A subthreshold MOS neuron circuit based on the Volterra system.

We present an analog neuron circuit consisting of a small number of metal-oxide semiconductor (MOS) devices operating in their subthreshold region. The dynamics of the circuit were designed to be equivalent to the well-known Volterra system to facilitate developing the circuit for a particular application. We show that a simple nonlinear transformation of system variables in the Volterra system enables designing a neuron-like oscillator, which can produce sequences in time of identically shaped pulses (spikes) by using current-mode subthreshold MOS circuits. We present experimental results of the fabricated neuron circuits as well as an application in an inhibitory neural network, where the neurons compete with each other in the frequency and time domains.     

一种可实现STDP机制的CMOS突触和神经元电路设计

设计了一个高度集成的、高能效的CMOS突触和神经元电路。该突触电路可以实现基于脉冲时间依赖可塑性(Spike Timing Dependent Plasticity,STDP)的学习机制。通过这种机制可以模拟动作脉冲在真实突触中的传导特性并大规模集成为神经形态芯片。该电路采用低功耗的设计方法,晶体管偏置在亚阈值区,由低压电源(0.6 V)供电,采用0.18μm标准CMOS工艺实现。仿真结果表明,突触权值的变化为0.17 V~0.43 V;神经元电路可以真实模拟出神经元放电的多种模式,如RS(Regular Spike)模式、LTS(Low-Threshold Spike)模式、CH(Chattering)模式和IB(Intrinsic Bursting)模式等,电路在产生动作电位时每个脉冲平均仅消耗约0.6 p J的能量。     

An integrated circuit design of a silicon neuron and its measurement results

A novel MOSFET-based silicon nerve membrane model and its measurement results are described in this paper. This model is designed based on a mathematical structure that is characterized by phase plane analysis and bifurcation theory. The circuit is fabricated through MOSIS TSMC 0.35 μm CMOS process. Measurement results demonstrate that our circuit shows fundamental abilities of excitable cells such as a) a resting state, b) an action potential, c) a threshold, and d) a refractoriness. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Generation of two-variable functions based on the polynomial approximation

This paper describes how to generate uniformly continuous functions of two variables without making any vertex. In this system, all functions are obtained as an algebraic sum of linearly independent two-variable polynomials with each weight. Then, the polynomials are given by simulating nonlinear currents passing through semiconductors with dual control electrodes in their uniformly continuous regions. The proper weights can be uniquely determined by solving a matrix equation related to the characteristics of both the desired function and the semiconductors used in this system. Different functions can be produced by adjusting the weights. The usefulness of this system is successfully proved by experimental results.     

Logic operations based on single neuron rational model

This paper focuses on phase analysis to explore the single neuron local arithmetic and logic operations on their input conductances. Based on the analysis of the rational function model of local spatial summation with the equivalent circuits for steady-state membrane potentials, the prototypes spatial summation with the equivalent circuits for steady-state membrane potentials, the prototypes of logic operations are constructed. A mapping from a partition of input conductance space into functionally distinct phases is described and the multiple mode models for logic operations are established. The transitions from output voltage to input conductance in logic operations are also discussed for the connections between neurons in different layers. Our theoretical studies and software simulations indicate that the single neuron local rational logic is programmable and the selection of these functional phases can be effectively instructed by presynaptic activities. This programmability makes the single neuron more flexible in processing the input information.     

