• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.5-8
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• 2000

Voltage mode chaotic neuron has been designed in integrated circuit and fabricated by using 0.8$\mu\textrm{m}$ single poly CMOS technology. The fabricated CMOS chaotic neuron consist of chaotic signal generator and sigmoid output function. This paper presents an analysis of the chaotic behavior in the voltage mode CMOS chaotic neuron. From empirical equations of the chaotic neuron, the dynamical responses such as time series, bifurcation, and average firing rate are calculated. And, results of experiments in the single chaotic neuron and chaotic neural networks by two neurons are shown and compared with the simulated results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.6-157
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• 2001

We estimated various neuron functions to construct of engineering neurons, which are the combination of sigmoid, linear, sine, quadric, double/single bended, soft max/minimum functions. These combinations are estimated by the property on the potential surface between the learning points, calculation speed, and learning convergence; because the surface depends on the inference ability of a neuron system; and speed and convergence are depend on the efficiency on the points of engineering applications. After the evaluating discussions, we can select more appropriate combination than original sigmoid function´s, which is single bended function and linear one. The combination ...

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.47-50
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• 2000

In this paper, we propose PID type direct control method using single neuron neural network. The proposed method has an output error and 2 time-delay as inputs and is designed to have input weights composed of P, I, D parameters to be controlled through teaming. We could verify the better performance of this system than the conventional method through simulations. And the reduced calculation, due to single neuron, makes it possible the real time processing, and the simple implementation.

• Journal of Biomedical Engineering Research
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• v.2 no.2
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• pp.127-140
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• 1981

This paper describes the mechanism of information processing in the nervous system through neuron pool model which is consisted of six single neural models. In the neuron pool model, summation characteristic of stimulus satisfies those of real nervous system and output impulse rate increases linearly to the input stimulus. Occlusion phenomena of the neuron pool model is approached to those of real nervous system and also if the threshold potential within sutlirninal fringe is increased, facilitation phenomena appreared. Therefore, the results of this study suggest that we can construct large neuron pool with many single neural models and verify the mechanism of information processing in the wide part of nervous system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.395-398
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• 1998

In this paper, a method for the improvement of learning speed and convergence rate was proposed applied it to physiological neural structure with the advantages of artificial neural networks and fuzzy theory to physiological neuron structure, To compare the proposed method with conventional the single layer perception algorithm, we applied these algorithms bit parity problem and pattern recognition containing noise. The simulation result indicated that our learning algorithm reduces the possibility of local minima more than the conventional single layer perception does. Furthermore we show that our learning algorithm guarantees the convergence.

• 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.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.921-924
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• 1998

In this paper, we investigate a system identification with a single neuron. The identifed performance of multi-perceptron is compared with proposed identification model and simulted.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1013-1016
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• 1993

This paper describes a new type of neuron model, the inputs of which are interfered with one another. It has a high mapping ability with only single unit. The learning speed is considerably improved compared with the conventional linear type neural networks. The proposed neuron model was successfully applied to the prediction problem of chaotic time series signal.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.6
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• pp.755-759
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• 2014

We propose an integrate-and-fire neuron circuit and synaptic devices with the floating body MOSFETs. The synaptic devices consist of a floating body MOSFET to imitate biological synaptic characteristics. The synaptic learning is performed by hole accumulation. The synaptic device has short-term and long-term memory in a single silicon device. I&F neuron circuit emulate the biological neuron characteristics such as integration, threshold triggering, output generation, and refractory period, using floating body MOSFET. The neuron circuit sends feedback signal to the synaptic transistor for long-term memory.

• Journal of International Academy of Physical Therapy Research
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• v.10 no.2
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• pp.1785-1790
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• 2019

Background: Lumbar disc herniation (LDH) causes neurological symptoms by compression of the dura mater and nerve roots. Due to the changed in proprioception inputs that can result in abnormal postural pattern, delayed reaction time, and changed in deep tendon reflex. Objective: To investigate the effects of lumbar stabilization exercises on motor neuron excitability and neurological symptoms in patients with LDH. Design: Randomized Controlled Trial (single blind) Methods: Thirty patients with LDH were recruited; they were randomly divided into the balance center stabilization resistance exercise group (n=15) and the Nordic walking group (n=15). Each group underwent their corresponding 20-minute intervention once a day, four times a week, for four weeks. Participants' motor neuron excitability and low back pain were assessed before and after the four-week intervention. Results: There were significant differences in all variables within each group (p<.05). There were significant differences between the experimental and control groups in the changes of upper motor neuron excitability and pain (p<.05), but not in the changes of lower motor neuron excitability and Korean Oswestry Disability Index. Conclusion: Lumbar stabilization exercises utilizing concurrent contraction of deep and superficial muscles improved low back function in patients with LDH by lowering upper motor neuron excitability than compared to exercises actively moving the limbs. Lumbar stabilization exercises without pain have a positive impact on improving motor neuron excitability.