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Journal Cover Journal of Town and City Management
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   ISSN (Print) 1756-9538 - ISSN (Online) 1756-9591
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  • Neurodynamics of up and down transitions in a single neuron
    • Abstract: Recent experimental studies have revealed that up and down transitions exist in membrane potential of neurons. This paper focuses on the neurodynamical research of these transitions in a single neuron since it is the basic to study the transitions in the neural network for further work. The results show there exists two stable levels in the neuron called up and down states. And transitions between these two states are bidirectional or unidirectional with the values of parameters changing. We also study the periodic spontaneous activity of the transitions between up and down states without any inputting stimulus which coheres with the experimental results.
      PubDate: 2014-12-01
  • Event related desynchronization: use as a neurophysiologic marker is
    • Abstract: The present research aims to show that the occurrence of alpha blocking or event-related desynchronization (ERD) strongly depends on the amplitude and also on the phase angle of alpha activity at the stimulus onset. Simple visual stimulation was presented to 17 healthy subjects during EEG recording. An O2 electrode was used for analysis with a 32 channel EEG sampling system. We used a segmentation of raw data in order to obtain the evoked potential. Prestimulus and poststimulus activities were filtered in the alpha (8–13 Hz) frequency band. Later, four different events (blocked, time-locked, phase-locked, and eliminated) were separately averaged. Phase-locked sweeps were determined by application of inter-trial coherence analysis. The evaluation of the data shows that “time-locked and phase-locked sweeps” were the dominating pattern and not “the blocked pattern”, which occurred only when the prestimulus alpha was high. In the analyses of EEG-EP sweeps, only 22 % of epochs showed (ERD). The ANOVA revealed significant differences between four different alpha responses (F(3,48) = 11.175; p < 0.001). Furthermore, alpha oscillations in time-locked responses were significantly higher than blocked (p < 0.0001). The analyses clearly demonstrate that important precaution is needed when using the ERD as a cognitive or pathological marker.
      PubDate: 2014-12-01
  • Different patterns of puberty effect in neural oscillation to negative
           stimuli: sex differences
    • Abstract: The present study investigated the impact of puberty on sex differences in neural sensitivity to negative stimuli. Event-related oscillation technique was used. Because girls are more vulnerable to affective disturbances than boys during adolescence, it was hypothesized that puberty exerts different influences on neural sensitivity to negative stimuli in boys and girls. EEGs were recorded for highly negative (HN), mildly negative (MN) and neutral pictures, when boys and girls distinct in pubertal status performed a non-emotional distracting task. No emotion effect and its interaction with sex and puberty were observed in response latencies. However, puberty influenced the gamma-band oscillation effect for negative stimuli differently for boys and girls: Pre-pubertal boys showed a significant emotion effect for HN stimuli, whose size was decreased in pubertal boys. By contrast, there was a significant emotion effect for HN stimuli in pubertal girls but not in pre-pubertal girls. On the other hand, the size of the emotion effect for HN stimuli was similar for pre-pubertal boys and girls; while this effect was significantly more pronounced in pubertal girls compared to pubertal boys. Additionally, the size of the emotion effect in gamma oscillations decreased as a function of pubertal development during both HN and MN stimulation in boys. For girls, the emotion effect in gamma oscillations increased with pubertal development during HN stimulation. Thus, puberty is associated with reduced neural sensitivity in boys but increased sensitivity in girls, in reaction to negative stimuli. The implications of these results for the psychopathology during adolescence were discussed.
      PubDate: 2014-12-01
  • MMN responses during implicit processing of changes in emotional prosody:
           an ERP study using Chinese pseudo-syllables
    • Abstract: In this study, we tested the underlying mechanisms of early emotional prosody perception, especially examined whether change detection in oddball paradigm was caused by emotional category and physical properties. Using implicit oddball paradigms, the current study manipulated the cues for detecting deviant stimuli from standards in three conditions: the simultaneous changes in emotional category and physical properties (EP condition), change in emotional category alone (E condition), and change in physical properties alone (P condition). ERP results revealed that physical property change increased brain responses to deviant stimuli in the EP than in the E condition at early stage 90–160 ms, suggesting that physical property change of emotional sounds can also be detected at the early stage. At the later stage 160–260 ms, the simultaneous and respective changes in emotional category and physical properties were reliably detected, and the sum of the brain responses to the corresponding changes in E and P conditions was equal to the brain responses to the simultaneous changes in EP condition. Source analysis further revealed that stimuli-driven regions (inferior parietal lobule), temporal and frontal cortices were activated at early stage, while only frontal cortices for higher cognitive processing were activated at later stage. These findings suggest that emotional prosody changes in physical properties and emotion category are perceived as domain-general change information in emotional prosody perception.