Robust multilayer neural network based on median neuron model

Multilayer perceptron has been widely used in time series forecasting for last two decades. However, it is a well-known fact that the forecasting performance of multilayer perceptron is negatively affected when data have outliers and this is an important problem. In recent years, some alternative neuron models such as generalized-mean neuron, geometric mean neuron, and single multiplicative neuron have been also proposed in the literature. However, it is expected that forecasting performance of artificial neural network approaches based on these neuron models can be also negatively affected by outliers since the aggregation function employed in these models is based on mean value. In this study, a new multilayer feed forward neural network, which is called median neuron model multilayer feed forward (MNM-MFF) model, is proposed in order to deal with this problem caused by outliers and to reach high accuracy level. In the proposed model, unlike other models suggested in the literature, MNM which has median-based aggregation function is employed. MNM is also firstly defined in this study. MNM-MFF is a robust neural network method since aggregation functions in MNM-MFF are based on median, which is not affected much by outliers. In addition, to train MNM-MFF model, particle swarm optimization method was utilized. MNM-MFF was applied to two well-known time series in order to evaluate the performance of the proposed approach. As a result of the implementation, it was observed that the proposed MNM-MFF model has high forecasting accuracy and it is not affected by outlier as much as multilayer perceptron model. Proposed method brings improvement in 7 % for data without outlier, in 90 % for data with outlier, in 95 % for data with bigger outlier.     

基于有向图的电路推理模型研究

电路仿真培训系统能够弥补实物培训设备的诸多缺陷，在电器设备操作及维修人员的岗位培训中发挥着重要作用。电路推理模型作为电路仿真系统的核心，目前多采用电路拓扑公式抽取方法来实现。该方法需人工分析电路的串并联关系，建立节点关联矩阵，因此建模工作量大。针对这一问题，提出基于有向图的电路推理模型，该模型直接从电路矢量图形出发，将其转化为电路有向图对象，在电路任意工作状态下搜索有向图的导通路径，给出合乎原理的工作状态演示。通过汽车电路仿真系统的实现，证明该模型的建模工作量小，运行可靠，尤其适合于复杂开关电路的仿真开发。     

A novel chaotic image cryptosystem based on DNA sequence operations and single neuron model

Multimedia Tools and Applications - In this paper, a novel image cryptosystem based on DNA sequence operations, Single Neuron Model (SNM) and chaotic map is designed. The initial conditions and...     

A group-theoretical model of the formal neuron

A group-theoretical model of the formal neuron which allows one to design optimally the neural computers over a finite set of the integer variations of the weight and threshold vectors was constructed and examined. This model creates the preconditions for direct mapping of the problems of the user of neural computer onto the quantum processes of the nanoelectronic systems whose fundamental characteristics are defined and described by the symmetry groups.     

Neural network learning with generalized-mean based neuron model

The advances in biophysics of computations and neurocomputing models have brought the foreground importance of dendritic structure of neuron. These structures are assumed as basic computational units of the neuron, capable of realizing the various mathematical operations. The well structured higher order neurons have shown improved computational power and generalization ability. However, these models are difficult to train because of a combinatorial explosion of higher order terms as the number of inputs to the neuron increases. In this paper we present a neural network using new neuron architecture i.e., generalized mean neuron (GMN) model. This neuron model consists of an aggregation function which is based on the generalized mean of all the inputs applied to it. The resulting neuron model has the same number of parameters with improved computational power as the existing multilayer perceptron (MLP) model. The capability of this model has been tested on the classification and time series prediction problems.     

A filter based neuron model for adaptive incremental learning of self-organizing maps

In this work a learning algorithm is proposed for the formation of topology preserving maps. In the proposed algorithm the weights are updated incrementally using a higher-order difference equation, which implements a low-pass digital filter. It is shown that by suitably choosing the filter the learning process can adaptively follow a specific dynamic. Numerical results, for time-varying and static distributions, show the potential of the proposed method for unsupervised learning.     

IC design of driving circuit of MEMS microrobot using pulse-type hardware neuron model

This paper presented a driving circuit which can output a driving waveform of the piezoelectric element impact-type actuator. The piezoelectric element impact-type actuator generates the rotational movement which is necessary to move the legs of the micro electro mechanical systems (MEMS) microrobot. The MEMS microrobot is made from silicon wafers fabricated by micro fabrication technology. The size of the fabricated MEMS microrobot is 4.0 mm × 4.6 mm × 3.6 mm. The driving circuit consists of a bare chip IC of the pulse-type hardware neuron model (P-HNM) and a peripheral circuit. P-HNM is an electrical oscillating model which has the same basic features of biological neurons. Therefore, P-HNM can output the driving waveform of the piezoelectric element impact-type actuator using electrical oscillation as biological neuron. As a result, we showed that the driving circuit can output the driving waveform of the piezoelectric element impact-type actuator without using any software programs or analog digital converters.     

