http://www.hindawi.com The latest articles from Hindawi Publishing Corporation © 2013 , Hindawi Publishing Corporation . All rights reserved. Sun, 21 Apr 2013 08:19:28 +0000 http://www.hindawi.com/journals/cin/2013/152073/ According to the data-frame theory, sensemaking is a macrocognitive process in which people try to make sense of or explain their observations by processing a number of explanatory structures called frames until the observations and frames become congruent. During the sensemaking process, the parietal cortex has been implicated in various cognitive tasks for the functions related to spatial and temporal information processing, mathematical thinking, and spatial attention. In particular, the parietal cortex plays important roles by extracting multiple representations of magnitudes at the early stages of perceptual analysis. By a series of neural network simulations, we demonstrate that the dissociation of different types of spatial information can start early with a rather similar structure (i.e., sensitivity on a common metric), but accurate representations require specific goal-directed top-down controls due to the interference in selective attention. Our results suggest that the roles of the parietal cortex rely on the hierarchical organization of multiple spatial representations and their interactions. The dissociation and interference between different types of spatial information are essentially the result of the competition at different levels of abstraction. Yanlong Sun and Hongbin Wang Copyright © 2013 Yanlong Sun and Hongbin Wang. All rights reserved. Tue, 16 Apr 2013 09:12:32 +0000 http://www.hindawi.com/journals/cin/2013/717853/ The motor clinical hallmarks of Parkinson's disease (PD) are usually quantified by physicians using validated clinimetric scales such as the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, clinical ratings are prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a simple, inexpensive, and objective rating method. As a first step towards this goal, a triaxial accelerometer-based system was used in a sample of 36 PD patients and 10 age-matched controls as they performed the MDS-UPDRS finger tapping (FT) task. First, raw signals were epoched to isolate the successive single FT movements. Next, eighteen FT task movement features were extracted, depicting MDS-UPDRS features and accelerometer specific features. An ordinal logistic regression model and a greedy backward algorithm were used to identify the most relevant features in the prediction of MDS-UPDRS FT scores, given by 3 specialists in movement disorders (SMDs). The Goodman-Kruskal Gamma index obtained (0.961), depicting the predictive performance of the model, is similar to those obtained between the individual scores given by the SMD (0.870 to 0.970). The automatic prediction of MDS-UPDRS scores using the proposed system may be valuable in clinical trials designed to evaluate and modify motor disability in PD patients. Julien Stamatakis, Jérome Ambroise, Julien Crémers, Hoda Sharei, Valérie Delvaux, Benoit Macq, and Gaëtan Garraux Copyright © 2013 Julien Stamatakis et al. All rights reserved. Sun, 17 Mar 2013 08:08:13 +0000 http://www.hindawi.com/journals/cin/2013/453812/ Aiming at the phenomenon of slow convergence rate and low accuracy of bat algorithm, a novel bat algorithm based on differential operator and Lévy flights trajectory is proposed. In this paper, a differential operator is introduced to accelerate the convergence speed of proposed algorithm, which is similar to mutation strategy “DE/best/2” in differential algorithm. Lévy flights trajectory can ensure the diversity of the population against premature convergence and make the algorithm effectively jump out of local minima. 14 typical benchmark functions and an instance of nonlinear equations are tested; the simulation results not only show that the proposed algorithm is feasible and effective, but also demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space. Jian Xie, Yongquan Zhou, and Huan Chen Copyright © 2013 Jian Xie et al. All rights reserved. Mon, 18 Feb 2013 15:56:44 +0000 http://www.hindawi.com/journals/cin/2013/968438/ Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones. Xigao Shao, Kun Wu, and Bifeng Liao Copyright © 2013 Xigao Shao et al. All rights reserved. Mon, 04 Feb 2013 15:04:18 +0000 http://www.hindawi.com/journals/cin/2013/949816/ Structural rearrangement of the dentate gyrus has been described as the underlying cause of many types of epilepsies, particularly temporal lobe epilepsy. It is said to occur when aberrant connections are established in the damaged hippocampus, as described in human epilepsy and experimental models. Computer modelling of the dentate gyrus circuitry and the corresponding structural changes has been used to understand how abnormal mossy fibre sprouting can subserve seizure generation observed in experimental models when epileptogenesis is induced by status epilepticus. The model follows the McCulloch-Pitts formalism including the representation of the nonsynaptic mechanisms. The neuronal network comprised granule cells, mossy cells, and interneurons. The compensation