      PubDate: 2014-12-01
  • Spatially constrained adaptive rewiring in cortical networks creates
           spatially modular small world architectures
    • Abstract: A modular small-world topology in functional and anatomical networks of the cortex is eminently suitable as an information processing architecture. This structure was shown in model studies to arise adaptively; it emerges through rewiring of network connections according to patterns of synchrony in ongoing oscillatory neural activity. However, in order to improve the applicability of such models to the cortex, spatial characteristics of cortical connectivity need to be respected, which were previously neglected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias. The spatially constrained adaptive rewiring principle is able to steer the evolving network topology to small world status, even more consistently so than without spatial constraints. Locally biased adaptive rewiring results in a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections. The principle of locally biased adaptive rewiring, thus, may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.
      PubDate: 2014-12-01
  • Robust synchronization of coupled neural oscillators using the
           derivative-free nonlinear Kalman Filter
    • Abstract: A synchronizing control scheme for coupled neural oscillators of the FitzHugh–Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane’s voltage variations for the two neurons. To compensate for disturbances that affect the neurons’ model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons’ model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.
      PubDate: 2014-12-01
  • How the speed of working memory updating influences the on-line thematic
           processing of simple sentences in Mandarin Chinese
    • Abstract: This ERP study used electrophysiological technique to examine how individual differences in the speed of working memory updating influence the use of syntactic and semantic information during on-line sentence argument interpretation, and the time course of that working memory updating effect. The basic structure of the experimental sentences was “Noun + Verb + adverb + ‘le’ + a two-character word”, with the Noun being the sentence initial argument. This initial argument is animate or inanimate and the following verb disambiguates it as an agent or patient. The results at the initial argument revealed that, the quick-updating group elicited a larger positivity over the frontal cortex (within 500–800 ms post-noun onset) as compared with the slow-updating group. At the following disambiguating verb, the slow-updating group only showed a word order effect, indicating that the patient-first condition elicited a larger P600 (within 500–1,000 ms post-verb onset) than the agent-first one; for the quick-updating group, at the early stage of processing, the patient-first sentences elicited a larger N400 (within 300–500 ms post-verb onset) than the agent-first ones only when the initial argument was inanimate; however, at the late stage, the patient-first sentences elicited an enhanced P600 (within 800–1,000 ms post-verb onset) only when the initial argument was animate. These results suggested that the speed of working memory updating not only influences the maintenance of sentence argument when the contents of working memory change but also influences the efficiency of integrating that argument with the verb at a late time point. When integrating the argument with the disambiguating verb, individuals with quick-updating ability can combine multiple sources of information (both noun animacy and word order), and conduct rapid and fine-grained two-stage processing; individuals with slow-updating ability, however, only rely on one dominant source of information types (word order), and conducted slow and course-grained processing.
      PubDate: 2014-12-01
  • Noise-induced burst and spike synchronizations in an inhibitory
           small-world network of subthreshold bursting neurons
    • Abstract: We are interested in noise-induced firings of subthreshold neurons which may be used for encoding environmental stimuli. Noise-induced population synchronization was previously studied only for the case of global coupling, unlike the case of subthreshold spiking neurons. Hence, we investigate the effect of complex network architecture on noise-induced synchronization in an inhibitory population of subthreshold bursting Hindmarsh–Rose neurons. 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. Thus, noise-induced burst synchronization (synchrony on the slow bursting time scale) and spike synchronization (synchrony on the fast spike time scale) are found to appear in a synchronized region of the \(J\) – \(D\) plane ( \(J\) : synaptic inhibition strength and \(D\) : noise intensity). 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. We separate the slow bursting and the fast spiking time scales via frequency filtering, and characterize the noise-induced burst and spike synchronizations by employing realistic order parameters and statistical-mechanical measures introduced in our recent work. Thus, the bursting and spiking thresholds for the burst and spike synchronization transitions are determined in terms of the bursting and spiking order parameters, respectively. Furthermore, we also measure the degrees of burst and spike synchronizations in terms of the statistical-mechanical bursting and spiking measures, respectively.
      PubDate: 2014-11-29
  • Pinning synchronization of coupled inertial delayed neural networks
    • Abstract: The paper is devoted to the investigation of synchronization for an array of linearly and diffusively coupled inertial delayed neural networks (DNNs). By placing feedback control on a small fraction of network nodes, the entire coupled DNNs can be synchronized to a common objective trajectory asymptotically. Two different analysis methods, including matrix measure strategy and Lyapunov–Krasovskii function approach, are employed to provide sufficient criteria for the synchronization control problem. Comparisons of these two techniques are given at the end of the paper. Finally, an illustrative example is provided to show the effectiveness of the obtained theoretical results.
