• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.61-99
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• 2016

This research investigated that participants' response time and recognition in the decision-making situation would vary according to either rational or emotional stimuli and analyzed how brain functions are related to each type of stimuli by means of fMRI. We tried to address the difference of cognitive processing between rational stimuli and emotional stimuli in the perspective of information processing theory. In order to achieve the research purpose above, we conducted two kinds of experiment studies. In study 1, subjects conducted decision-making task which selected which kind of information type the stimuli was after stimuli - rational stimuli or emotional stimuli - was randomly seen during experiment. During this experiment, we investigated the effect of each stimuli by measuring the duration from the onset time at which stimuli was shown to the response time at which subjects conducted decision-making. Furthermore, we compared the brain functions by finding out what kinds of brain areas were activated during the decision-making task. In study 2, subjects conducted recognition task at which subjects made a decision whether the stimuli was sees in the previous experiment or not. During the second experiment, we investigated the recognition effect by measuring the memory for each stimuli type. Moreover, we compared the cognitive processes during recognition by analyzing the differences of brain area functions. The results of two experiments above were as following. Firstly, regarding the response time as the effect of stimuli, we found that the effect of emotional stimuli was higher than that of rational stimuli. And regarding the recognition as the effect of stimuli, it was found that the effect of rational stimuli was higher than that of emotional stimuli. Secondly, the explanation about the characteristics of cognitive processes with the result of behavioral response by analyzing brain functions was as following. First of all, regarding the decision-making task which conducted for analyzing the effect of response time, the relatively high activated brain areas of rational stimuli were related with the functions of movement control or working memory, and the relatively high activated brain areas of emotional stimuli were connected with the functions of lingual processing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.61-67
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• 2004

We propose a new algorithm to generate additional training patterns using the brain-style information processing algorithm, that is, supervised and unsupervised learning models. This will be useful in the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on. We adopt the independent component analysis as an unsupervised model for generating exempalr patterns and multilayer perceptions as supervised models for verifying usefulness of the generated patterns. After statistical analysis of the proposed pattern generation algorithm, we verify successful operations of our algorithm through simulation of handwritten digit recognition with various numbers of training patterns.

• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.101-112
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• 2017

We investigated the neural representation of reward probability recognition and its neural connectivity with other regions of the brain. Using functional magnetic resonance imaging (fMRI), we used a simple guessing task with different probabilities of obtaining rewards across trials to assay local and global regions processing reward probability. The results of whole brain analysis demonstrated that lateral prefrontal cortex, inferior parietal lobe, and postcentral gyrus were activated during probability-based decision making. Specifically, the higher the expected value was, the more these regions were activated. Fronto-parietal connectivity, comprising inferior parietal regions and right lateral prefrontal cortex, conjointly engaged during high reward probability recognition compared to low reward condition, regardless of whether the reward information was extrinsically presented. Finally, the result of a regression analysis identified that cortico-subcortical connectivity was strengthened during the high reward anticipation for the subjects with higher cognitive impulsivity. Our findings demonstrate that interregional functional involvement is involved in valuation based on reward probability and that personality trait such as cognitive impulsivity plays a role in modulating the connectivity among different brain regions.

• Molecules and Cells
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• v.46 no.8
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• pp.461-469
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• 2023

The tail of the striatum (TS) is located at the caudal end in the striatum. Recent studies have advanced our knowledge of the anatomy and function of the TS but also raised questions about the differences between rodent and primate TS. In this review, we compare the anatomy and function of the TS in rodent and primate brains. The primate TS is expanded more caudally during brain development in comparison with the rodent TS. Additionally, five sensory inputs from the cortex and thalamus converge in the rodent TS, but this convergence is not observed in the primate TS. The primate TS, including the caudate tail and putamen tail, primarily receives inputs from the visual areas, implying a specialized function in processing visual inputs for action generation. This anatomical difference leads to further discussion of cellular circuit models to comprehend how the primate brain processes a wider range of complex visual stimuli to produce habitual behavior as compared with the rodent brain. Examining these differences and considering possible neural models may provide better understanding of the anatomy and function of the primate TS.

• Applied Microscopy
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• v.48 no.4
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• pp.102-109
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• 2018

Multiple synaptic boutons (MSBs) have been reported to be synapse with two or more postsynaptic terminals in one presynaptic terminal. These MSBs are known to be increased by various brain stimuli. In the motor cortex, increased number of MSB was observed in both acrobat training (AC) model and traumatic brain injury (TBI) model. Interestingly one is a physiological stimuli and the other is pathological insult. The purpose of this study is to compare the connectivity of MSBs between AC model and TBI model in the cerebral motor cortex, based on the hypothesis that the connectivity of MSBs might be different according to the models. The motor cortex was dissected from perfused brain of each experimental animal, the samples were prepared for routine transmission electron microscopy. The 60~70 serial sections were mounted on the one-hole grid and MSB was analyzed. The 3-dimensional analysis revealed that 94% of MSBs found in AC model synapse two postsynaptic spines from same dendrite. But, 28% MSBs from TBI models synapse two postsynaptic spines from different dendrite. This imply that the MSBs observed in motor cortex of AC model and TBI model might have different circuits for the processing the information.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.321-326
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• 2004

