• The Korean Journal of Physiology and Pharmacology
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• 제11권6호
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• pp.247-251
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• 2007

Changes of single unit activity of CA1 hippocampus region were investigated in anesthetized Mongolian gerbils for six days following transient ischemia. Ischemia was produced immediately before the implantation of micro-wire recording electrodes. In control animals receiving pseudo-ischemic surgery, neither spontaneous neuronal activities ($5.70{\pm}0.4Hz$) nor the number of recorded neurons per animal changed significantly for six days. Correlative firings among simultaneously recorded neurons were weak (correlation coefficient > 0.6) in the control animals. Animals subjected to ischemia exhibited a significant elevation of neural firing at post-ischemic 12 hr ($9.95{\pm}0.9Hz$) and day 1 ($8.48{\pm}0.8Hz$), but a significant depression of activity at post-ischemic day 6 ($1.84{\pm}0.3Hz$) when compared to the activities of non-ischemic control animal. Ischemia significantly (correlation coefficient > 0.6) increased correlative firings among simultaneously recorded neurons, which were prominent especially during post-ischemic days 1, 2 and 6. Although the numbers of spontaneously active neurons recorded from control group varied within normal range during the experimental period, those from ischemic group changed in post-ischemic time-dependent manner. Temporal changes of the number of cells recorded per animal between control group and ischemic group were also significantly different (p = 0.0084, t = 3.271, df = 10). Cresyl violet staining indicated significant loss of CA1 cells at post-ischemic day 7. Overall, we showed post-ischemic time-dependent, differential changes of three characteristics, including spontaneous activity, network relationship and excitability of CA1 cells, suggesting sustained neural functions. Thus, histological observation of CA1 cell death till post-ischemic day 7 may not represent actual neuronal death.

• The Korean Journal of Physiology and Pharmacology
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• 제25권2호
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• pp.97-109
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• 2021

Neonatal hypoxia/ischemia (H/I), injures white matter, results in neuronal loss, disturbs myelin formation, and neural network development. Neuroinflammation and oxidative stress have been reported in neonatal hypoxic brain injuries. We investigated whether Paeoniflorin treatment reduced H/I-induced inflammation and oxidative stress and improved white matter integrity in a neonatal rodent model. Seven-day old Sprague-Dawley pups were exposed to H/I. Paeoniflorin (6.25, 12.5, or 25 mg/kg body weight) was administered every day via oral gavage from postpartum day 3 (P3) to P14, and an hour before induction of H/I. Pups were sacrificed 24 h (P8) and 72 h (P10) following H/I. Paeoniflorin reduced the apoptosis of neurons and attenuated cerebral infarct volume. Elevated expression of cleaved caspase-3 and Bad were regulated. Paeoniflorin decreased oxidative stress by lowering levels of malondialdehyde and reactive oxygen species generation and while, and it enhanced glutathione content. Microglial activation and the TLR4/NF-κB signaling were significantly down-regulated. The degree of inflammatory mediators (interleukin 1β and tumor necrosis factor-α) were reduced. Paeoniflorin markedly prevented white matter injury via improving expression of myelin binding protein and increasing O1-positive olidgodendrocyte and O4-positive oligodendrocyte counts. The present investigation demonstrates the potent protective efficiency of paeoniflorin supplementation against H/I-induced brain injury by effectually preventing neuronal loss, microglial activation, and white matter injury via reducing oxidative stress and inflammatory pathways.

• Sleep Medicine and Psychophysiology
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• 제4권1호
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• pp.15-28
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• 1997

Longterm memory is encoded in the neuronal connectivities of the brain. The most successful models of human memory in their operations are models of distributed and self-organized associative memory, which are founded in the principle of simulaneous convergence in network formation. Memory is not perceived as the qualities inherent in physical objects or events, but as a set of relations previously established in a neural net by simultaneousy occuring experiences. When it is easy to find correlations with existing neural networks through analysis of network structures, memory is automatically encoded in cerebral cortex. However, in the emergence of informations which are complicated to classify and correlated with existing networks, and conflictual with other networks, those informations are sent to the subcortex including hippocampus. Memory is stored in the form of templates distributed across several different cortical regions. The hippocampus provides detailed maps for the conjoint binding and calling up of widely distributed informations. Knowledge about the distribution of correlated networks can transform the existing networks into new one. Then, hippocampus consolidats new formed network. Amygdala may enable the emotions to influence the information processing and memory as well as providing the visceral informations to them. Cortico-striatal-pallido-thalamo-cortical loop also play an important role in memory function with analysis of language and concept. In case of difficulty in processing in spite of parallel process of informations, frontal lobe organizes theses complicated informations of network analysis through temporal processing. With understanding of brain mechanism of memory and information processing, the brain mechanism of mental phenomena including psychopathology can be better explained in terms of neurobiology and meuropsychology.