theory and the Hebbian and anti-Hebbian rules were used to describe the structural rearrangement including the effects of the nonsynaptic mechanisms on the neuronal activity. The simulations were based on neuroanatomic data and on the connectivity pattern between the cells represented. The results suggest that there is a joint action of the compensation theory and Hebbian rules during the inflammatory process that accompanies the status epilepticus. The structural rearrangement simulated for the dentate gyrus circuitry promotes speculation about the formation of the abnormal mossy fiber sprouting and its role in epileptic seizures. Keite Lira de Almeida França, Antônio-Carlos Guimarães de Almeida, Antonio Fernando Catelli Infantosi, Mario Antônio Duarte, Gilcélio Amaral da Silveira, Fulvio Alexandre Scorza, Ricardo Mario Arida, Esper Abrão Cavalheiro, and Antônio Márcio Rodrigues Copyright © 2013 Keite Lira de Almeida França et al. All rights reserved. Sun, 30 Dec 2012 10:47:03 +0000 http://www.hindawi.com/journals/cin/2012/968272/ Hebbian plasticity precisely describes how synapses increase their synaptic strengths according to the correlated activities between two neurons; however, it fails to explain how these activities dilute the strength of the same synapses. Recent literature has proposed spike-timing-dependent plasticity and short-term plasticity on multiple dynamic stochastic synapses that can control synaptic excitation and remove many user-defined constraints. Under this hypothesis, a network model was implemented giving more computational power to receptors, and the behavior at a synapse was defined by the collective dynamic activities of stochastic receptors. An experiment was conducted to analyze can spike-timing-dependent plasticity interplay with short-term plasticity to balance the excitation of the Hebbian neurons without weight constraints? If so what underline mechanisms help neurons to maintain such excitation in computational environment? According to our results both plasticity mechanisms work together to balance the excitation of the neural network as our neurons stabilized its weights for Poisson inputs with mean firing rates from 10 Hz to 40 Hz. The behavior generated by the two neurons was similar to the behavior discussed under synaptic redistribution, so that synaptic weights were stabilized while there was a continuous increase of presynaptic probability of release and higher turnover rate of postsynaptic receptors. Subha Fernando and Koichi Yamada Copyright © 2012 Subha Fernando and Koichi Yamada. All rights reserved. Sat, 29 Dec 2012 15:00:48 +0000 http://www.hindawi.com/journals/cin/2012/601296/ Linear multiple kernel learning model has been used for predicting financial time series. However, -norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt -norm multiple kernel support vector regression () as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than -norm multiple support vector regression model. Xigao Shao, Kun Wu, and Bifeng Liao Copyright © 2012 Xigao Shao et al. All rights reserved. Mon, 10 Dec 2012 18:53:19 +0000 http://www.hindawi.com/journals/cin/2012/421032/ In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach. Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, and Mahantapas Kundu Copyright © 2012 Arindam Kar et al. All rights reserved. Thu, 06 Dec 2012 15:56:37 +0000 http://www.hindawi.com/journals/cin/2012/967305/ Utilizing changes in steady-state visual evoked potentials (SSVEPs) is an established approach to operate a brain-computer interface (BCI). The present study elucidates to what extent development-specific changes in the background EEG influence the ability to proper handle a stimulus-driven BCI. Therefore we investigated the effects of a wide range of photic driving on children between six and ten years in comparison to an adult control group. The results show differences in the driving profiles apparently in close communication with the specific type of intermittent stimulation. The factor age gains influence with decreasing stimulation frequency, whereby the superior performance of the adults seems to be determined to a great extent by elaborated driving responses at 10 and 11 Hz, matching the dominant resonance frequency of the respective background EEG. This functional interplay was only partially obtained in higher frequency ranges and absent in the induced driving between 30 and 40 Hz, indicating distinctions in the operating principles and developmental changes of the underlying neuronal oscillators. Jan Ehlers, Diana Valbuena, Anja Stiller, and Axel Gräser Copyright © 2012 Jan Ehlers et al. All rights reserved. Thu, 06 Dec 2012 10:46:52 +0000 http://www.hindawi.com/journals/cin/2012/173910/ Most models of central pattern generators (CPGs) involve two distinct nuclei mutually inhibiting one another via synapses. Here, we present a single-nucleus model of biologically realistic Hodgkin-Huxley neurons with random gap junction coupling. Despite no explicit division of neurons into two groups, we observe a spontaneous division of neurons into two distinct firing groups. In addition, we also demonstrate this phenomenon