Pulse-type Hardware Neuron Model Research Articles

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...

Development of impact-type rotary actuator and application for MEMS microrobot by bare chip IC of hardware neuron model

This paper proposes an impact-type microelectro mechanical systems (MEMS) rotary actuator and application for a millimeter-scale MEMS microrobot. The rotational motion of the actuator was generated by vibration of a multilayer piezoelectric element. The size of the fabricated actuator was 1.0 mm × 4.4 mm × 3.2 mm. The fabricated actuator was controlled by the pulse-type hardware neuron model (P-HNM) as the biomimetics technology, and it was integrated in a CMOS IC. The maximum rotational speed was 37.5 rpm when an applied neuron frequency was 39.6 kHz and an input voltage was 3.48 V. The actuator showed the low power consumption of 96.4 mW. The fabricated actuator and the fabricated control circuit that included the P-HNM were built in the MEMS microrobot. The size of the fabricated microrobot was 4.0 mm × 4.6 mm × 4.8 mm.

Locomotion Control of MEMS Microrobot Using Pulse‐Type Hardware Neural Networks

SUMMARYThis paper presents the locomotion control of a microelectromechanical system (MEMS) microrobot. The MEMS microrobot demonstrates locomotion control by pulse‐type hardware neural networks (P‐HNN). P‐HNN generate oscillatory patterns of electrical activity like those of living organisms. The basic component of P‐HNN is a pulse‐type hardware neuron model (P‐HNM). The P‐HNM has the same basic features as biological neurons, such as the threshold, the refractory period, and spatiotemporal summation characteristics, and allows the generation of continuous action potentials. P‐HNN has been constructed with MOSFETs and can be integrated by CMOS technology. Like living organisms, P‐HNN has realized robot control without using software programs or A/D converters. The size of the microrobot fabricated by MEMS technology was 4 × 4 × 3.5 mm. The frame of the robot was made of a silicon wafer, equipped with rotary actuators, link mechanisms, and six legs. The MEMS microrobot emulated the locomotion method and the neural networks of an insect by rotary actuators, link mechanisms, and the P‐HNN. We show that the P‐HNN can control the forward and backward locomotion of the fabricated MEMS microrobot, and that it is possible to switch its direction by inputting an external trigger pulse. The locomotion speed was 19.5 mm/min and the step size was 1.3 mm. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(3): 43–50, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22473

1P1-B09 生物の脳を模したパルス形ハードウェアニューラルネットワークによるサーボモータ用PWM波形の生成(脳・神経・認知ロボティクス)

This paper presents the pulse-type hardware neural networks (P-HNN) which could output the pulse width modulation (PWM) waveform of PWM servo motor for robots. Basic components of P-HNN were pulse type hardware neuron model (P-HNM). P-HNM consisted by the cell body model, the axon model and the synaptic model. P-HNM had the same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics and enabled the generation of continuous action potentials. We used the synaptic model which could vary the synaptic weights by controlling voltage. As a result, we showed that P-HNN could control the PWM servo motor from 0 to 180 degrees.

Biomimetics Micro Robot with Active Hardware Neural Networks Locomotion Control and Insect-Like Switching Behaviour

In this paper, we presented the 4.0, 2.7, 2.5 mm, width, length, height size biomimetics micro robot system which was inspired by insects. The micro robot system was made from silicon wafer fabricated by micro electro mechanical systems (MEMS) technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the insect-like switching behaviour. In addition, we constructed the active hardware neural networks (HNN) by analogue CMOS circuits as a locomotion controlling system. The HNN utilized the pulse-type hardware neuron model (P-HNM) as a basic component. The HNN outputs the driving pulses using synchronization phenomena such as biological neural networks. The driving pulses can operate the actuators of the biomimetics micro robot directly. Therefore, the HNN realized the robot control without using any software programs or A/D converters. The micro robot emulated the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms and HNN. The micro robot performed forward and backward locomotion, and also changed direction by inputting an external trigger pulse. The locomotion speed was 26.4 mm/min when the step width was 0.88 mm.

