We propose a minimal model of neuronal burst-firing that can be considered as a modification and extention of the Bonhoeffer-van der Pol (BVP) model. By using linear stability analysis we show that one of the equilibrium points of the fast subsystem is a saddle point which divides the phase plane into two regions. In one region all phase trajectories approach a limit cycle and in the other they approach a stable equilibrium point. The slow subsystem describes a slowly varying inward current. Various types of bursting phenomena are presented by using bifurcation analysis. The simplicity of the model and the variety of firing modes are the biggest advantages of our model with obvious applications in understanding underlying mechanisms of generation of neuronal firings and modeling oscillatory neural networks.

The low phase noise frequency source to be used for measurements and so on realizes by oscillator having highly output signal power against output noise power. SAW devices can be used with high power than BAW devices. So we examine on configuration of SAW oscillator circuits with the power gain. In this paper we shall discuss a configuration of oscillator circuit to obtain an extremely low phase noise and an oscillator operating at a non-reactive frequency of SAW resonator.

A number of studies have recently been published concerning chaotic neuron models and asynchronous neural networks having chaotic neuron models. In the case of large-scale neural networks having chaotic neuron models, the neural network should be constructed using analog hardware, rather than by computer simulation via software, due to the high speed and high integration of analog circuits. In the present study, we discuss the circuit structure of a chaotic neuron model, which is constructed on the basis of the mathematical model of an asynchronous chaotic neuron. We show that the pulse-type hardware chaotic neuron model can be constructed on the basis of the mathematical model of an asynchronous chaotic neuron. The proposed model is an effective model for the cell body section of the pulse-type hardware chaotic neuron model for ICs. In addition, we show the bifurcation structure of our composed model, and discuss the bifurcation routes and return maps thereof.

In this paper, we discuss noise reduction approaches to improving range images using a nonlinear 2D Kalman filter. First, we propose the nonlinear 2D Kalman filter, which can reduce noise in the range image using an estimated edge vector and a nonlinear function that does not distort sharp edges. Second, we evaluate reduction of the additive noise in a test range image using the mean square error (MSE). Third, we discuss the detection rate and the number of false detections in the estimated range image. Fourth, a simulation example demonstrating the performance of the proposed 2D Kalman filter for a real range image having abrupt changes is presented. Finally, simulation results are presented which show that the estimated image of the nonlinear 2D Kalman filter is effective in reducing the amount of noise, while causing minimal smoothing of the abrupt changes.

In this paper, we propose the CMOS implementation of a multiple-valued memory cell using -shaped negative-resistance devices. We first propose the construction of a multiple-stable circuit that consists of -shaped negative-resistance devices from four enhancement-mode MOSFETs without a floating voltage source, and connect this in parallel with a unit circuit. It is shown that the movement of -shaped negative-resistance characteristics in the direction of the voltage axis is due to voltage sources. Furthermore, we propose the construction of a multiple-valued memory cell using a multiple-stable circuit. It is shown that it is possible to write and hold data. If the power supply is switched on, it has a feature which enables operation without any electric charge leakage. It is possible, by connecting -shaped negative-resistance devices in parallel, to easily increase the number of multiple values.

In this paper, we propose the CMOS implementation of neuron models for an artificial auditory neural network. We show that when voltage is added directly to the control terminal of the basic circuit of the hardware neuron model, a change in the output firing is observed. Next, based on this circuit, a circuit that changes with time is added to the control terminal of the basic circuit of the hardware neuron model. As a result, a neuron model is constructed with ON firing, adaptation firing, and repetitive firing using CMOS. Furthermore, an improved circuit of a neuron model with OFF firing using CMOS which has been improved from the previous model is also constructed.

Brain subsystems have a high degree of information processing ability using nonlinear dynamics and although various neuron models and artificial neural networks have been investigated, the information processing functions of biological neural networks have not yet been clarified. Recently, various research efforts have confirmed that dendrites perform an important role in brain information processing. In this paper, we discuss the nonlinear characteristics of a hardware active dendrite model, in order to clarify information encoding and transmission via action potentials. That is to say, we show that our proposed model can reproduce the nonlinear characteristics of a biologically active dendrite. First, the hardware active dendrite model we propose is described. We next discuss the response characteristics for pulse stimuli using the model. As a result, when input pulses are applied to an active line, which is the basic structure of the dendrite model, it is shown clearly that backpropagation characteristics are acquired and that the characteristics are qualitatively in agreement with the characteristics of biological dendrites. Furthermore, we verify that the ratio of input to output frequency at the cell body is influenced by the backpropagation characteristics with two branches, which is the simplest structure in the active dendrite model. Thus, with backpropagation characteristics, the possibility that the model can carry out clearly the information processing of biological neural networks, is suggested.