• Journal of Sensor Science and Technology
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• v.18 no.2
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• pp.128-134
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• 2009

Integrated circuit of a new neuron chain with a synapse function for Hodgkin-Huxley model which is a good electrical model about a real biological neuron is implemented in a $0.5{\mu}m$ 1 poly 2 metal CMOS technology. Pulse type neuron chain consist of series connected current controlled single neurons through synapses. For the realization of the single neuron, a pair of voltage mode oscillators using operational transconductance amplifiers and capacitors is used. The synapse block which is a connection element between neurons consist of a voltage-current conversion circuit using current mirror. SPICE simulation results of the proposed circuit show 160 mV amplitude pulse output and propagation of the signal through synapses. Measurements of the fabricated pulse type neuron chip in condition of ${\pm}2.5\;V$ power supply are shown and compared with the simulated results.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.4
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• pp.383-390
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• 2014

This paper presents an excitatory CMOS neuron oscillator circuit design, which can synchronize two neuron-bursting patterns. The excitatory CMOS neuron oscillator is composed of CMOS neurons and CMOS excitatory synapses. And the neurons and synapses are connected into a close loop. The CMOS neuron is based on the Hindmarsh-Rose (HR) neuron model and excitatory synapse is based on the chemical synapse model. In order to fabricate using a 0.18 um CMOS standard process technology with 1.8V compatible transistors, both time and amplitude scaling of HR neuron model is adopted. This full-chip integration minimizes the power consumption and circuit size, which is ideal for motion control unit of the proposed bio-mimetic micro-robot. The experimental results demonstrate that the proposed excitatory CMOS neuron oscillator performs the expected waveforms with scaled time and amplitude. The active silicon area of the fabricated chip is $1.1mm^2$ including I/O pads.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.3-5
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• 1997

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.6
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• pp.658-663
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• 2015

In the previous work, we have proposed an integrate-and-fire neuron circuit and synaptic device based on the floating body MOSFET [1-3]. Integrate-and-Fire(I&F) neuron circuit emulates the biological neuron characteristics such as integration, threshold triggering, output generation, refractory period using floating body MOSFET. The synaptic device has short-term and long-term memory in a single silicon device. In this paper, we connect the neuron circuit and the synaptic device using current mirror circuit for summation of post synaptic pulses. We emulate spike-timing-dependent-plasticity (STDP) characteristics of the synapse using feedback voltage without controller or clock. Using memory device in the logic circuit, we can emulate biological synapse and neuron with a small number of devices.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.284-288
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• 2019

A calibration-free analog neuron circuit is proposed as a viable alternative to the power hungry digital neuron in implementing a deep neural network. The conventional analog neuron requires calibrations because a voltage-mode link is used between the soma and the synapse, which results in significant uncertainty in terms of current mapping. In this work, a current-mode link is used to establish a robust link between the soma and the synapse against the variations in the process and interconnection impedances. The increased hardware owing to the adoption of the current-mode link is estimated to be manageable because the number of neurons in each layer of the neural network is typically bounded. To demonstrate the utility of the proposed analog neuron, a simple neural network with $4{\times}7{\times}3$ architecture has been designed for classifying iris flowers. The chip is now under fabrication in 0.35 mm CMOS technology. Thus, the proposed true current-mode analog neuron can be a practical option in realizing power-efficient neural networks for edge computing.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.1
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• pp.7-11
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• 1998

In this paper, a new synapse cell with nonvolatile SONOS semiconductor memory device is proposed and it's fundamental function electronically implemented SONOS NVSM has shown characteristics that the memory value, synaptic weights, can be increased or decreased incrementally. A novel SONOS synapse is used to read out the stored analog value. For the purpose of synapse implementation using SONOS NVSM, this work has investigated multiplying characteristics including weight updating characteristics and neuron output characteristics. It is concluded that SONOS synapse cell has good agreement for use as a synapse in artificial neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.516-516
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• 2000

We propose a neuron model that is possible to learn three-dimensional movement. The neuron model by imitating structure of a neuron, has the system resemble a neuron. We considered a neuron system based on the arguments, and wished to examine whether the system had reasonable function. Koch, Poggio and Torre believed that inhibition signal would shunt excitation signal on the dendrites. They believed that excitation signal operated input-signals and inhibition did as delayed ones. Thus, they were sure that function for directional selectivity was arisen by the shunting. Koch's concept is so important; therefore, we construct the neuron system with their concept. The neuron system makes the shunting function; thus, the model may have a function for directional selectivity. We initialized the connections and the dendrites by random data, and trained them by the back-propagation algorithm for three-dimensional movement. We made sure the defection of three-dimensional movement in the system.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.457-461
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• 2014

Using $0.18{\mu}m$ CMOS process silicon neuron circuit of the pulse type for modeling biological neurons, were designed in the semiconductor integrated circuit. Neuron circuiSt providing is formed by MOS switch for initializing the input terminal of the capacitor to the input current signal, a pulse signal and an amplifier stage for generating an output voltage signal. Synapse circuit that can convert the current signal output of the input voltage signal, using a bump circuit consisting of NMOS transistors and PMOS few. Configure a chain of neurons for verification of the neuron model that provides synaptic neurons and two are connected in series, were performed SPICE simulation. Result of simulation, it was confirmed the normal operation of the synaptic transmission characteristics of the signal generation of nerve cells.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.462-462
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• 2000

In this paper, genetic algorithm (GA) is the technique to search for the optimal structures (i,e., the kind of neural network, the number of hidden neuron, ..) of the neural networks which are used approximating a given nonlinear function, In this paper, we used multi layer feed-forward neural network. The decision method of synapse weights of each neuron in each generation used back-propagation method. In this study, we simulated nonlinear function approximation in the temperature control system.

• Applied Microscopy
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• v.36 no.2
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• pp.83-91
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• 2006

The number and structure of synapses are dynamically changed in response to diverse physiological and pathological conditions. Since strength of synaptic transmission is closely related to the synaptic density on a neuron, both synaptogenesis and synapse loss may play important roles in controlling neuronal activity. Thus it is essential to estimate the number of synapses using an accurate quantitative method for better understanding of the numerical alteration of synapses under terrain experimental conditions. We applied physical disector principle to estimating the number of synapses per neuron in the dentate gyrus of adult mice. First, we measured the numerical density of granule cells using the physical disector principle. Second, the density of medial perforant path to granule cell synapses was estimated using the bidirectional physical disector. Then, the volume ratio of molecular layer to granule cell layer was measured. With these numerial values, we successfully calculated the number of synapses per neuron. Individual granule cells have approximately 6500 synapses in the dentate gyrus of adult mice $(6,545{\pm}330)$, which are comparable to those of other researchers. Our results showed that the estimation of synapse numbers per neuron using the physical disector principle would provide accurate and precise information on the numerical alteration of synapses in diverse physiological and pathological conditions. Following analyses of synapse numbers using this method will contribute to the better understanding of structural synaptic plasticity in a variety of experimental animal models.