• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.60-64
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• 1995

The conventional neural network models are a parody of biological neural structures, and have very slow learning. In order to emulate some dynamic functions, such as learning and adaption, and to better reflect the dynamics of biological neurons, M.M. Gupta and D.H. Rao have developed a 'dynamic neural model'(DNU). Proposed neural unit model is to introduce some dynamics to the neuron transfer function, such that the neuron activity depends on internal states. Integrating an dynamic elementry processor within the neuron allows the neuron to act dynamic response Numerical examples are presented for a model system. Those case studies showed that the proposed DNU is so useful in practical sense.

• Annals of Clinical Neurophysiology
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• v.18 no.1
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• pp.18-20
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• 2016

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron degenerative disease that clinically manifests both upper and lower motor neuron signs. However, it is unknown where and how the motor neuron degeneration begins, and conflicting hypotheses have been suggested. Recent advanced radiological techniques enable us to look into ALS neuropathology in vivo. Herein, we report a case with upper motor neuron-predominant ALS in whom the results of brain magnetic resonance imaging (MRI) and myelin water fraction MRI suggest axonal degeneration.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.157-163
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• 2000

In this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algoithm(RVEGA) was presented for stabilization of an Inverter Pendulum(IP) system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the determinations of input or output neuron, the deleted neuron and the activation functions types are given according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. Through the simulations, we showed that the finally acquired optimal ENNC was successfully applied to the stabilization control of an IP system.

• Proceedings of the Korea Society of Environmental Biology Conference
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• 2002.05a
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• pp.26-26
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• 2002

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.2
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• pp.174-179
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• 2017

We designed the CMOS analog integrate and fire (I&F) neuron circuit for driving memristor based on resistive-switching random access memory (RRAM). And we fabricated the RRAM device that have $HfO_2$ switching layer using atomic layer deposition (ALD). The RRAM device has gradual set and reset characteristics. By spice modeling of the synaptic device, we performed circuit simulation of synaptic device and CMOS neuron circuit. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, two inverters for pulse generation, a refractory part, and finally a feedback part for learning of the RRAM. We emulated the spike-timing-dependent-plasticity (STDP) characteristic that is performed automatically by pre-synaptic pulse and feedback signal of the neuron circuit. By STDP characteristics, the synaptic weight, conductance of the RRAM, is changed without additional control circuit.

• Proceedings of the KSAR Conference
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• 2002.06a
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• pp.19-19
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• 2002

This study was to examine whether the in vitro differentiated neural cells derived from human embryonic stem (hES, MB03) cells can be survived and expressed tyrosin hydroxylase(TH) in grafted normal or PD rat brain. To differentiate in vitro into neural cells, embryoid bodies (EB: for 5 days, without mitogen) were formed from hES cells, neural progenitor cells(neurosphere, for 7-10 days, 20 ng/㎖ of bFGF added N2 medium) were produced from EB, and then finally neurospheres were differentiated into mature neuron cells in N2 medium(without bFGF) for 2 weeks. In normal rat brain, neural progenitor cells or mature neuron cells (1×10/sup 7/ cells/㎖) were grafted to the striatum of normal rats. After 2 weeks, when the survival of grafted hES cells was examined by immunohistochemical analysis, the neural progenitor cell group indicated higher BrdU, NeuN＋, MAP2＋ and GFAP＋ than mature neuron cell group in grafted sites of normal rats. This result demonstrated that the in vivo differentiation of grafted hES cells be increased simultaneously in both of neuronal and glial cell type. Also, neural progenitor cell grafted normal rats expressed more TH pattern than mature neuron cells. Based on this data, as a preliminary test, when the neural progenitor cells were grafted into the striatum of 6-hydroxydopamine lesioned PD rats, we confirmed the cell survival (by double staining of Nissl and NeuN) and TH expression. This result suggested that in vitro differentiated neural progenitor cells derived from hES cells are more usable than mature neuron cells for the neural cell grafting in animal model and those grafted cells were survived and expressed TH in normal or PD rat brain.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.565-570
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• 2013

The mirror neuron regions which are distributed in cortical area handled a functionality of intention recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper an automated intention recognition system is proposed by applying computational model of mirror neuron system to the human-robot interaction system. The computational model of mirror neuron system is designed by using dynamic neural networks which have model input which includes sequential feature vector set from the behaviors from the target object and actor and produce results as a form of motor data which can be used to perform the corresponding intentional action through the imitative learning and estimation procedures of the proposed computational model. The intention recognition framework is designed by a system which has a model input from KINECT sensor and has a model output by calculating the corresponding motor data within a virtual robot simulation environment on the basis of intention-related scenario with the limited experimental space and specified target object.

• Journal of the Korea Computer Graphics Society
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• v.27 no.4
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• pp.1-9
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• 2021

Segmenting the compartments of neurons, such as axons, dendrites, and cell bodies, is helpful in the analysis of neurological phenomena. Recently, there have been several studies to segment the compartments through deep learning. However, this approach has the potential to include errors in the results due to noise in data and differences between training data and actual data. Therefore, in order to use these for actual analysis, it is essential to proofread the results. The proofreading process requires a lot of effort and time because an expert must perform it manually. We propose an interactive neuron structure proofreading method that can more easily correct errors in the segmentation results of a deep learning. This method proofread the neuron structure based on the characteristics of the neuron with structural consistency, so that a high-accuracy proofreading result can be obtained with less interaction.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.921-924
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• 1998

In this paper, we investigate a system identification with a single neuron. The identifed performance of multi-perceptron is compared with proposed identification model and simulted.

• Clinical and Experimental Reproductive Medicine
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• v.29 no.2
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• pp.117-127
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• 2002

Objective: This study was to investigate the generation of the functional neuron derived from human embryonic stem (hES, MB03) cells on in vitro neural cell differentiation system. Methods: For neural progenitor cell formation derived from hES cells, we produced embryoid bodies (EB: for 5 days, without mitogen) from hES cells and then neurospheres (for $7{\sim}10$ days, 20 ng/ml of bFGF added N2 medium) from EB. And then finally for the differentiation into mature neuron, neural progenitor cells were cultured in i) N2 medium only (without bFGF), ii) N2 supplemented with 20 ng/ml platelet derived growth factor-bb (PDGF-bb) or iii) N2 supplemented with 5 ng/ml brain derived neurotrophic factor (BDNF) for 2 weeks. Identification of neural cell differentiation was carried out by immunocytochemistry using $\beta_{III}$-tubulin (1:250), MAP-2 (1:100) and GFAP (1:500). Also, generation of functional neuron was identified using anti-glutamate (Sigma, 1:1000), anti-GABA (Sigma, 1:1000), anti-serotonin (Sigma, 1:1000) and anti-tyrosine hydroxylase (Sigma, 1:1000). Results: In vitro neural cell differentiation, neurotrophic factors (PDGF and BDNF) treated cell groups were high expressed MAP-2 and GFAP than non-treated cell group. The highest expression pattern of MAP-2 and $\beta_{III}$-tubulin was indicated in BDNF treated group. Also, in the presence of PDGF-bb or BDNF, most of the neural cells derived from hES cells were differentiated into glutamate and GABA neuron in vitro. Furthermore, we confirmed that there were a few serotonin and tyrosine hydroxylase positive neuron in the same culture environment. Conclusion: This results suggested that the generation of functional neuron derived from hES cells was increased by addition of neurotrophic factors such as PDGF-bb or BDNF in b-FGF induced neural cell differentiation system and especially glutamate and GABA neurons were mainly produced in the system.