Abstract:
Anti-epidermal growth factor receptor (EGFR) therapy has been tried in triple negative breast cancer (TNBC) patients without evaluation of molecular and clinical predictors in several randomized clinical studies. Only fewer than 20% of metastatic TNBCs showed response to anti-EGFR therapy. In order to increase the overall response rate, first step would be to classify TNBC into good or poor responders according to oncogenic mutation profiles. This study provides the molecular characteristics of TNBCs including EGFR gene copy number changes and mutation status of EGFR and KRAS gene in Korean TNBC patients. Mutation analysis for EGFR, KRAS, BRAF and TP53 from a total of 105 TNBC tissue samples was performed by direct sequencing, peptide nucleic acid-mediated PCR clamping method and real-time PCR. Copy number changes of EGFR gene were evaluated using multiplex ligation-dependent probe amplification. Out of all 105 TNBCs, 15.2% (16/105) showed EGFR copy number changes. Among them, increased or decreased EGFR copy number was detected in 13 (5 single copy gain, 2 amplification and 4 high-copy number amplification) and 3 cases (3 hemizygous deletion), respectively. The mutation frequencies of KRAS, EGFR and TP53 gene were 1.9% (G12V and G12D), 1.0% (exon 19 del) and 31.4%, respectively. There was no BRAF V600E mutation found. Future studies are needed to evaluate the clinical outcomes of TNBC patients who undergo anti-EGFR therapy according to the genetic status of EGFR.

Abstract:
Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

Abstract:
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 $\alpha-$ and $\beta-$processes. The $\alpha-$process corresponds to a directed version of the Barab\'{a}si-Albert SFN model with growth and preferential attachment, while for the $\beta-$process only preferential attachments between pre-existing nodes are made without addition of new nodes. We first consider the "pure" $\alpha-$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 the 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 $\alpha-$process, and (3) the occurrence probability of the $\beta-$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 $L_p$, and the betweenness centralization $B_c$. It is thus found that not only $L_p$ and $B_c$ (affecting global communication between nodes) but also the in-degree distribution (affecting individual dynamics) are important network factors for effective population synchronization in SFNs.

Abstract:
For modeling complex synaptic connectivity, we consider the Watts-Strogatz small-world network which interpolates between regular lattice and random network via rewiring, and investigate the effect of small-world connectivity on emergence of noise-induced population synchronization in an inhibitory population of subthreshold bursting Hindmarsh-Rose neurons. Thus, noise-induced slow burst synchronization and fasg spike synchronization are found to appear in a synchronized region of the $J-D$ plane. As the rewiring probability $p$ is decreased from 1 (random network) to 0 (regular lattice), the region of spike synchronization shrinks rapidly in the $J-D$ plane, while the region of the burst synchronization decreases slowly. Population synchronization may be well visualized in the raster plot of neural spikes which can be obtained in experiments. Instantaneous population firing rate, $R(t)$, which is directly obtained from the raster plot of spikes, is a realistic population quantity exhibiting collective behaviors with both the slow bursting and the fast spiking timescales. Through frequency filtering, we separate $R(t)$ into $R_b(t)$ (describing the slow bursting behavior) and $R_s(t)$ (describing the fast intraburst spiking behavior). Then, we develop thermodynamic order parameters and statistical-mechanical measures, based on $R_b (t)$ and $R_s (t)$, for characterization of the burst and spike synchronizations of the bursting neurons and show their usefulness in explicit examples. With increase in $p$, both the degrees of the burst and spike synchronizations are found to increase because more long-range connections appear. However, they become saturated for some maximal values of $p$ because long-range short-cuts which appear up to the maximal values of $p$ play sufficient role to get maximal degrees of the burst and spike synchronizations.

Abstract:
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 $V_G$ in computational neuroscience. The time-averaged fluctuation of $V_G$ plays the role of a "thermodynamic" order parameter $\cal {O}$ 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 $M_s$ introduced by considering the occupation and the pacing patterns of spikes. The global potential $V_G$ is also used to give a reference global cycle for the calculation of $M_s$. Hence, $V_G$ becomes an important collective quantity because it is associated with calculation of both $\cal {O}$ and $M_s$. However, it is practically difficult to directly get $V_G$ in real experiments. To overcome this difficulty, instead of $V_G$, 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 $V_G$.

