• The Korean Journal of Physiology and Pharmacology
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• v.1 no.3
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• pp.241-249
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• 1997

The aim of the present study is to examine the brainstem sites where the electrical stimulation produces a suppression of dorsal horn neuron responses of neuropathic rats. An experimental neuropathy was induced by a unilateral ligation of L5-L6 spinal nerves of rats. Ten to 15 days after surgery, the spinal cord was exposed and single-unit recording was made on wide dynamic range (WDR) neurons in the dorsal horn. Neuronal responses to mechanical stimuli applied to somatic receptive fields were examined to see if they were modulated by electrical stimulation of various brainstem sites. Electrical stimulation of periaqueductal gray (PAG), n. raphe magnus (RMg) or n. reticularis gigantocellularis (Gi) significantly suppressed responses of WDR neurons -to both noxious and non-noxious stimuli. Electrical stimulation of other brainstem areas, such as locus coeruleus. (LC) and n. reticularis paragigantocellularis lateralis (LPGi), produced little or no suppression. Microinjection of morphine into PAG, RMg, or Gi also produced a suppression as similar pattern to the case of electrical stimulation, whereas morphine injection into LC or LPGi exerted no effects. The results suggest that PAG, NRM and Gi are the principle brainstem nuclei involved in the descending inhibitory systems responsible for the control of neuropathic pain. These systems are likely activated by endogenous opioids and exert their inhibitory effect by acting on WDR neurons in the spinal cord.

• The Korean Journal of Physiology and Pharmacology
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• v.6 no.5
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• pp.241-246
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• 2002

We studied the excitatory action of morphine on the responses of dorsal horn neuron to iontophoretic application of excitatory amino acid and C-fiber stimulation by using the in vivo electrophysiological technique in the rat. In 137 of the 232 wide dynamic range (WDR) neurons tested, iontophoretic application of morphine enhanced the WDR neuron responses to N-methyl-D-aspartate (NMDA), kainate, and graded electrical stimulation of C-fibers. Morphine did not have any excitatory effects on the responses of low threshold cells. Morphine-induced excitatory effect at low ejection current was naloxone-reversible and reversed to an inhibitory action at high ejection current. NMDA receptor, calcium channel and intracellular $Ca^{2+}$ antagonists strongly antagonized the morphine-induced excitatory effect. These results suggest that changes in intracellular ionic concentration, especially $Ca^{2+},$ play an important role in the induction of excitatory effect of morphine in the rat dorsal horn neurons.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.308-313
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• 2005

For a real-time quadruped robot running control, there are many important objectives to consider. In this paper, the running control architecture based on global states, which describe the cyclic target motion, and central pattern is proposed. The main goal of the controller is how the robot can have robustness to an unpredictable environment with reducing calculation burden to generate control inputs. Additional goal is construction of a single framework controller to avoid discontinuities during transition between multi-framework controllers and of a training-free controller. The global state dependent neuron network induces adaptation ability to an environment and makes the training-free controller. The central pattern based approach makes the controller have a single framework, and calculation burden is resolved by extracting dynamic equations from the control loop. In our approach, the model of the quadruped robot is designed using anatomical information of a cat, and simulated in 3D dynamic environment. The simulation results show the proposed single framework controller is robustly performed in an unpredictable sloped terrain without training.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.523-529
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• 2002

The present study describes neural networks for stereoscopic vision, which are applied to identifying human speech. In speech recognition based on stereoscopic vision neural networks (SVNN), the similarities are first obtained by comparing input vocal signals with standard models. They are then given to a dynamic process in which both competitive and cooperative processes are conducted among neighboring similarities. Through the dynamic processes, only one winner neuron is finally detected. In a comparative study, the two-layered SVNN was 7.7% higher in recognition accuracies than the hidden Markov model (HMM). From the evaluation results, it was noticed that SVNN outperformed the existing HMM recognizer.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.242-248
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• 2007

This paper analyzes on the chaos characteristics of the chaotic neural networks and presents the convergence condition. Although the transient chaos of neural network sould be beneficial to overcome the local minimum problem and speed up the learning, the permanent chaotic response gives adverse effect on optimization problems and makes neural network unstable in general. This paper investigates the dynamic characteristics of the chaotic neural networks with the chaotic dynamic neuron, and presents the convergence condition for stabilizing the chaotic neural networks.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.159-167
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• 1996

In this paper, a new competitive learning algorithm called dynamic competitive learning (DCL) is presented. DCL is a supervised learning mehtod that dynamically generates output neuraons and nitializes weight vectors from training patterns. It introduces a new parameter called LOG (limit of garde) to decide whether or not an output neuron is created. In other words, if there exist some neurons in the province of LOG that classify the input vector correctly, then DCL adjusts the weight vector for the neuraon which has the minimum grade. Otherwise, it produces a new output neuron using the given input vector. It is largely learning is not limited only to the winner and the output neurons are dynamically generated int he trining process. In addition, the proposed algorithm has a small number of parameters. Which are easy to be determined and applied to the real problems. Experimental results for patterns recognition of remote sensing data and handwritten numeral data indicate the superiority of dCL in comparison to the conventional competitive learning methods.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Proceedings of the Korean Society of Developmental Biology Conference
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• 1998.07a
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• pp.59-60
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• 1998

A DNA construct containing rat T $\alpha$ 1 $\alpha$ -tuulin gene 5'-flanking sequence and GFP reporer gene was microinjected into 1-cell loach embryos. Neuron-specific FGP expression was observed in developing loach embryos and early stage fry. The results demonstrated that rat T $\alpha$ 1 $\alpha$ -tubulin gene promoter may be sufficient to specify gene expression to neurons in loach embryos. Thus, the use of GFP reporter controlled by T $\alpha$ 1 $\alpha$ -tubulin gene promoter may facilitate visualization of the dynamic processes of neural tissue development.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.169-175
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• 1996

In this paper, a new parallel neural network system that performs dynamic competitive learning is proposed. Conventional learning mehtods utilize the full dimension of the original input patterns. However, a particular attribute or dimension of the input patterns does not necessarily contribute to classification. The proposed system consists of parallel neural networks with the reduced input dimension in order to take advantage of the information in each dimension of the input patterns. Consensus schemes were developed to decide the netowrks performs a competitive learning that dynamically generates output neurons as learning proceeds. Each output neuron has it sown class threshold in the proposed dynamic competitive learning. Because the class threshold in the proposed dynamic learning phase, the proposed neural netowrk adapts properly to the input patterns distribution. Experimental results with remote sensing and speech data indicate the improved performance of the proposed method compared to the conventional learning methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.253-258
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• 2000

This paper presents an application of Kohonen neural networks to assess the dynamic security of power systems. The dynamic security assessment(DSA) is an important factor in power system operation, but conventional techniques have not achieved the desired speed and accuracy. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of Kohonen networks is a mapping of the pre-fault system conditions into the neurons based on the CCTs. The power flow on each line is used as the input data, and an activated output neuron has information of the CCT of each contingency. The trajectory of the activated neurons during load changes can be used in on-line DSA efficiently. The applicability of the proposed method is demonstrated using a 9-bus example.