• 제목/요약/키워드: Neural activations

검색결과 32건 처리시간 0.026초

A Predictive Model of Situation Awareness with ACT-R

  • Kim, Junghwan;Myung, Rohae
    • 대한인간공학회지
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    • 제35권4호
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    • pp.225-235
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    • 2016
  • Objective: The aim of this study is to model all levels of situation awareness (SA), which would be able to predict situation awareness quantitatively. Background: When measuring situation awareness, directly measuring SA methods such as SAGAT and SART have been utilized. Several approaches (cognitive modeling approaches) were introduced to model SA but level 3 SA was not completed. For real-life situation, however, it is necessary to detect the problematic level of SA rather than overall SA. Therefore, we proposed a new model of all levels of SA in this study. Method: In order to model all levels of SA, this study chose factors in ACT-R architecture through literature review. ATC (Air Traffic Control)-related simulation task was video-taped to analyze human behaviors in order to model all levels of SA including level 3. Results: As a result, regression analyses show that cognitive activities (neural activations) represented for all levels of SA were highly correlated with SAGAT. Conclusion: In conclusion, neural activations in ACT-R could be proved to be effective to model all levels of SA. Application: Our SA model could be used to predict all levels of SA quantitatively without directly measuring the SA of operators.

그림의 부호화 과정과 신경기제 : fMRI 연구 (Neural Substrates of Picture Encoding: An fMRI Study)

  • 강은주;김희정;김성일;나동규;이경민;나덕렬;이정모
    • 인지과학
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    • 제13권1호
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    • pp.23-40
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    • 2002
  • 본 연구는 fMRI를 이용하여 정상인에 있어서 자극 유형, 특히 그림 자극의 부호화와 관련된 두뇌영역을 확인하고자 하였다. Scan 1에서는 그림 자극의 부호화 과정에 관여하는 두뇌 영역을 화인하기 위하여 어의범주 판단 과정 중에 그림과 단어에 대하여 비교 관찰하였으며 Scan 2에서는 그림자극에 대하여 그림 명명과제와 어의범주 판단과제를 비교하여 과제 유형에 따른 그림의 부호화에 관여하는 두뇌 활성화 영역을 연구하였다. 피험자는 어의범주(인공물/자연물)에 따라 마우스를 눌러 반응하거나(Scan 1) 그림명명이나 범주 소속 여부를 속으로 말하도록(subvocal response)(Scan 2) 요구되었다. 자극의 유형과 무관하게 부호화 중에 좌측 전전두 영역 양측의 두정엽, 그리고 양측의 고차시각 피질 등이 공통적으로 활성화 되었다. 그림보다 단어의 부호화에는 좌측 하 전전두엽, 우측 전측 전전두 영역, 양측의 도(insula), 좌측 두정-측두엽 등 광범위한 언어/개념관련 두뇌 영역에서 더 높은 활성화가 발견되는 반면, 그림의 부호화에는 양측의 고차 시각 영역과 해마방화(parahippocampal gyrus) 영역에서 더 높은 활성화가 관찰되었다. 이는 동일한 어의판단 과제를 수행하는 과정에도 단어는 어의적/언어적 처리가 그림은 지각적 정보처리 및 novelty 관련 정보처리가 서로 다른 해부학적인 영역에 의하여 매개됨을 의미한다. 그림 명명과제나 어의범주 판단과제 모두를 속으로 말하는 수행(Scan 2)은 배측 하 전전두 영역, 즉 Broca영역의 활동 증가를 야기시켰으며, 특히 명명과제 수행에는 어의범주 판단과제를 수행할 때에 비하여 양측의 시각영역에서 더 많은 활성화가 발견되었는데, 이는 대상의 명칭을 인출하는 과정에 고차 시각정보 처리가 더 많이 관여하였을 가능성을 시사한다.

