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Daegu, South Korea

Daegu National University of Education, commonly abbreviated as Daegu-gyodae in Korean, is one of National University of Education which provide training courses for preliminary teachers in the public primary school of South Korea.Founded in 1950, DNUE was previously called Daegu Normal School. Its first president was Kim Young-gi . In 1963, it was renamed as Daegu Gyoyuk Dae. It concentrated on primary school education, not including secondary school course. It had the last change of name in 1993, taking the name it continues to use today. The current president is Nam Seung-in , selected in 2011. About 85 instructors are employed by the university. Wikipedia.

Hong D.-G.,Kangwon National University | Kim S.-Y.,Kangwon National University | Lim W.,Daegu National University of Education
Journal of the Korean Physical Society | Year: 2011

We study the effect of network structure on the stochastic spiking coherence (i.e., collective coherence emerging via cooperation of noise-induced neural spikings) in an inhibitory population of subthreshold neurons (which cannot fire spontaneously without noise). Previously, stochastic spiking coherence was found to occur for the case of global coupling. However, "sparseness" of a real neural network is well known. Hence, we investigate the effect of sparse random connectivity on the stochastic spiking coherence by varying the average number of synaptic inputs per neuron Msyn. From our numerical results, stochastic spiking coherence seems to emerge if Msyn is larger than a threshold M*syn whose dependence on the network size N seems to be quite weak. This stochastic spiking coherence may be well visualized in a raster plot of neural spikes. For a coherent case, partially-occupied "stripes" (composed of spikes and indicating collective coherence) appear. As Msyn is decreased from N - 1 (globally-coupled case), the average occupation degree of spikes increases very slowly. On the other hand, the average pacing degree between spikes (representing the precision of spike timing) decreases slowly, but near M*syn its decrease becomes very rapid. This decrease in the pacing degree can also be well seen through merging of multiple peaks in the interspike interval histograms. Due to the effect of the pacing degree, the degree of stochastic spiking coherence decreases abruptly near the threshold M*syn.

Kim S.-Y.,LABASIS Corporation | Lim W.,Daegu National University of Education
Journal of the Korean Physical Society | Year: 2013

Sparsely-synchronized cortical rhythms, associated with diverse cognitive functions, have been observed in electric recordings of brain activity. At the population level, cortical rhythms exhibit small-amplitude fast oscillations while at the cellular level, individual neurons show stochastic firings sparsely at a much lower rate than the population rate. We study the effect of network architecture on sparse synchronization in an inhibitory population of subthreshold Morris-Lecar neurons (which cannot fire spontaneously without noise). Previously, sparse synchronization was found to occur for cases of both global coupling (i.e., regular all-to-all coupling) and random coupling. However, a real neural network is known to be non-regular and non-random. Here, we consider sparse Watts-Strogatz small-world networks which interpolate between a regular lattice and a random graph via rewiring. We start from a regular lattice with only short-range connections and then investigate the emergence of sparse synchronization by increasing the rewiring probability p for the short-range connections. For p = 0, the average synaptic path length between pairs of neurons becomes long; hence, only an unsynchronized population state exists because the global efficiency of information transfer is low. However, as p is increased, long-range connections begin to appear, and global effective communication between distant neurons may be available via shorter synaptic paths. Consequently, as p passes a threshold p th (}~ 0.044), sparsely-synchronized population rhythms emerge. However, with increasing p, longer axon wirings become expensive because of their material and energy costs. At an optimal value p*DE (}~ 0.24) of the rewiring probability, the ratio of the synchrony degree to the wiring cost is found to become maximal. In this way, an optimal sparse synchronization is found to occur at a minimal wiring cost in an economic small-world network through trade-off between synchrony and wiring cost. © 2013 The Korean Physical Society.

Kim S.-Y.,LABASIS Corporation | Lim W.,Daegu National University of Education
Journal of Neuroscience Methods | Year: 2014

Synchronized brain rhythms, associated with diverse cognitive functions, have been observed in electrical recordings of brain activity. Neural synchronization may be well described by using the population-averaged global potential VG in computational neuroscience. The time-averaged fluctuation of VG plays the role of a "thermodynamic" order parameter O used for describing the synchrony-asynchrony transition in neural systems. Population spike synchronization may be well visualized in the raster plot of neural spikes. The degree of neural synchronization seen in the raster plot is well measured in terms of a "statistical-mechanical" spike-based measure Ms introduced by considering the occupation and the pacing patterns of spikes. The global potential VG is also used to give a reference global cycle for the calculation of Ms. Hence, VG becomes an important collective quantity because it is associated with calculation of both O and Ms. However, it is practically difficult to directly get VG in real experiments. To overcome this difficulty, instead of VG, we employ the instantaneous population spike rate (IPSR) which can be obtained in experiments, and develop realistic thermodynamic and statistical-mechanical measures, based on IPSR, to make practical characterization of the neural synchronization in both computational and experimental neuroscience. Particularly, more accurate characterization of weak sparse spike synchronization can be achieved in terms of realistic statistical-mechanical IPSR-based measure, in comparison with the conventional measure based on VG. © 2014 .

Disclosed herein are an apparatus and a method for generating a multi-level test case for testing software from a unified modeling language (UML) sequence diagram (SD) based on a multiple condition control flow graph (MCCFG). The apparatus includes: a UML SD metamodel storing unit storing a UML SD metamodel defined for a model to be converted therein; an MCCFG metamodel storing unit storing an MCCFG metamodel; a model converting unit model-converting the UML SD from which the test case is to be generated according to the UML SD metamodel and the MCCFG metamodel to generate the MCCFG; and a coverage criteria unit converting the MCCFG into a tree structure and then converting the tree structure into test cases according to a selection command.

Kim S.-Y.,Daegu National University of Education | Lim W.,Daegu National University of Education
Neural Networks | Year: 2016

We investigate the effect of network architecture on burst and spike synchronization in a directed scale-free network (SFN) of bursting neurons, evolved via two independent α- and β-processes. The α-process corresponds to a directed version of the Barabási-Albert SFN model with growth and preferential attachment, while for the β-process only preferential attachments between pre-existing nodes are made without addition of new nodes. We first consider the "pure" α-process of symmetric preferential attachment (with the same in- and out-degrees), and study emergence of burst and spike synchronization by varying the coupling strength J and the noise intensity D for a fixed attachment degree. Characterizations of burst and spike synchronization are also made by employing realistic order parameters and statistical-mechanical measures. Next, we choose appropriate values of J and D where only burst synchronization occurs, and investigate the effect of the scale-free connectivity on the burst synchronization by varying (1) the symmetric attachment degree and (2) the asymmetry parameter (representing deviation from the symmetric case) in the α-process, and (3) the occurrence probability of the β-process. In all these three cases, changes in the type and the degree of population synchronization are studied in connection with the network topology such as the degree distribution, the average path length Lp, and the betweenness centralization Bc. It is thus found that just taking into consideration Lp and Bc (affecting global communication between nodes) is not sufficient to understand emergence of population synchronization in SFNs, but in addition to them, the in-degree distribution (affecting individual dynamics) must also be considered to fully understand for the effective population synchronization. © 2016 Elsevier Ltd.

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