in a simplified version of the model, highlighting the importance of afterhyperpolarization currents () to CPGs utilizing gap junction coupling. The properties of these CPGs also appear sensitive to gap junction conductance, probability of gap junction coupling between cells, topology of gap junction coupling, and, to a lesser extent, input current into our simulated nucleus. Kyle G. Horn, Heraldo Memelli, and Irene C. Solomon Copyright © 2012 Kyle G. Horn et al. All rights reserved. Tue, 04 Dec 2012 14:35:21 +0000 http://www.hindawi.com/journals/cin/2012/575129/ In specific regions of the central nervous system (CNS), gap junctions have been shown to participate in neuronal synchrony. Amongst the CNS regions identified, some populations of brainstem motoneurons are known to be coupled by gap junctions. The application of various gap junction blockers to these motoneuron populations, however, has led to mixed results regarding their synchronous firing behavior, with some studies reporting a decrease in synchrony while others surprisingly find an increase in synchrony. To address this discrepancy, we employ a neuronal network model of Hodgkin-Huxley-style motoneurons connected by gap junctions. Using this model, we implement a series of simulations and rigorously analyze their outcome, including the calculation of a measure of neuronal synchrony. Our simulations demonstrate that under specific conditions, uncoupling of gap junctions is capable of producing either a decrease or an increase in neuronal synchrony. Subsequently, these simulations provide mechanistic insight into these different outcomes. Heraldo Memelli, Kyle G. Horn, Larry D. Wittie, and Irene C. Solomon Copyright © 2012 Heraldo Memelli et al. All rights reserved. Mon, 03 Dec 2012 13:49:54 +0000 http://www.hindawi.com/journals/cin/2012/160356/ Yen-Wei Chen, Ikuko Nishikawa, Shinichi Tamura, Bao-Liang Lu, and Huiyan Jiang Copyright © 2012 Yen-Wei Chen et al. All rights reserved. Mon, 03 Dec 2012 13:19:37 +0000 http://www.hindawi.com/journals/cin/2012/197262/ We propose a model to generate a group of artificial lives capable of coping with various environments which is equivalent to a set of requested task, and likely to show that the plays or hobbies are necessary for the group of individuals to maintain the coping capability with various changes of the environment as a whole. This may be an another side of saying that the wide variety of the abilities in the group is necessary, and if the variety in a species decreased, its species will be extinguished. Thus, we show some simulation results, for example, in the world where more variety of abilities are requested in the plays, performance of the whole world becomes stable and improved in spite of being calculated only from job tasks, and can avoid the risk of extinction of the species. This is the good effect of the play. Shinichi Tamura, Shoji Inabayashi, Waichi Hayakawa, Takahiro Yokouchi, Hiroshi Mitsumoto, and Hisashi Taketani Copyright © 2012 Shinichi Tamura et al. All rights reserved. Tue, 20 Nov 2012 15:26:11 +0000 http://www.hindawi.com/journals/cin/2012/817485/ Although, there are considerable works on the neural mechanisms of reward-based learning and decision making, and most of them mention that addiction can be explained by malfunctioning in these cognitive processes, there are very few computational models. This paper focuses on nicotine addiction, and a computational model for nicotine addiction is proposed based on the neurophysiological basis of addiction. The model compromises different levels ranging from molecular basis to systems level, and it demonstrates three different possible behavioral patterns which are addict, nonaddict, and indecisive. The dynamical behavior of the proposed model is investigated with tools used in analyzing nonlinear dynamical systems, and the relation between the behavioral patterns and the dynamics of the system is discussed. Selin Metin and N. Serap Sengor Copyright © 2012 Selin Metin and N. Serap Sengor. All rights reserved. Wed, 14 Nov 2012 15:32:12 +0000 http://www.hindawi.com/journals/cin/2012/209590/ We present a formal methodology for identifying a channel in a system consisting of a communication channel in cascade with an asynchronous sampler. The channel is modeled as a multidimensional filter, while models of asynchronous samplers are taken from neuroscience and communications and include integrate-and-fire neurons, asynchronous sigma/delta modulators and general oscillators in cascade with zero-crossing detectors. We devise channel identification algorithms that recover a projection of the filter(s) onto a space of input signals loss-free for both scalar and vector-valued test signals. The test signals are modeled as elements of a reproducing kernel Hilbert space (RKHS) with a Dirichlet kernel. Under appropriate limiting conditions on the bandwidth and the order of the test signal space, the filter projection converges to the impulse response of the filter. We show that our results hold for a wide class of RKHSs, including the space of finite-energy bandlimited signals. We also extend our channel identification results to