• Journal of information and communication convergence engineering
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• 제1권3호
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• pp.104-108
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• 2003

A biologically inspired fuzzy multilayer perceptron is proposed in this paper. The proposed algorithm is established under consideration of biological neuronal structure as well as fuzzy logic operation. We applied this suggested learning algorithm to benchmark problem in neural network such as exclusive OR and 3-bit parity, and to digit image recognition problems. For the comparison between the existing and proposed neural networks, the convergence speed is measured. The result of our simulation indicates that the convergence speed of the proposed learning algorithm is much faster than that of conventional backpropagation algorithm. Furthermore, in the image recognition task, the recognition rate of our learning algorithm is higher than of conventional backpropagation algorithm.

• 전기의세계
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• 제28권8호
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• pp.66-71
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• 1979

This paper describes electrical analysis of the information processing of the nervous system. A general-purpose electrical neuronal model for simulating the electrical activity in a single nerve cell and in small groups of nerve cell has constructed. This model consists of two basic electronic modules to represent respectively a "cell body" and an "axon (with synapses)", together with various related appurtenances. The primary advantages of this method are; holistic view, actual physical representation of various electrical activities in a single nerve cell, display of the activity of all nerve cells flexibility with respect to network parameters. Moreover, this model can effectively help push forward our general ability to explore and conceptualize the electrical activity of interconnected networks of nerve cell behaving in concert. Also, this electronic module technique is the best of various means for this task of realistic representation of aggregates of neurons.gregates of neurons.

• Proceedings of the KIEE Conference
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1943-1944
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• 2006

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin

• Proceedings of the KIEE Conference
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2484-2486
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• 2004

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms. such as the back propagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• Clinical and Experimental Pediatrics
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• 제54권6호
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• pp.241-245
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• 2011

Tuberous sclerosis complex (TSC) is a genetic multisystem disorder that results from mutations in the TSC1 or TSC2 genes, and is associated with hamartomas in several organs, including subependymal giant cell tumors. The neurological manifestations of TSC are particularly challenging and include infantile spasms, intractable epilepsy, cognitive disabilities, and autism. The TSC1- and TSC2-encoded proteins modulate cell function via the mammalian target of rapamycin (mTOR) signaling cascade, and are key factors in the regulation of cell growth and proliferation. The mTOR pathway provides an intersection for an intricate network of protein cascades that respond to cellular nutrition, energy levels, and growth factor stimulation. In the brain, TSC1 and TSC2 have been implicated in cell body size, dendritic arborization, axonal outgrowth and targeting, neuronal migration, cortical lamination, and spine formation. The mTOR pathway represents a logical candidate for drug targeting, because mTOR regulates multiple cellular functions that may contribute to epileptogenesis, including protein synthesis, cell growth and proliferation, and synaptic plasticity. Antagonism of the mTOR pathway with rapamycin and related compounds may provide new therapeutic options for TSC patients.

• Proceedings of the KAIS Fall Conference
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• 한국산학기술학회 2006년도 춘계학술발표논문집
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• pp.311-315
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• 2006

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin

• Smart Structures and Systems
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• 제5권2호
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• pp.139-152
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• 2009

Globally, civil infrastructures are deteriorating at an alarming rate caused by overuse, overloading, aging, damage or failure due to natural or man-made hazards. With such a vast network of deteriorating infrastructure, there is a growing interest in continuous monitoring technologies. In order to provide a true distributed sensor and control system for civil structures, we are developing a Structural Nervous System that mimics key attributes of a human nervous system. This nervous system is made up of building blocks that are designed based on mechanoreceptors as a fundamentally new approach for the development of a structural health monitoring and diagnostic system that utilizes the recently developed piezo-fibers capable of sensing and actuation. In particular, our research has been focused on producing a sensory nervous system for civil structures by using piezo-fibers as sensory receptors, nerve fibers, neuronal pools, and spinocervical tract to the nodal and central processing units. This paper presents up to date results of our research, including the design and analysis of the structural nervous system.