 6H₂O and CoCl₂ · 6H₂O.     

Computational techniques for the analysis and design of dielectric-loaded plasmonic circuitry

The finite element and the beam propagation method, two widely used methods in photonics, are utilized for the analysis of plasmonic components based on the dielectric-loaded plasmonic waveguide. Two components are chosen as examples and are subsequently numerically investigated by employing the aforementioned methods, in order to demonstrate their applicability in plasmonics. Specifically, a microring resonator add-drop filter and a Mach–Zehnder interferometric switch are analyzed by means of the finite element and the beam propagation method, respectively. The formulation adopted is clearly presented in both cases and the case-dependent implementation details are thoroughly discussed.     

Phase synchronization of bursting neural networks with electrical and delayed dynamic chemical couplings

Diffusive electrical connections in neuronal networks are instantaneous, while excitatoryor inhibitory couplings through chemical synapses contain a transmission time-delay.Moreover, chemical synapses are nonlinear dynamical systems whose behavior can bedescribed by nonlinear differential equations. In this work, neuronal networks withdiffusive electrical couplings and time-delayed dynamic chemical couplings are considered.We investigate the effects of distributed time delays on phase synchronization of burstingneurons. We observe that in both excitatory and Inhibitory chemical connections, the phasesynchronization might be enhanced when time-delay is taken into account. This distributedtime delay can induce a variety of phase-coherent dynamical behaviors. We also study thecollective dynamics of network of bursting neurons. The network model presents theso-called Small-World property, encompassing neurons whose dynamics have two time scales(fast and slow time scales). The neuron parameters in such Small-World network, aresupposed to be slightly different such that, there may be synchronization of the bursting(slow) activity if the coupling strengths are large enough. Bounds for the criticalcoupling strengths to obtain burst synchronization in terms of the network structure aregiven. Our studies show that the network synchronizability is improved, as itsheterogeneity is reduced. The roles of synaptic parameters, more precisely those of thecoupling strengths and the network size are also investigated.     

Synchronization of neural networks by decentralized feedback control

This Letter deals with the synchronization problem of a class of neural networks with/without time delays. Based on the drive–response concept and the Lyapunov stability theorem, a delay-independent and decentralized control law is derived to achieve the exponential synchronization. Two illustrative examples are given to demonstrate the effectiveness of the presented synchronization scheme.     

Imaging and control of interfering wave packets in a dissociating molecule

Using two identical 110 femtosecond (fs) optical pulses separated by 310 fs, we launch two dissociative wave packets in I2. We measure the square of the wave function as a function of both the internuclear separation, /Psi(R)/(2), and of the internuclear velocity, /Psi(v(R))/(2), by ionizing the dissociating molecule with an intense 20 fs probe pulse. Strong interference is observed in both /Psi(R)/(2) and in /Psi(v(R))/(2). The interference, and therefore the shape of the wave function, is controlled through the phase difference between the two dissociation pulses in good agreement with calculations.     

Imaging and control of surface magnetization domains in a magnetoelectric antiferromagnet

We report the direct observation of surface magnetization domains of the magnetoelectric Cr(2)O(3) using photoemission electron microscopy with magnetic circular dichroism contrast and magnetic force microscopy. The domain pattern is strongly affected by the applied electric field conditions. Zero-field cooling results in an equal representation of the two domain types, while electric-field cooling selects one dominant domain type. These observations confirm the existence of surface magnetization, required by symmetry in magnetoelectric antiferromagnets.     

Partial state feedback control of chaotic neural network and its application

The chaos control in the chaotic neural network is studied using the partial state feedback with a control signal from a few control neurons. The controlled CNN converges to one of the stored patterns with a period which depends on the initial conditions, i.e., the set of control neurons and other control parameters. We show that the controlled CNN can distinguish between two initial patterns even if they have a small difference. This implies that such a controlled CNN can be feasibly applied to information processing such as pattern recognition.     

基于一维卷积神经网络的化爆和地震次声分类

次声事件的分类识别方法应用广泛,传统分类方法在很多方面进行了尝试,但由于次声信号具备非线性的特点,致使分类难度较大,分类精度不高,这对次声事件的分类工作提出了挑战.针对次声事件中的化学爆炸与天然地震信号分类问题,文章构建了一种改进的深度卷积神经网络分类模型用于实现两类次声信号的分类.论文采用"全面禁止核试验条约组织"官...     

Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control

The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural networks. Then a robust control law is designed to ensure the occurrence of the sliding motion for stabilization of the fractional-order Hopfield neural networks. Besides, for the unknown parameters of the fractional-order Hopfield neural networks, some estimations are made. Based on the fractional-order Lyapunov theory, the finite-time stability of the sliding surface to origin is proved well. Finally, a typical example of three-dimensional uncertain fractional-order Hopfield neural networks is employed to demonstrate the validity of the proposed method.     

Stochastic synchronization of coupled neural networks with intermittent control

In this Letter, we study the exponential stochastic synchronization problem for coupled neural networks with stochastic noise perturbations. Based on Lyapunov stability theory, inequality techniques, the properties of Weiner process, and adding different intermittent controllers, several sufficient conditions are obtained to ensure exponential stochastic synchronization of coupled neural networks with or without coupling delays under stochastic perturbations. These stochastic synchronization criteria are expressed in terms of several lower-dimensional linear matrix inequalities (LMIs) and can be easily verified. Moreover, the results of this Letter are applicable to both directed and undirected weighted networks. A numerical example and its simulations are offered to show the effectiveness of our new results.     

混沌神经网络的延时反馈控制研究

针对混沌神经网络，提出了一种改进的延时反馈控制方法. 利用该方法，当延时参数τ为奇数时，被控神经网络收敛于记忆模式以及它的反相模式的2周期上. 若选取不同的延时参数，被控网络则收敛于不同的周期态上. 关键词： 控制混沌 延时反馈控制 混沌神经网络     

Boundary control of an axially moving string via fuzzy sliding-mode control and fuzzy neural network methods

The objective of this study is to develop intelligent control schemes for the transverse vibration reduction of an axially moving string. The proposed approaches are backboned by the methods of fuzzy sliding-mode control (FSMC) and fuzzy neural network control (FNNC). In practice, the control effort for the system is realized through a typical mass-damper-spring (MDS) system attached at the right-hand side boundary of the moving string. The dynamic coupling between the string and the MDS system provides an actuation force to suppress transverse vibration. In the first phase of this study, the framework of FSMC is designed, in which the techniques of region-wise linear fuzzy logic control design and generic algorithm technique are employed to facilitate FSMC to reduce a large number of fuzzy rule bases and to select optimal control gains, respectively. In the second phase, the FNNC is developed, which is, compared to the FSMC, easier to design the control rule, more robust against environment and capable of on-line learning. Numerical simulations are conducted and the comparison between various controllers is made based on simulations. The simulated results show that the transverse vibration can be well suppressed by both approaches. FSMC offers the capability to regulate the transient response, while FNNC holds advantage of on-line learning capability.     

Impulsive control of stochastic systems with applications in chaos control, chaos synchronization, and neural networks

Real systems are often subject to both noise perturbations and impulsive effects. In this paper, we study the stability and stabilization of systems with both noise perturbations and impulsive effects. In other words, we generalize the impulsive control theory from the deterministic case to the stochastic case. The method is based on extending the comparison method to the stochastic case. The method presented in this paper is general and easy to apply. Theoretical results on both stability in the pth mean and stability with disturbance attenuation are derived. To show the effectiveness of the basic theory, we apply it to the impulsive control and synchronization of chaotic systems with noise perturbations, and to the stability of impulsive stochastic neural networks. Several numerical examples are also presented to verify the theoretical results.     

Chen氏混沌电路实现与同步控制实验研究

对Chen氏混沌系统的电路实现作了进一步的研究，推导出切实可行的电路参数.在此基础上利用Electronic Workbench电路仿真软件进行了仿真实验，并搭建实现了实际的Chen氏电路，在示波器上观察到了Chen氏混沌系统的混沌波形.同时，利用单变量耦合反馈控制方法对两个Chen氏系统的同步控制进行了实验研究，给出了实现同步的电路参数调节范围. 关键词： 混沌系统 Chen氏混沌 同步控制 电路实现