Abstract:
Fast cortical rhythms with stochastic and intermittent neural discharges have been observed in electric recordings of brain activity. Recently, Brunel et al. developed a framework to describe this kind of fast sparse synchronization in both random and globally-coupled networks of suprathreshold spiking neurons. However, in a real cortical circuit, synaptic connections are known to have complex topology which is neither regular nor random. Hence, in order to extend the works of Brunel et al. to realistic neural networks, we study the effect of network architecture on these fast sparsely synchronized rhythms in an inhibitory population of suprathreshold fast spiking (FS) Izhikevich interneurons. We first employ the conventional Erd\"{o}s-Renyi 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 $D$. Thus, fast sparsely synchronized states of relatively high degree are found to appear for large values of $J$ and $D$. Second, for fixed values of $J$ and $D$ where fast sparse synchronization occurs in the random network, 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 $p^*_c$ $(\simeq 0.12)$, fast sparsely synchronized population rhythms are found to emerge in small-world networks with predominantly local connections and rare long-range connections.

Abstract:
We consider an excitatory population of subthreshold Izhikevich neurons which exhibit noise-induced firings. By varying the coupling strength $J$, we investigate population synchronization between the noise-induced firings which may be used for efficient cognitive processing such as sensory perception, multisensory binding, selective attention, and memory formation. As $J$ is increased, rich types of population synchronization (e.g., spike, burst, and fast spike synchronization) are found to occur. Transitions between population synchronization and incoherence are well described in terms of an order parameter $\cal{O}$. As a final step, the coupling induces oscillator death (quenching of noise-induced spikings) because each neuron is attracted to a noisy equilibrium state. The oscillator death leads to a transition from firing to non-firing states at the population level, which may be well described in terms of the time-averaged population spike rate $\overline{R}$. In addition to the statistical-mechanical analysis using $\cal{O}$ and $\overline{R}$, each population and individual state are also characterized by using the techniques of nonlinear dynamics such as the raster plot of neural spikes, the time series of the membrane potential, and the phase portrait. We note that population synchronization of noise-induced firings may lead to emergence of synchronous brain rhythms in a noisy environment, associated with diverse cognitive functions.

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.

Abstract:
We are interested in characterization of synchronization transitions of bursting neurons in the frequency domain. Instantaneous population firing rate (IPFR) $R(t)$, which is directly obtained from the raster plot of neural spikes, is often used as a realistic collective quantity describing population activities in both the computational and the experimental neuroscience. For the case of spiking neurons, a realistic time-domain order parameter, based on $R(t)$, was introduced in our recent work to characterize the spike synchronization transition. Unlike the case of spiking neurons, the IPFR $R(t)$ of bursting neurons exhibits population behaviors with both the slow bursting and the fast spiking timescales. For our aim, we decompose the IPFR $R(t)$ into the instantaneous population bursting rate $R_b(t)$ (describing the bursting behavior) and the instantaneous population spike rate $R_s(t)$ (describing the spiking behavior) via frequency filtering, and extend the realistic order parameter to the case of bursting neurons. Thus, we develop the frequency-domain bursting and spiking order parameters which are just the bursting and spiking "coherence factors" $\beta_b$ and $\beta_s$ of the bursting and spiking peaks in the power spectral densities of $R_b$ and $R_s$ (i.e., "signal to noise" ratio of the spectral peak height and its relative width). Through calculation of $\beta_b$ and $\beta_s$, we obtain the bursting and spiking thresholds beyond which the burst and spike synchronizations break up, respectively. Consequently, it is shown in explicit examples that the frequency-domain bursting and spiking order parameters may be usefully used for characterization of the bursting and the spiking transitions, respectively.

Abstract:
We consider a directed Barab\'{a}si-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees, and study emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast spiking Izhikevich interneurons. For a study on the fast sparsely synchronized rhythms, we fix $J$ (synaptic inhibition strength) at a sufficiently large value, and investigate the population states by increasing $D$ (noise intensity). For small $D$, full synchronization with the same population-rhythm frequency $f_p$ and mean firing rate (MFR) $f_i$ of individual neurons occurs, while for sufficiently large $D$ partial synchronization with $f_p > {\langle f_i \rangle}$ ($\langle f_i \rangle$: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; particularly, the case of $f_p > 4 {\langle f_i \rangle}$ is referred to as sparse synchronization. Only for the partial and sparse synchronization, MFRs and contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous random graphs and small-world networks. Finally, we investigate the effect of network architecture on sparse synchronization in the following three cases: (1) variation in the degree of symmetric attachment (2) asymmetric preferential attachment of new nodes with different in- and out-degrees (3) preferential attachment between pre-existing nodes (without addition of new nodes). In these three cases, both relation between network topology and sparse synchronization and contributions of individual dynamics to the sparse synchronization are discussed.