Locomotion Control of MEMS Micro Robot Using Pulse-Type Hardware Neural Networks

This paper presents the locomotion control of micro electro mechanical systems (MEMS) micro robot. MEMS micro robot demonstrates the locomotion control by the pulse-type hardware neural networks (P-HNN). P-HNN generates oscillatory patterns of electrical activity such as living organisms. The basic component of P-HNN is pulse-type hardware neuron model (P-HNM). P-HNM has same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics and enables the generation of continuous action potentials. P-HNN was constructed by MOSFETs, can be integrated by CMOS technology. Same as the living organisms P-HNN realized the robot control without using software programs, or A/D converters. The size of micro robot fabricated by the MEMS technology was 4×4×3.5 [mm]. The frame of robot was made of silicon wafer, equipped with rotary type actuators, link mechanisms and 6 legs. MEMS micro robot emulated the locomotion method and the neural networks of the insect by rotary actuators, link mechanisms and P-HNN. As a result, we show that P-HNN can control the forward and backward locomotion of fabricated MEMS micro robot, and also switched the direction by inputting the external trigger pulse. The locomotion speed was 19.5 [mm/min] and the step width was 1.3 [mm].

A synaptic circuit of a pulse-type hardware neuron model with STDP

A number of recent studies of neural networks have been conducted with the purpose of applying engineering to the brain. We have been attempting to construct artificial neural networks. In the present study, we focus on STDP (spike timing dependent synaptic plasticity) and construct a neural network from a pulse-type hardware neuron model (P-HNM) with STDP. In addition, we propose a simple synaptic circuit of the P-HNM with STDP, which is composed of MOSFETs, and construct a simple neural network in order to verify the robustness with respect to phase fluctuation. We show that a pulse-type hardware neural network with STDP synapses has a learning function that has robustness with respect to phase fluctuation.

Short-term memory circuit using hardware ring neural networks

A number of studies have recently been made on various neuron models and neural networks. This research is studied for applications to engineering problems and an understanding of the information processing functions of living organisms. We are studying an asynchronous neural network using a pulse-type hardware neuron model (P-HNM). Recently, we have been trying to construct a short-term memory circuit using hardware ring neural networks (RNN) with P-HNM. In this article, we discuss the construction of a short-term memory circuit using the hardware RNN, and conduct experiments that explain the characteristics of the network through circuit simulation using PSpice. As a result, we verify that the RNN which is proposed in this article can be used as the short-term memory circuit.

A Λ‐type neuron model using enhancement‐mode MOSFETs

AbstractRecently, research has been actively pursued to implement a neuron, which is the basic unit for building neural networks, in hardware with engineering applications as the objective. Specifically, pulse‐type hardware neuron models, which approximately replicate pulse signals, have been examined as the information transmission stage in the brain. However, a pulse‐type hardware neuron model is a complex circuit and includes inductors. Consequently, there are few practical models. In this paper, we present the Λ‐type neuron model, which is a pulse‐type hardware neuron model built from only enhancement‐mode MOSFETs (E‐MOSFETs), which can be handled in a standard CMOS process, and capacitors, and is useful as a hardware neuron model intended for large‐scale neural networks. First, we show that a Λ‐type negative resistance circuit having a negative resistance that varies over time can be fabricated from E‐MOSFETs, and explain its principle. Next, we show that this negative resistance circuit can be used to fabricate a Λ‐type neuron model from E‐MOSFETs, which can be handled in a standard CMOS process. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(1): 18–25, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10020