基于泛函分析的人工神经元模型改进研究

人工神经网络是对生物神经系统的模拟,它的信息处理功能与网络单元的特性密切相关。神经元的研究方兴未艾,神经元的效率问题有待提升。通过研究神经元学习算法,提高神经元的学习效率,并且提出一种新的神经元模型。     

Multiplicative neuron model artificial neural network based on Gaussian activation function

Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural networks which involve the use of radial basis activation function produce more successful forecasting results, Gaussian activation function has not been used for multiplicative neuron model yet. In this study, rather than using a sigmoid activation function, Gaussian activation function was used in multiplicative neuron model artificial neural network. The weights of artificial neural network and parameters of activation functions were optimized by guaranteed convergence particle swarm optimization. Two major contributions of this study are as follows: the use of Gaussian activation function in multiplicative neuron model for the first time and the optimizing of central and propagation parameters of activation function with the weights of artificial neural network in a single optimization process. The superior forecasting performance of the proposed Gaussian activation function-based multiplicative neuron model artificial neural network was proved by applying it to real-life time series.     

基于CNNs电路模型的运动目标检测方法

针对帧差法和光流法两种运动目标检测方法,给出了相应的细胞神经网实现方式。采用不同视频图像序列进行了仿真,结果证明了所提出方法的有效性。     

一种新型PWM逆变器吸收电路

针对传统开关管吸收电路(RCD)存在自身消耗能量大,造成开关管温升加快,使用寿命降低等问题,提出了一种新型无源无损软开关吸收电路(Lossless Passive Soft-switching Snubber,LPSSS),解决了传统吸收电路自身的能量损耗,同时实现了能量回收。理论分析了逆变电路中IGBT关断过程中过电压产生的原因,以及RCD型吸收电路和新型LPSSS电路的工作原理。通过LPSSS吸收电路在逆变器中的分析可知LPSSS电路在IGBT关断过程中对浪涌电压dv/dt的有效抑制,同时最大程度地降低了吸收电路的能量损耗,实现了能量反馈,提高了能量转换效率。仿真分析表明,开关管的过电压降到了5.2%左右,验证了LPSSS电路的有效性和适用性。     

一种基于米勒电容的采样/保持电路

讨论了目前存在的基于米勒电容的采样/保持电路,在此基础上设计了一种简化形式.该电路利用简单的CMOS反相器代替米勒反馈电路中的运算放大器,在保证采样速度和精度的前提下,节省了面积.最后,采用TSMC公司的0.35μm标准CMOS工艺库对整体电路进行了性能分析和仿真.     

A two-variable approach to solve the polynomial Lyapunov equation

A two-variable polynomial approach to solve the one-variable polynomial Lyapunov equation is proposed. Lifting the problem from the one-variable to the two-variable context allows to use Faddeev-type recursions in order to solve the polynomial Lyapunov equation in an iterative fashion. The method is especially suitable for applications requiring exact or symbolic computation.     

基于SystemView的m序列发生器模型电路设计实现

分析了数字通信中伪随机码的数学及物理模型。以4位序列为例,利用SystemView仿真软件设计实现了伪随机码序列生成器的电路模型,并搭建仿真模型进行验证。仿真表明该设计具有合理性及正确性。     

基于Matlab热路模型的变压器绕组温度场研究

通过Matlab仿真分析,建立变压器绕组热路模型,得到绕组的热点位置和温升范围,将一种聚四氟乙烯材料作底板的光纤Bragg光栅(FBG)传感器通过匝间的垫片安装于绕组的热点位置.通过光栅窄带滤波反射后,解调拟合出光波长,通过数学模型计算得到热点温度.采集数据显示,测得绕组热点温度在额定功率实测为71℃,仿真结果为71.8℃;1.3倍功率实测温度为73℃,仿真结果为73.6℃.通过对比,能够及时在线反映出绕组的热点温度,为变压器的安全运行提供重要的参考数据.