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반복적 양측 운동학습에 따른 대뇌 및 소뇌 피질 활성화 (Activations of Cerebral and Cerebellar Cortex Induced by Repetitive Bilateral Motor Excercise)

  • 태기식;송성재;김영호
    • 대한의용생체공학회:의공학회지
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    • 제28권1호
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    • pp.139-147
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    • 2007
  • The aim of this study was to evaluate effects of short-tenn repetitive-bilateral excercise on the activation of motor network using functional magnetic resonance imaging (fMRI). The training program was performed at 1 hr/day, 5 days/week during 6 weeks. Fugl-Meyer Assessments (FMA) were performed every two weeks during the training. We compared cerebral and cerebellar cortical activations in two different tasks before and after the training program: (1) the only unaffected hand movement (Task 1); and (2) passive movements of affected hand by the active movement of unaffected hand (Task 2). fMRI was performed at 3T with wrist flexion-extension movement at 1 Hz during the motor tasks. All patients showed significant improvements of FMA scores in their paretic limbs after training. fMRI studies in Task 1 showed that cortical activations decreased in ipsilateral sensorimotor cortex but increased in contralateral sensorimotor cortex and ipsilateral cerebellum. Task 2 showed cortical reorganizations in bilateral sensorimotor cortex, premotor area, supplemetary motor area and cerebellum. Therefore, this study demonstrated that plastic changes of motor network occurred as a neural basis of the improvement subsequent to repetitive-bilateral excercise using the symmetrical upper-limb ann motion trainer.

저주파 필터 특성을 갖는 다층 구조 신경망을 이용한 시계열 데이터 예측 (Time Series Prediction Using a Multi-layer Neural Network with Low Pass Filter Characteristics)

  • Min-Ho Lee
    • Journal of Advanced Marine Engineering and Technology
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    • 제21권1호
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    • pp.66-70
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    • 1997
  • In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

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시계열 예측을 위한 1, 2차 미분 감소 기능의 적응 학습 알고리즘을 갖는 신경회로망 (A neural network with adaptive learning algorithm of curvature smoothing for time-series prediction)

  • 정수영;이민호;이수영
    • 전자공학회논문지C
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    • 제34C권6호
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    • pp.71-78
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    • 1997
  • In this paper, a new neural network training algorithm will be devised for function approximator with good generalization characteristics and tested with the time series prediction problem using santaFe competition data sets. To enhance the generalization ability a constraint term of hidden neuraon activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. A hybrid learning algorithm of the error-back propagation and Hebbian learning algorithm with weight decay constraint will be naturally developed by the steepest decent algorithm minimizing the proposed cost function without much increase of computational requriements.

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새로운 신경회로망 구조를 이용한 로봇 매니퓰레이터의 적응 제어 방식 (Adaptive Control Method of Robot Manipulators using a New Neural Network)

  • 정경권;김인;이승현;이현관;엄기환
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 1999년도 추계종합학술대회
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    • pp.210-213
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    • 1999
  • 본 논문에서는 로봇 매니퓰레이터 제어를 위해 새로운 신경회로망을 제안한다. 제안한 신경회로망구조는 은닉층과 출력층의 출력이 피드백 층을 거쳐 다시 은닉층과 출력층으로 피드백되는 구조이다. 피드백 층은 한번의 시간 지연을 갖는다. 제안한 신경회로망의 학습은 일반적인 오차 역전파 알고리즘을 사용한다. 로봇 매니퓰레이터를 대상으로 시뮬레이션과 실험을 통해서 제안한 신경회로망 구조의 유용성을 확인한다.

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Kainic acid 유발 간질 생쥐모델에서 소부혈(少府穴) 침치료의 해마 신경세포 보호효과연구 (The Neuroprotective Effect of Acupuncture Treatment at Shaofu (HT8) on Kainic Acid-induced Epilepsy Mouse Model.)

