基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性动态平衡

大脑皮层的兴奋性与抑制性平衡是维持正常脑功能的前提, 而其失衡会诱发癫痫、帕金森、抑郁症等多种神经疾病, 因此兴奋性与抑制性平衡的研究是脑科学领域的核心科学问题. 反馈神经回路是脑皮层网络的典型连接模式, 抑制性突触可塑性在兴奋性与抑制性平衡中扮演关键角色. 本文首先构建具有抑制性突触可塑性的反馈神经回路模型; 然后通过计算模拟研究揭示在抑制性突触可塑性的调控下反馈神经回路的兴奋性与抑制性可取得较高程度的动态平衡, 并且二者的平衡对输入扰动具有较强的鲁棒性; 其次给出了基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性平衡机理的解释; 最后发现反馈回路神经元数目有利于提高兴奋性与抑制性平衡的程度, 这在一定程度上解释了为何神经元之间会存在较多的连接. 本文的研究对于理解脑皮层的兴奋性与抑制性动态平衡机理具有重要的参考价值.     

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.     

Asymmetric lateral inhibitory neural activity in the auditory system: a magnetoencephalographic study

Background  

Decrements of auditory evoked responses elicited by repeatedly presented sounds with similar frequencies have been well investigated by means of electroencephalography and magnetoencephalography (MEG). However the possible inhibitory interactions between different neuronal populations remains poorly understood. In the present study, we investigated the effect of proceeding notch-filtered noises (NFNs) with different frequency spectra on a following test tone using MEG.     

Equalization of synaptic efficacy by synchronous neural activity

It is commonly believed that spike timings of a postsynaptic neuron tend to follow those of the presynaptic neuron. Such orthodromic firing may, however, cause a conflict with the functional integrity of complex neuronal networks due to asymmetric temporal Hebbian plasticity. We argue that reversed spike timing in a synapse is a typical phenomenon in the cortex, which has a stabilizing effect on the neuronal network structure. We further demonstrate how the firing causality in a synapse is perturbed by synchronous neural activity and how the equilibrium property of spike-timing dependent plasticity is determined principally by the degree of synchronization. Remarkably, even noise-induced activity and synchrony of neurons can result in equalization of synaptic efficacy.     

Polarization dynamics and low-frequency fluctuations in vertical-cavity surface-emitting lasers subjected to optical feedback

The dynamics of vertical-cavity surface-emitting lasers with isotropic optical feedback from a distant reflector is studied experimentally and numerically. Low-frequency fluctuations with sudden drop-outs of power are found to be qualitatively independent of the amount of excitation of the second polarization component. The latter is shown to be controlled to a great extent by the intrinsic dichroism of the solitary laser. The experimental findings are in very good qualitative agreement with simulations. The characteristic features of a doublet in the radio-frequency spectra around the external-cavity round-trip frequency are discussed. PACS 42.55.Px; 42.60.Mi; 42.65.Sf     

光反馈半导体激光器产生低频起伏与高维混沌信号及其演化过程

利用光反馈半导体激光器产生了低频起伏及关联维数为12.2的高维混沌状态. 实验研究了低频起伏到高维混沌的演变过程. 实验表明,当偏置电流I_b <1.03I_th时,随着反馈强度的降低,低频起伏的峰峰值先增大后减小,平均周期一直减小,直至进入稳定状态,但不能转化为混沌. 当偏置电流I_b >1.03I_th时,随着反馈强度的降低,低频起伏的峰峰值一直增大,平均周期一直减小 关键词： 低频起伏 混沌 半导体激光器 反馈     

一种具有正弦基函数权值的反馈型神经网络模型

提出了一种新的两层反馈型神经网络模型. 该网络采用正弦基函数作为权值, 神经元激活函数为线性函数, 连接形式为两层反馈型结构. 研究并定义了该反馈型神经网络的能量函数, 分析了网络运行的稳定性问题, 并证明了在Liapunov意义下网络运行的稳定性. 网络运行过程中, 其权值不做调整(但随时间按正弦规律变化), 网络状态不断地转换. 随着网络状态变化其能量不断减小, 最终在达到稳定时能量到达极小点. 由于该反馈型神经网络权值为正弦函数, 特别适合于周期信号的自适应逼近和检测, 为实际中周期性信号检测与处理提供了一种新的、有效的网络模型和方法. 作为应用实例把该网络应用于电力系统中电压凹陷特征量实时检测, 仿真结果表明, 网络用于信号检测不仅有很高的静态精度, 而且有非常好的动态响应特性.     

