神经元膜Ca2+敏感K+通道非线性动力学数学模型的研究

给出了神经元膜C^2 敏感K^ 通道三变量非线性模型，用以分析Ca^2 离子在神经元自主振荡中所引起的慢波振荡现象，并使用Matlab5.3计算了神经元振荡，分叉和混沌等现象的时间序列，分析了该模型的应用前景。     

神经元滤波型2自由度PID控制器研究

本文给出了一种用神经元实现的给定值滤波型2自由度PID控制器．这种控制器利用 神经元权值自学习功能解决了2自由度PID控制器参数整定这个难题并使系统具有自适应能力 ．物理实验表明：神经元滤波型2自由度PID控制器具有应用价值．     

基于LonWorks技术的智能型列车火灾报警控制器

介绍了LonWorks现场总线技术，并利用该技术设计完成列车火灾报警控制网络，同时成功地解决了神经元芯片与PC机以及单片之间的串行通信，从而为在传统的测控系统中运用LonWorks技术开辟了一条途径。     

基于LonWorks技术的变电站综合自动系统的通信技术

本文介绍了LonWorks技术的系统结构和技术特点，论述了变电站综合自动化系统现场总线应用LonWorks技术的设计方法。通过对网络可靠通信问题进行研究，得出了在网络节点负载较重的情况下，采用优先级对等和轮询相结合的通信方式，可较好地降低网络通信出错率，提高对紧急事件的响应速度。     

Application of Single Neuron Adaptive PID Regulators with Auto-tuning Gain in Industrial Parameter (Pressure) Closed-loop Process Control Systems

1IntroductionTheworkcontroloffluidmachinessuchasfans,waterPUInpsandair-Pressersetc.frequentlyemploypressureclosed-loopprocesscontrolSystems[11.IndustrialproducingandPracticingshowthatthesystemshaveaseriousPressurefluctUationPhenomenonduringworkingPro...     

NEURONC语言与事件驱动编程

介绍了ＬｏｎＷｏｒｋｓ网络系统中节点的主要开发语言ＮＥＵＲＯＮＣ的语言,着重阐明了该语言对事件驱动编程的支持,在说明事件调度策略的基础上,提出一种“链式激发”的事件驱动编程方法 。     

Symbolic dynamics of a chaotic neuron model

Chaotic neurons change their internal state according to a bimodal map, and when they communicate with other neurons their internal state is transformed into one of two separate outputs, firing or resting. We address and investigate the topological entropy of the two-valued output of the chaotic neuron from two different viewpoints: the dependence upon the parameters of the neurons, and the relationship to their threshold. From the former viewpoint, we clarify the mechanism that changes the shift space corresponding to the time series of the neuronal output. From the latter viewpoint, we examine the effect of small fluctuations on the threshold of the chaotic neuron. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Effects of age and GDNF on noradrenergic innervation of the hippocampal formation: studies from intraocular grafts

Recent studies have suggested that factors in the target tissue influence the degree of plasticity and regeneration following aging and/or specific insults. We have investigated whether young or aged targets differ in their noradrenergic innervation from fetal locus coeruleus (LC) neurons, and also if a specific growth factor, glial cell line-derived neurotrophic factor (GDNF) can affect this innervation pattern. Tissue pieces of fetal brainstem and young (3 months) or old (18 months) iris tissue were transplanted simultaneously into the anterior chamber of the eye of adult hosts. We found that aged iris transplants became innervated to a significantly lesser degree by the cografted LC neurons than young iris transplants. Fetal hippocampal tissue was then grafted to adult hosts, and a fetal brainstem graft containing LC neurons was placed adjacent to the first graft, either at 3 or 21 months post-grafting. Thus, old/young chimeras of the noradrenergic coeruleo-hippocampal pathway were created. Aged hippocampal grafts received a much less dense innervation from co-grafted LC neurons than young hippocampal grafts. Tyrosine hydroxylase-positive-immunoreactive innervation was only found in the outskirts of aged grafts, while the young hippocampal grafts contained an even innervation pattern. The innervation density of hippocampal grafts was significantly enhanced by GDNF treatment. These findings demonstrate that target-derived factors may regulate neuronal plasticity, and that the age of the target is more important for innervation properties than the age of the neuron innervating a particular target.     

MLM形态神经网络在彩色图像恢复中的应用

目前彩色图像的恢复方法多集中在加强色彩相似性的研究上。提出了一种基于形态神经网络MLM的图像恢复法,利用非线性的形态神经元算子对噪声图像进行处理,以获得高质量的清晰图像。仿真结果表明,采用本法恢复的图像在连续性及细节的保护上优于其人方法。     

M‐matrices and global convergence of discontinuous neural networks

The paper considers a general class of neural networks possessing discontinuous neuron activations and neuron interconnection matrices belonging to the class of M‐matrices or H‐matrices. A number of results are established on global exponential convergence of the state and output solutions towards a unique equilibrium point. Moreover, by exploiting the presence of sliding modes, conditions are given under which convergence in finite time is guaranteed. In all cases, the exponential convergence rate, or the finite convergence time, can be quantitatively estimated on the basis of the parameters defining the neural network. As a by‐product, it is proved that the considered neural networks, although they are described by a system of differential equations with discontinuous right‐hand side, enjoy the property of uniqueness of the solution starting at a given initial condition. The results are proved by a generalized Lyapunov‐like approach and by using tools from the theory of differential equations with discontinuous right‐hand side. At the core of the approach is a basic lemma, which holds under the assumption of M‐matrices or H‐matrices, and enables to study the limiting behaviour of a suitably defined distance between any pair of solutions to the neural network. Copyright © 2006 John Wiley & Sons, Ltd.