인간의 뇌와 같이 다양한 정보들을 구분하고 처리하며 기억할 수 있는 능력을 지닌 시스템은 현재 지능 기법이 적용되고 있는 제어, 통신, 인터넷 응용 기술, 경영 분야, 분석 및 예측 등의 분야에 응용될 수 있으며 그 성능을 효과적으로 향상시킬 것이다. 이러한 다양한 분야에 활용될 수 있는 통합 모델을 제시하고 점차 발전시켜나감으로써 미래에 기대되는 다양한 인간형 시스템, 친환경적 시스템, 인간보조 시스템 등의 공학적 시스템 측면과 인간을 대신할 수 있으면서 인간과 유사한 능력을 지닌 학습 시스템, 추론 시스템, 판단 시스템 등의 지능형 시스템 측면에서 활용 가능한 모델로 성장시켜 나갈 수 있다. 이에, 본 논문에서는 생물학적인 두뇌의 정보처리 메커니즘을 해석하고 공학적인 개념의 정립과 정보처리 흐름을 규명하고 정의함으로써 출력에 반영할 수 있는 모듈을 설계하고 최종적으로, 뇌 정보처리 메커니즘에 기반한 레이어를 설계하여 범용으로 사용될 수 있도록 하고자 한다. 본 논문에서 설명되는 레이어 구조는 공학적인 분야는 물론 생물학적 뇌 연구에도 활용될 수 있을 것으로 기대된다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.377-380
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• 2005

가장 완벽한 지능형 모델로 알려져 있는 두뇌는 인공 지능을 구현하기 위해 이해되어야 하는 많은 내용을 지니고 있다. 하지만, 현재까지는 두뇌의 생물학적인 정보처리 메커니즘은 극히 일부분에서 밝혀졌고 대부분의 내용은 추측이나 가정으로 설명되고 있다. 이미 밝혀진 두뇌의 정보처리 메커니즘에 기반한 정보처리 시스템은 다양한 응용 분야에 활용되어 지금의 시스템보다 월등한 성능을 보일 것으로 예상된다. 이에, 본 논문에서는 두뇌의 생물학적 흐름을 카테고리 별로 정리하였으며 이를 구현할 수 있는 소프트 컴퓨팅 기법을 소개한다. 다양한 소프트 컴퓨팅 기법을 이용하여 구현된 인공두뇌 모델은 정보처리 과정에서 자율적이며, 효과적인 정보처리 성능을 보여줌을 알 수 있다. 이는 인공 지능 시스템의 새로운 도약에 필요한, 정형화된 모델로 활용될 수 있을 것으로 기대 된다.

• Korean Journal of Biological Psychiatry
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• v.22 no.2
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• pp.29-33
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• 2015

The brain maintains homeostasis and normal microenvironment through dynamic interactions of neurons and neuroglial cells to perform the proper information processing and normal cognitive functions. Recent post-mortem investigations and animal model studies demonstrated that the various brain areas such as cerebral cortex, hippocampus and amygdala have abnormalities in neuroglial numbers and functions in subjects with mental illnesses including schizophrenia, dementia and mood disorders like major depression and bipolar disorder. These findings highlight the putative role and involvement of neuroglial cells in mental disorders. Herein I discuss the physiological roles of neuroglial cells such as astrocytes, oligodendrocytes, and microglia in maintaining normal brain functions and their abnormalities in relation to mental disorders. Finally, all these findings could serve as a useful starting point for potential therapeutic concept and drug development to cure unnatural behaviors and abnormal cognitive functions observed in mental disorders.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.178-190
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• 2021

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

• Journal of Korean Elementary Science Education
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• v.24 no.1
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• pp.86-101
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• 2005

In this study, a brain-basrd model for science teaching and learning was developed based on the natural processes which human acquire knowledge about a natural object or on event, the major domains of science educational objectives of the national curriculum, and the human brain's organizational patterns and functions. In the model, each educational objective domain is related to the brain regions as follows: The affective domain is related to the limbic system, especially amygdala of human brain which is involved in emotions, the psychomotor domain is related to the occipital lobes of human brain which perform visual processing, temporal lobes which perform functions of language generating and understandng, and parietal lobes which receive and process sensory information and execute motor activities of body, and the cognitive domain is related to the frontal and prefrontal lobes which are involved in think-ing, planning, judging, and problem solving. The model is a kind of procedural model which proceed fiom affective domain to psychomotor domain, and to cognitive domain of science educational objective system, and emphasize the order of each step and authentic assessment at each step. The model has both properties of circularity and network of activities. At classrooms, the model can be used as various forms according to subjects and student characteristics. STS themes can be appropriately covered by the model.