  • 김윤영;민상연;김지용;김장현
    • 대한한의학회지
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    • 제31권5호
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    • pp.167-178
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    • 2010
  • Objectives: The present study investigated the effects of acupuncture treatment and their mechanism by using the kainic acid (KA)-induced epilepsy mouse model. Materials and Methods: The seizure was induced by an intraperitoneal (i.p.) injection of 30 mg/kg KA, and the acupuncture treatment was subsequently administered to acupoint Shaofu(HT8) bilaterally with two pretreatment sessions before injection (total 3 times over 3 days). Twenty four hours after injection, we observed the survival of neuronal cells in the CA3 region of the hippocampus. In addition, the activation of microglia and astrocytes was observed by using CD11b and GFAP immunohistochemistry in the same region. Results: The results indicate that acupuncture treatment reduced the rate of neural cell death in the CA3 region of the hippocampus and decreased the activations of microglia and astrocytes in this region. Conclusion: These results demonstrate that acupuncture treatment protects hippocampal neuronal cell death from KA-induced epileptic seizure by inhibiting the activations of microglia and astrocytes.

시각추적과제의 뇌자도 : 예비실험 (A Pilot MEG Study During A Visual Search Task)

  • 김성훈;이상건;김광기
    • Annals of Clinical Neurophysiology
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    • 제8권1호
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    • pp.44-47
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    • 2006
  • Background: The present study used magnetoencephalography (MEG) to investigate the neural substrates for modified version of Treisman's visual search task. Methods: Two volunteers who gave informed consent participated MEG experiment. One was 27- year old male and another was 24-year-old female. All were right handed. Experiment were performed using a 306-channel biomagnetometer (Neuromag LTD). There were three task conditions in this experiment. The first was searching an open circle among seven closed circles (open condition). The second was searching a closed circle among seven uni-directionally open circles (closed condition). And the third was searching a closed circle among seven eight-directionally open circles (random (closed) condition). In one run, participants performed one task condition so there were three runs in one session of experiment. During one session, 128 trials were performed during every three runs. One participant underwent one session of experiment. The participant pressed button when they found targets. Magnetic source localization images were generated using software programs that allowed for interactive identification of a common set of fiduciary points in the MRI and MEG coordinate frames. Results: In each participant we can found activations of anterior cingulate, primary visual and association cortices, posterior parietal cortex and brain areas in the vicinity of thalamus. Conclusions: we could find activations corresponding to anterior and posterior visual attention systems.

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헛디딤 탐지의 신경 상관: 기능적 자기공명 영상 연구 (Neural Correlates of Faux Pas Detection: An fMRI Study)

  • 박민;이승복;윤효운;김혜리
    • 인지과학
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    • 제21권1호
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    • pp.77-93
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    • 2010
  • 본 연구는 마음이론 능력 측정 과제의 하나인 헛디딤 탐지를 수행하는데 관여하는 신경상관 영역을 확인하려는 것이었다. 기능적 자기공명영상 기법을 이용하여 헛디딤 이야기 문장과 헛디딤을 포함하지 않는 통제 이야기 문장을 제시하였을 때 나타나는 뇌 활성화 영역을 비교하였다. 양 반구의 상전두회(BA 6/9)와 설전소엽(BA 7), 좌반구의 내전두회(BA 9), 상측두회(BA 38), 하측두회(BA 20)와 우반구의 하두정소엽(BA 40), 중심후회(BA 1), 설회(BA 18), 횡전두회(BA 41) 등의 영역에서 활성화가 나타났다. 헛디딤 탐지를 하는 동안 안와전두피질과 편도의 활성화는 나타나지 않았다. 이런 결과는 마음이론과 관련된 뇌 활성화는 과제가 이끌어내는 마음상태의 유형에 달려있음을 시사한다.

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Robustness를 형성시키기 위한 Hybrid 학습법칙을 갖는 다층구조 신경회로망 (Multi-layer Neural Network with Hybrid Learning Rules for Improved Robust Capability)

  • 정동규;이수영
    • 전자공학회논문지B
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    • 제31B권8호
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    • pp.211-218
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    • 1994
  • In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

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