noisy circuits. Aurel A. Lazar and Yevgeniy B. Slutskiy Copyright © 2012 Aurel A. Lazar and Yevgeniy B. Slutskiy. All rights reserved. Wed, 14 Nov 2012 13:33:32 +0000 http://www.hindawi.com/journals/cin/2012/582453/ The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal. Vijay G. S., Kumar H. S., Srinivasa Pai P., Sriram N. S., and Raj B. K. N. Rao Copyright © 2012 Vijay G. S. et al. All rights reserved. Tue, 30 Oct 2012 11:34:31 +0000 http://www.hindawi.com/journals/cin/2012/946589/ In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism. Si-Yao Fu, Guo-Sheng Yang, and Xin-Kai Kuai Copyright © 2012 Si-Yao Fu et al. All rights reserved. Tue, 30 Oct 2012 08:11:09 +0000 http://www.hindawi.com/journals/cin/2012/404806/ A biobjective slack-diversifying nonlinear fluctuation-smoothing rule (biSDNFS) is proposed in the present work to improve the scheduling performance of a wafer fabrication factory. This rule was derived from a one-factor bi-objective nonlinear fluctuation-smoothing rule (1f-biNFS) by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several previous studies. The efficacy of the biSDNFS was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future. Toly Chen and Yu Cheng Wang Copyright © 2012 Toly Chen and Yu Cheng Wang. All rights reserved. Thu, 11 Oct 2012 17:34:55 +0000 http://www.hindawi.com/journals/cin/2012/412512/ This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities. E. W. Lang, A. M. Tomé, I. R. Keck, J. M. Górriz-Sáez, and C. G. Puntonet Copyright © 2012 E. W. Lang et al. All rights reserved. Sun, 30 Sep 2012 09:00:07 +0000 http://www.hindawi.com/journals/cin/2012/876357/ Large-scale models of neuronal structures are needed to explore emergent properties of mammalian brains. Because these models have trillions of synapses, a major problem in their creation is synapse placement. Here we present a novel method for exploiting consistent fiber orientation in a neural tissue to perform a highly efficient modified plane-sweep algorithm, which identifies all regions of 3D overlaps between dendritic and axonal projection fields. The first step in placing synapses in physiological models is neurite-overlap detection, at large scales a computationally intensive task. We have developed an efficient “Staggered Walk” algorithm that can find all 3D overlaps of neurites where trillions of synapses connect billions of neurons. Jack Zito, Heraldo Memelli, Kyle G. Horn, Irene C. Solomon, and Larry D. Wittie Copyright © 2012 Jack Zito et al. All rights reserved. Thu, 27 Sep 2012 09:54:01 +0000 http://www.hindawi.com/journals/cin/2012/850259/ Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. S. Ganapathy, P. Yogesh, and A. Kannan Copyright © 2012 S. Ganapathy et al. All rights reserved. Tue, 25 Sep 2012 09:15:11 +0000 http://www.hindawi.com/journals/cin/2012/359529/ The cerebellum input stage has been known to perform combinatorial operations on input signals. In this paper, two types of mathematical models were used to reproduce the role of feed-forward inhibition and computation in the granular layer microcircuitry to investigate spike train processing. A simple spiking model and a biophysically-detailed model of the network were used to study signal recoding in the granular layer and to test observations like center-surround organization and time-window hypothesis in addition to effects of induced plasticity. Simulations suggest that simple neuron models may be used to abstract timing phenomenon in large networks, however detailed models were needed to reconstruct population coding via evoked local field potentials (LFP) and for simulating changes in synaptic plasticity. Our results also indicated that spatio-temporal code of the granular network is mainly controlled by the feed-forward inhibition from the Golgi cell synapses. Spike amplitude and total number of spikes were modulated by LTP and LTD. Reconstructing granular layer evoked-LFP suggests that granular layer propagates the nonlinearities of individual neurons. Simulations indicate that granular layer network operates a robust population code for a wide range of intervals, controlled by the Golgi cell inhibition and is regulated by the post-synaptic excitability. Chaitanya Medini, Bipin Nair, Egidio D'Angelo, Giovanni Naldi, and Shyam Diwakar Copyright © 2012 Chaitanya Medini et al. All rights reserved. Wed, 19 Sep 2012 17:04:14 +0000 http://www.hindawi.com/journals/cin/2012/541890/ An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality. Huiyan Jiang, Zhiyuan Ma, Yang Hu, Benqiang Yang, and Libo Zhang Copyright © 2012 Huiyan Jiang et al. All rights reserved. Thu, 06 Sep 2012 13:43:23 +0000 http://www.hindawi.com/journals/cin/2012/135204/ Whereas a variety of different feature-point matching approaches have been reported in computer vision, few feature-point matching approaches employed in images from nonrigid, nonuniform human tissues have been reported. The present work is concerned with interior deformation field measurement of complex human tissues from three-dimensional magnetic resonance (MR) volumetric images. To improve the reliability of matching results, this paper proposes composite match index (CMI) as the foundation of multimethod fusion methods to increase the reliability of these various methods. Thereinto, we discuss the definition, components, and weight determination of CMI. To test the validity of the proposed approach, it is applied to actual MR volumetric images obtained from a volunteer’s calf. The main result is consistent with the actual condition. Penglin Zhang, Xubing Zhang, and Jiangping Chen Copyright © 2012 Penglin Zhang et al. All rights reserved. Wed, 22 Aug 2012 15:50:16 +0000 http://www.hindawi.com/journals/cin/2012/561406/ In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms—subpopulation and crossover. Chen-Lun Lin, Aya Mimori, and Yen-Wei Chen Copyright © 2012 Chen-Lun Lin et al. All rights reserved. Thu, 16 Aug 2012 16:24:02 +0000 http://www.hindawi.com/journals/cin/2012/795291/ Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies. Hidetoshi Ikeno, Tomoki Kazawa, Shigehiro Namiki, Daisuke Miyamoto, Yohei Sato, Stephan Shuichi Haupt, Ikuko Nishikawa, and Ryohei Kanzaki Copyright © 2012 Hidetoshi Ikeno et al. All rights reserved. Tue, 14 Aug 2012 15:13:13 +0000 http://www.hindawi.com/journals/cin/2012/704673/ Since the pioneering work of Fatt and Katz at the neuromuscular junction (NMJ), spontaneous synaptic release (minis), that is, the quantal discharge of neurotransmitter molecules which occurs in the absence of action potentials, has been unanimously considered a memoryless random Poisson process where each quantum is discharged with a very low release probability independently from other quanta. When this model was thoroughly tested, for both population and single-synapse recordings, some clear evidence in favor of a more complex scenario emerged. This included short- and long-range correlation in mini occurrences and divergence from mono-exponential inter-mini-interval distributions, both unexpected for a homogeneous Poisson process, that is, with a rate parameter that does not change over time. Since we are interested in accurately quantifying the fractal exponent 𝛼 of the spontaneous neurotransmitter release process at central synaptic sites, this work was aimed at evaluating the sensitivity of the most established methods available, such as the periodogram, the Allan, factor and the detrended fluctuation analysis. For this analysis we matched spontaneous release series recorded at individual hippocampal synapses (single-synapse recordings) to generate large collections of simulated quantal events by means of a custom algorithm combining Monte Carlo sampling methods with spectral methods for the generation of 1/𝑓 series. These tests were performed by varying separately: (i) the fractal exponent 𝛼 of the rate driving the release process; (ii) the distribution of intervals between successive releases, mimicking those encountered in single-synapse experimental series; (iii) the number of samples. The aims were to provide a methodological framework for approaching the fractal analysis of single-unit spontaneous release series recorded at central synapses. Jacopo Lamanna, Antonio Malgaroli, Sergio Cerutti, and Maria G. Signorini Copyright © 2012 Jacopo Lamanna et al. All rights reserved. Tue, 14 Aug 2012 12:05:53 +0000 http://www.hindawi.com/journals/cin/2012/261089/ Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each of size 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database. Debotosh Bhattacharjee, Ayan Seal, Suranjan Ganguly, Mita Nasipuri, and Dipak Kumar Basu Copyright © 2012 Debotosh Bhattacharjee et al. All rights reserved. Thu, 02 Aug 2012 07:38:17 +0000 http://www.hindawi.com/journals/cin/2012/918030/ The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs). Among these, the directional selective cells (DSRGC) are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs) framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion) and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system. Aurel Vasile Martiniuc and Alois Knoll Copyright © 2012 Aurel Vasile Martiniuc and Alois Knoll. All rights reserved. Wed, 18 Jul 2012 11:18:50 +0000 http://www.hindawi.com/journals/cin/2012/862579/ In circuit theory, it is well known that a linear feedback shift register (LFSR) circuit generates pseudorandom bit sequences (PRBS), including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3). These results show the possibility of assembling LFSR circuits or its equivalent ones in a neuronal network. However, since the M3 pattern was composed of only four spike intervals, the possibility of an accidental detection was not zero. Then, we detected M-sequences from random spike sequences which were not generated from an LFSR circuit and compare the result with the number of M-sequences from the originally observed raster data. As a result, a significant difference was confirmed: a greater number of “0–1” reversed the 3-stage M-sequences occurred than would have accidentally be detected. This result suggests that some LFSR equivalent circuits are assembled in neuronal networks. Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, Tomomitsu Miyoshi, Hajime Sawai, and Shinichi Tamura Copyright © 2012 Yoshi Nishitani et al. All rights reserved.