Daegu National University of Education

www.dnue.ac.kr
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.


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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.,Daegu National University of Education | Lim W.,Daegu National University of Education
Physica A: Statistical Mechanics and its Applications | Year: 2015

Fast cortical rhythms with stochastic and intermittent neural discharges have been observed in electric recordings of brain activity. For these fast sparsely synchronized oscillations, individual neurons fire spikings irregularly and sparsely as Geiger counters, in contrast to fully synchronized oscillations where individual neurons exhibit regular firings like clocks. We study the effect of network architecture on these fast sparsely synchronized rhythms in an inhibitory population of suprathreshold fast spiking (FS) Izhikevich interneurons (which fire spontaneously without noise). We first employ the conventional Erdös-Rényi random graph of suprathreshold FS Izhikevich interneurons for modeling the complex connectivity in neural systems, and study emergence of the population synchronized states by varying both the synaptic inhibition strength J and the noise intensity D. Fast sparsely synchronized states of relatively high degree are found to appear for large values of J and D. However, in a real cortical circuit, synaptic connections are known to have complex topology which is neither regular nor random. Hence, for fixed values of J and D we consider the Watts-Strogatz small-world network of suprathreshold FS Izhikevich interneurons which interpolates between regular lattice and random graph via rewiring, and investigate the effect of small-world synaptic connectivity on emergence of fast sparsely synchronized rhythms by varying the rewiring probability p from short-range to long-range connection. When passing a small critical value pc∗, fast sparsely synchronized population rhythms are found to emerge in small-world networks with predominantly local connections and rare long-range connections. This transition to fast sparse synchronization is well characterized in terms of a realistic "thermodynamic" order parameter. For further understanding of this transition, we also investigate the effect of long-range connections on dynamical correlations between neuronal pairs, and find that for p>pc∗, global synchronization appears in the whole population because the spatial correlation length covers the whole system thanks to sufficient number of long-range connections. The degree of fast sparse synchronization for p>pc∗ is also measured in terms of a realistic "statistical-mechanical" spiking measure. As p is increased from pc∗, the degree of population synchrony becomes higher, while the axon "wire length" of the network also increases. At a dynamical-efficiency optimal value pE∗, there is a trade-off between the population synchronization and the wiring economy, and hence an optimal fast sparsely-synchronized rhythm is found to occur at a minimal wiring cost in an economic small-world network. © 2014 Elsevier B.V.


Kim S.-Y.,Daegu National University of Education | Lim W.,Daegu National University of Education
Physica A: Statistical Mechanics and its Applications | Year: 2015

Abstract We are interested in characterization of population synchronization of bursting neurons which exhibit both the slow bursting and the fast spiking timescales, in contrast to spiking neurons. Population synchronization may be well visualized in the raster plot of neural spikes which can be obtained in experiments. The instantaneous population firing rate (IPFR) R(t), which may be directly obtained from the raster plot of spikes, is often used as a realistic collective quantity describing population behaviors in both the computational and the experimental neuroscience. For the case of spiking neurons, realistic thermodynamic order parameter and statistical-mechanical spiking measure, based on R(t), were introduced in our recent work to make practical characterization of spike synchronization. Here, we separate the slow bursting and the fast spiking timescales via frequency filtering, and extend the thermodynamic order parameter and the statistical-mechanical measure to the case of bursting neurons. Consequently, it is shown in explicit examples that both the order parameters and the statistical-mechanical measures may be effectively used to characterize the burst and spike synchronizations of bursting neurons. © 2015 Published by Elsevier B.V.


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.,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.


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 .


Kim S.-Y.,Daegu National University of Education | Lim W.,Daegu National University of Education
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2015

We consider a clustered network with small-world subnetworks of inhibitory fast spiking interneurons and investigate the effect of intermodular connection on the emergence of fast sparsely synchronized rhythms by varying both the intermodular coupling strength Jinter and the average number of intermodular links per interneuron Msyn(inter). In contrast to the case of nonclustered networks, two kinds of sparsely synchronized states such as modular and global synchronization are found. For the case of modular sparse synchronization, the population behavior reveals the modular structure, because the intramodular dynamics of subnetworks make some mismatching. On the other hand, in the case of global sparse synchronization, the population behavior is globally identical, independently of the cluster structure, because the intramodular dynamics of subnetworks make perfect matching. We introduce a realistic cross-correlation modularity measure, representing the matching degree between the instantaneous subpopulation spike rates of the subnetworks, and examine whether the sparse synchronization is global or modular. Depending on its magnitude, the intermodular coupling strength Jinter seems to play "dual" roles for the pacing between spikes in each subnetwork. For large Jinter, due to strong inhibition it plays a destructive role to "spoil" the pacing between spikes, while for small Jinter it plays a constructive role to "favor" the pacing between spikes. Through competition between the constructive and the destructive roles of Jinter, there exists an intermediate optimal Jinter at which the pacing degree between spikes becomes maximal. In contrast, the average number of intermodular links per interneuron Msyn(inter) seems to play a role just to favor the pacing between spikes. With increasing Msyn(inter), the pacing degree between spikes increases monotonically thanks to the increase in the degree of effectiveness of global communication between spikes. Furthermore, we employ the realistic sub- and whole-population order parameters, based on the instantaneous sub- and whole-population spike rates, to determine the threshold values for the synchronization-unsynchronization transition in the sub- and whole populations, and the degrees of global and modular sparse synchronization are also measured in terms of the realistic sub- and whole-population statistical-mechanical spiking measures defined by considering both the occupation and the pacing degrees of spikes. It is expected that our results could have implications for the role of the brain plasticity in some functional behaviors associated with population synchronization. © 2015 American Physical Society.


Kwon S.,Daegu National University of Education
New Physics: Sae Mulli | Year: 2015

The purpose of this study is to examine the profiles of understanding the concept of mass by student teachers. The elementary science curriculum does not require that the concept of mass be taught, but rather requires that of weight to be taught. However, how to teach the concept of weight without a valid and credible understanding of mass is a question that many elementary student teachers have. Only when teaching related courses can elementary pre-service teachers could be expected to learn the scientific units of mass and weight. The subjects for this study were 79 student teachers with a science major selected from a University of Education in a local city. Questionnaires on the concept of mass were developed so as to match those for the concept of weight in previous research. After the pre-service teachers' choices and written responses had been analyzed, we are able to get some profiles for the concept of mass and to discuss the problems associated with teaching the concept of mass combined with units of measuring weight in the elementary textbooks. We will suggest ways to develop new effective tips for teaching the concept of mass to elementary students and pre-service teachers.


Kim S.-Y.,Daegu National University of Education | Lim W.,Daegu National University of Education
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2015

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>(fi) ((fi): ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4(fi) is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D∗ (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D


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.

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