• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.247-250
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• 1999

This paper presents characteristics of neuron-MOSFET for the implementation of logic circuits such at the inverter and D/A converter. Neuron-MOSFETS were fabricated using double poly CMOS process. From the measured results, it was found that noise margin of the inverter was dependant on the coupling ratio and a complete D/A characteristics of the source follower could be obtained by using any input Sate as a control gate.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.54-55
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• 2010

• 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 the Korea Society of Computer and Information
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• v.4 no.2
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• pp.27-38
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• 1999

This paper proposes a high speed digital neuron processor which uses the residue number system, making the high speed operation possible without carry propagation,. Consisting of the MAC(Multiplier and with Accumulator) operation unit, quotient operation unit and sigmoid function operation unit, the neuron processor is designed through 0.8$\mu$m CMOS fabrication. The result shows that the new implemented neuron processor can run at the speed of 19.2 nSec and the size can be reduced to 1/2 compared to the neuron processor implemented by the real number operation unit.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.324-329
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• 1992

A physical phenomenon is observed through analysis of the Hodgkin-Huxley's model that is, according to Maxwell field equations a fired neuron can yield magnetic fields. The magnetic signals are an output of the neuron as some type of information, which may be supposed to be the conscious control information. Therefore, study on neural networks should take the field effect into consideration. Accordingly, a study on the behavior of a unit neuron in the field is made and a new neuron model is proposed. A mathematical Memory-Learning Relation has been derived from these new neuron equations, some concepts of memory and learning are introduced. Two learning theorems are put forward, and the control mechanisms of memory are also discussed. Finally, a theory, i.e. Neural Electromagnetic(NEM) field theory is advanced.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.615-618
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• 2000

A number of studies have recently been made on hardware for a biological neuron f3r application with information processing functions of neural networks. We have been trying to produce hardware from the viewpoint that development of a new hardware neuron model is one of the important problems in the study of neural networks. In this paper, we first discuss the circuit structure of a pulse-type hardware neuron model with the enhancement-mode MOSFETs (E-MOSFETs). And we construct a pulse-type hardware neuron model using I-MOSFETs. As a result, it is shown that our proposed new model can exhibit firing phenomena even if the power supply voltage becomes less than 1.5[V]. So it is verified that our model is profitable for IC.

• Journal of the Korean Academy of Clinical Electrophysiology
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• v.2 no.3
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• pp.37-49
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• 2004

The purpose of this study was to determine the effect of spinal motor neuron excitability by cranial electrostimulation(CES). The fifteen Sparague-Dawley adult male rats were assigned to the three groups; GroupI(control), GroupII(low rate CES), GroupIII(high rate CES). Spinal motor neuron excitability was measured to use a computerized H reflex. The results of this study was as follows; M latency, M amplitude and H latency were no significant difference in all groups on repeated measured ANOVA(p>.05) but low rate CES and high rate CES groups were lower than ether group in comparative measurement of H amplitude and Hmax/Mmax ratio(p<.05). These results lead to the conclusion that spinal neuron excitability was influenced by CES. These results suggest that CES had the capability to lower spinal motor neuron excitability used synaptic blockade in spinal segment.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.32-39
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• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.