• BMB Reports
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• v.49 no.9
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• pp.459-473
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• 2016

Neurodegenerative diseases (NDs) often involve the formation of abnormal and toxic protein aggregates, which are thought to be the primary factor in ND occurrence and progression. Aged neurons exhibit marked increases in aggregated protein levels, which can lead to increased cell death in specific brain regions. As no specific drugs/therapies for treating the symptoms or/and progression of NDs are available, obtaining a complete understanding of the mechanism underlying the formation of protein aggregates is needed for designing a novel and efficient removal strategy. Intracellular proteolysis generally involves either the lysosomal or ubiquitin-proteasome system. In this review, we focus on the structure and assembly of the proteasome, proteasome-mediated protein degradation, and the multiple dynamic regulatory mechanisms governing proteasome activity. We also discuss the plausibility of the correlation between changes in proteasome activity and the occurrence of NDs.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.250-253
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• 1998

This paper presents a multi-modal neural network composed of a preprocessing module and a multi-layer neural network module in order to enhance the nonlinear characteristics of neural network. The former module is based on spectral method using Chebyschev polynomials and transforms input data into spectra. The latter module identifies the system using the spectra generated by the preprocessing module. The omnibus numerical experiments show that the method is applicable to many a nonlinear dynamic system in the real world, and that preprocessing using Chebyschev polynomials reduces the number of neurons required for the multi-layer neural network.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.184-191
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• 2001

This paper proposes a prefilter type velocity compensating control system using modified chaotic neural networks for the trajectory control of robotic manipulator. Since the structure of modified chaotic neural networks(MCNN) and neurons have highly nonlinear dynamic characteristics, MCNN can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis robot manipulator is designed by MCNN. The MCNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on-line learning and the performance is excellent. The MCNN controller showed much better control performance and shorter calculation time compared to the RNN controller, Another advantage of the proposed controller could by attached to conventional robot controller without hardware changes.

• Progress in Medical Physics
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• v.9 no.2
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• pp.95-104
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• 1998

The dynamic properties of the 3rd-order neuron of the retina was investigated by using conventional intracellular recording techniques. Experiments were performed in the superfused retina-eyecup preparation of the channel catfish, Ictalurus punctatus. The cornea, iris, lens, and vitreous were removed by absorption with Kimwipe tissue under the dissection microscope thereby exposing the retina in a hemi -eyecup. The electrical signal was amplified by electrometer, viewed on oscilloscope. Regular signals from the cells were recorded on a penwriter and stored by data recorder and computer. Full-field, spot or annular light stimuli were generated on a computer monitor and focused onto the retina. Baclofen hyperpolarized the dark membrane potential, suppressed sustained component and enhanced transient component of the ON-sustained cell with a large transient component, but did not affect the surround antagonism of the cell. Baclofen selectively suppressed responses evoked by moving bar light stimuli on the ON-OFF transient cell. The results suggest that transient cells have directional selectivity in the inner retina. These dynamic properties of amacrine and ganglion cells were modulated by baclofen. Therefore, it is presumed that there is baclofen-induced directional selectivity in ON-OFF transient cells in the catfish retina.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.179-184
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• 2006

An intuitive understanding of the dynamic pattern generation in asymmetric networks may be considered an essential component in developing models for the dynamic information processing. It has been reported that the neural network with cyclic connections generates multiple limit cycles. The dynamics of discrete time network with cyclic connections has been investigated intensively. However, the dynamics of a cyclic connection neural network in continuous-time has not been well-known due to the considerable complexity involved in its calculation. In this paper, the dynamic behavior of a continuous-time cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$, has been investigated. Furthermore, the dynamics and stability of the network have been analyzed using a piece-wise linear approximation.

• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.290-299
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• 2009

Evolutionary Artificial Neural Networks (EANNs) has been highly effective in Artificial Intelligence (AI) and in training NPCs in video games. When EANNs is applied to design game NPCs' smart AI which can make the game more interesting, there always comes two important problems: the more complex situation NPCs are in, the more complex structure of neural networks needed which leads to large operation cost. In this paper, the Dynamic State Evolutionary Neural Networks (DSENNs) is proposed based on EANNs which deletes or fixes the connection of the neurons to reduce the operation cost in evolution and evaluation process. Darwin Platform is chosen as our test bed to show its efficiency: Darwin offers the competitive team game playing behaviors by teams of virtual football game players.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.5
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• pp.46-55
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• 2000

This paper presents a direct adaptive control of robot system using chaotic neural networks and PD controller. The chaotic neural networks have robust nonlinear dynamic characteristics because of the sufficient nonlinearity in neuron itself, and the additional self-feedback and inter-connecting weights between neurons in same layer. Since the structure and the learning method are not appropriate for applying in control system, this neural networks have not been applied. In this paper, a modified chaotic neural networks is presented for dynamic control system. To evaluate the performance of the proposed neural networks, these networks are applied to the trajectory control of the three-axis PUMA robot. The structure of controller consists of PD controller and chaotic neural networks in parallel for conforming the stability in initial learning phase. Therefore, the chaotic neural network controller acts as a compensating controller of PD controller.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.136-141
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• 2007

This paper introduces an artificial neuron which is a nano composite continuous sensor. The continuous nano sensor is fabricated as a thin and narrow polymer film sensor that is made of carbon nanotubes composites with a PMMA or a silicone matrix. The sensor can be embedded onto a structure like a neuron in a human body and it can detect deteriorations of the structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensor can form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods. The artificial neuron is expected to effectively detect damage in large complex structures including composite helicopter blades and composite aircraft and vehicles.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.634-642
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• 1993

In this paper a new trajectory control method is proposed for a robot manipulator using a time delay neural network(TDNN) as a feedforward controller with an algorithm to learn inverse dynamics of the manipulator. The TDNN structure has so favorable characteristics that neurons can extract more dynamic information from both present and past input signals and perform more efficient learning. The TDNN neural network receives two normalized inputs, one of which is the reference trajectory signal and the other of which is the error signals from the PD controller. It is proved that the normalized inputs to the TDNN neural network can enhance the learning efficiency of the neural network. The proposed scheme was investigated for the planar robot manipulator with two joints by computer simulation.