Topographic organization of Hebbian neural connections by synchronous wave activity

Experimental studies have revealed that the refinement of early, imprecise connections in the developing visual system involves activity in the retina before the onset of vision. We study the evolution of initially random unidirectional connections between two excitable layers of FitzHugh-Nagumo neurons with simulated spontaneous activity in the input layer. Lateral coupling within the layers yields synchronous neural wave activity that serves as a template for the Hebbian learning process, which establishes topographically precise interlayer connections. (c) 2001 American Institute of Physics.     

时滞递归神经网络中神经抑制的作用

研究了具有时滞的二阶递归神经网络中抑制自连接的作用，给出了时滞依赖的全局渐近稳定的充分判据.研究结果表明：抑制自连接可镇定不稳定的网络并使其渐近稳定；抑制自连接的镇定作用受到网络传输时滞的制约.仿真示例验证了结果的有效性. 关键词： 递归神经网络 时滞 抑制神经元 动态行为     

A low-dimensional dynamical system to describe low-frequency fluctuations in a semiconductor laser with optical feedback

We derive a low-dimensional dynamical system to describe a semiconductor laser with optical feedback. This model captures many features from the original time delayed dynamical system and explains the origin of the low-frequency instability observed in the time-delayed equation.     

Qubit decoherence by Gaussian low-frequency noise

We have derived an explicit nonperturbative expression for decoherence of quantum oscillations in a qubit by Gaussian low-frequency noise. Decoherence strength is controlled by the noise spectral density at zero frequency, while the noise correlation time τ determines the time t of crossover from the \({1 \mathord{\left/ {\vphantom {1 {\sqrt t }}} \right. \kern-\nulldelimiterspace} {\sqrt t }}\) to the exponential suppression of coherence. We also performed Monte Carlo simulations of qubit dynamics with noise which agree with the analytical results.     

Dephasing of qubits by transverse low-frequency noise

We analyze the dissipative dynamics of a two-level quantum system subject to low-frequency, e.g., 1/f noise, motivated by recent experiments with superconducting quantum circuits. We show that the effect of transverse linear coupling of the system to low-frequency noise is equivalent to that of quadratic longitudinal coupling. We further find the decay law of quantum coherent oscillations under the influence of both low-and high-frequency fluctuations, in particular, for the case of comparable rates of relaxation and pure dephasing.     

Analysis of the fast recovery dynamics of a semiconductor laser with feedback in the low-frequency fluctuation regime

We experimentally investigate the temporal evolution of the power of an external cavity semiconductor laser in the low-frequency fluctuation regime with subnanosecond resolution. We show, for the first time to our knowledge, that generally the laser power drops to a value significantly different from the solitary laser power. We demonstrate the analogy between the recovery of the laser intensity and the turn-on transient of a semiconductor laser.     

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.     

低频水下声信号的激光探测

根据表面波声光效应的原理,提出了一种低频水下声信号的激光探测技术,并建立了实验装置。在几十赫兹的低频段,对水下声源所产生的表面声波进行了探测。实验过程中,利用MATLAB软件对拍摄的衍射图样进行扫描分析,得到了衍射图样中条纹的像素差。根据波长与条纹间距的解析关系式,得到了低频液体表面声波波长,其大小在毫米量级。利用计算机编程,根据最小二乘法拟合色散关系的回归曲线,测量结果与理论色散关系吻合。该方法具有实时、非接触的特点。     

Memory feedback finite-time control for memristive neutral-type neural networks with quantization

The drive-response of memory feedback control design for memristor neural networks of neutral type over finite-time domain is scrutinized in this paper. Notably, the main purpose of this work is to synthesize memory feedback controller in the presence of logarithmic quantizer and actuator saturation to guarantee the finite-time boundedness of the resulting memristive neural networks. On basis of proper Lyapunov–Krasovskii-functional and linear matrix inequalities, new sufficient criterian is established to assure the delay-dependent finite-time stabilization criteria for the addressed network model. Also, by solving the developed linear matrix inequalities, the finite-time memory feedback control law gain matrices could be attained. Eventually, the validations of the proposed mechanism are ultimately explored through two numerical examples.     

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