Yagi‐type hardware neuron model with CMOS IC

AbstractWith a view to engineering applications of the information processing mechanisms used by neural networks in biological bodies, various studies have been conducted on pulse‐type neurons and neural networks. When research on a large‐scale pulse‐type neural network is carried out, there are limitations on computer simulation, so that hardware realization is desirable. When a neural network is realized by hardware, it is advantageous to model a single neuron by a CMOS IC suitable for large‐scale LSI. In this paper, in reference to the Yagi neuron model, a pulse‐type hardware neuron model, it is shown that a new pulse‐type hardware neuron model can be constructed with a simple circuit configuration that can be realized with CMOS ICs. First, the equivalent inductor constructed with a low capacitance and an enhancement‐mode MOSFET is discussed. Its configuration and characteristics are elucidated. Next, the negative resistance circuit is discussed. A circuit is proposed that can be applied to the CMOS process. From their characteristics, it is shown that two new neuron models based on the Yagi neuron model can be constructed. They are the conventional pulse‐type neuron model fired by an external input and the pacemaker neuron model that can fire itself. © 2002 Scripta Technica, Electron Comm Jpn Pt 2, 85(3): 23–29, 2002

A pulse-type hardware neuron model with beating, bursting excitation and plateau potential

We proposed a pulse-type hardware neuron model. It could reproduce simple excitations, beating and bursting discharges as well as an action potential with a plateau potential observed in living membranes. The model exhibited one of these dynamics depending on parameter values of the model's circuit. They include resistance, capacitance and externally injected DC current intensity. We studied the model's dynamics based on hardware experiments and mathematical analyses. Our results showed that two inward currents introduced into the model and differences in their operating time scales determined dynamics of the model. In particular, we illustrated a mechanism of the bursting discharges generation in terms of bifurcation theory and time-dependent changes in the form of instantaneous current–voltage characteristics of the model.

Pulse-type hardware chaotic neuron model and its bifurcation phenomena

A number of studies has recently been made on hardware for a biological neuron for application to information processing functions of neural networks. A model of a single neuron with chaotic dynamics (called “the chaotic neuron model” proposed by Aihara) has the following properties of biological neurons: graded responses, relative refractoriness, and spatio-temporal summation of inputs. The model exhibits not only periodic responses but also chaotic responses for periodic stimulation. It is suggested that, with the chaotic neuron model, new information processing structures can be realized in neural networks. Accordingly, a single-neuron model should be designed so as to have the properties of biological neurons. But a pulse-type hardware chaotic neuron model has not been found. We previously proposed a pulse-type hardware neuron model composed of a negative resistance circuit, resistors, and capacitors. We reported also that the pulse-type hardware neuron model exhibited continuous firing for a constant stimulus current. But chaotic features of the neuron model have not been investigated. In this paper, we show, firstly, that the pulse-type hardware neuron model has the features of the chaotic neuron model, and we show that it is useful for the pulse-type hardware chaotic neuron model. Next, we show that the pulse-type hardware chaotic neuron model has three chaotic regions in the bifurcation diagram to periodic pulse train stimulation, and we clarify the bifurcation route and the return map in each chaotic region.

Pulse-type hardware chaotic neuron model and its bifurcation phenomena

Abstract A number of studies has recently been made on hardware for a biological neuron for application to information processing functions of neural networks. A model of a single neuron with chaotic dynamics (called “the chaotic neuron model” proposed by Aihara) has the following properties of biological neurons: graded responses, relative refractoriness, and spatio-temporal summation of inputs. The model exhibits not only periodic responses but also chaotic responses for periodic stimulation. It is suggested that, with the chaotic neuron model, new information processing structures can be realized in neural networks. Accordingly, a single-neuron model should be designed so as to have the properties of biological neurons. But a pulse-type hardware chaotic neuron model has not been found. We previously proposed a pulse-type hardware neuron model composed of a negative resistance circuit, resistors, and capacitors. We reported also that the pulse-type hardware neuron model exhibited continuous firing for a constant stimulus current. But chaotic features of the neuron model have not been investigated. In this paper, we show, firstly, that the pulse-type hardware neuron model has the features of the chaotic neuron model, and we show that it is useful for the pulse-type hardware chaotic neuron model. Next, we show that the pulse-type hardware chaotic neuron model has three chaotic regions in the bifurcation diagram to periodic pulse train stimulation, and we clarify the bifurcation route and the return map in each chaotic region.