      PubDate: 2014-11-26
  • Control of absence seizures induced by the pathways connected to SRN in
           corticothalamic system
    • Abstract: The cerebral cortex, thalamus and basal ganglia together form an important network in the brain, which is closely related to several nerve diseases, such as parkinson disease, epilepsy seizure and so on. Absence seizure can be characterized by 2–4 Hz oscillatory activity, and it can be induced by abnormal interactions between the cerebral cortex and thalamus. Many experimental results have also shown that basal ganglia are a key neural structure, which closely links the corticothalamic system in the brain. Presently, we use a corticothalamic-basal ganglia model to study which pathways in corticothalamic system can induce absence seizures and how these oscillatory activities can be controlled by projections from the substantia nigra pars reticulata (SNr) to the thalamic reticular nucleus (TRN) or the specific relay nuclei (SRN) of the thalamus. By tuning the projection strength of the pathway “Excitatory pyramidal cortex-SRN”, ”SRN-Excitatory pyramidal cortex” and “SRN–TRN” respectively, different firing states including absence seizures can appear. This indicates that absence seizures can be induced by tuning the connection strength of the considered pathway. In addition, typical absence epilepsy seizure state “spike-and-slow wave discharges” can be controlled by adjusting the activation level of the SNr as the pathways SNr–SRN and SNr–TRN open independently or together. Our results emphasize the importance of basal ganglia in controlling absence seizures in the corticothalamic system, and can provide a potential idea for the clinical treatment.
      PubDate: 2014-11-25
  • Brain electrical activities of dancers and fast ball sports athletes are
    • Abstract: Exercise training has been shown not only to influence physical fitness positively but also cognition in healthy and impaired populations. However, some particular exercise types, even though comparable based on physical efforts, have distinct cognitive and sensorimotor features. In this study, the effects of different types of exercise, such as fast ball sports and dance training, on brain electrical activity were investigated. Electroencephalography (EEG) scans were recorded in professional dancer, professional fast ball sports athlete (FBSA) and healthy control volunteer groups consisting of twelve subjects each. In FBSA, power of delta and theta frequency activities of EEG was significantly higher than those of the dancers and the controls. Conversely, dancers had significantly higher amplitudes in alpha and beta bands compared to FBSA and significantly higher amplitudes in the alpha band in comparison with controls. The results suggest that cognitive features of physical training can be reflected in resting brain electrical oscillations. The differences in resting brain electrical oscillations between the dancers and the FBSA can be the result of innate network differences determining the talents and/or plastic changes induced by physical training.
      PubDate: 2014-11-23
  • Improving N1 classification by grouping EEG trials with phases of
           pre-stimulus EEG oscillations
    • Abstract: A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time–frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.
      PubDate: 2014-11-19
  • EEG-based investigation of brain connectivity changes in psychotic
           patients undergoing the primitive expression form of dance therapy: a
           methodological pilot study
    • Abstract: Primitive expression (PE) is a form of dance therapy (DT) that involves an interaction of ethologically and socially based forms which are supplied for re-enactment. There exist very few studies of DT applications including in their protocol the measurement of neurophysiological parameters. The present pilot study investigates the use of the correlation coefficient (ρ) and mutual information (MI), and of novel measures extracted from ρ and MI, on electroencephalographic (EEG) data recorded in patients with schizophrenia while they undergo PE DT, in order to expand the set of neurophysiology-based approaches for quantifying possible DT effects, using parameters that might provide insights about any potential brain connectivity changes in these patients during the PE DT process. Indication is provided for an acute potentiation effect, apparent at late-stage PE DT, on the inter-hemispheric connectivity in frontal areas, as well as for attenuation of the inter-hemispheric connectivity of left frontal and right central areas and for potentiation of the intra-hemispheric connectivity of frontal and central areas, bilaterally, in the transition from early to late-stage PE DT. This pilot study indicates that by using EEG connectivity measures based on ρ and MI, the set of useful neurophysiology-based approaches for quantifying possible DT effects is expanded. In the framework of the present study, the causes of the observed connectivity changes cannot be attributed with certainty to PE DT, but indications are provided that these measures may contribute to a detailed assessment of neurophysiological mechanisms possibly being affected by this therapeutic process.
      PubDate: 2014-11-14
  • The stability of impulsive stochastic Cohen–Grossberg neural networks
           with mixed delays and reaction–diffusion terms
    • Abstract: The global asymptotic stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms is investigated. Under some suitable assumptions and using Lyapunov–Krasovskii functional method, we apply the linear matrix inequality technique to propose some new sufficient conditions for the global asymptotic stability of the addressed model in the stochastic sense. The mixed time delays comprise both the time-varying and continuously distributed delays. The effectiveness of the theoretical result is illustrated by a numerical example.
      PubDate: 2014-11-04
  • Psychomotor functions at various weeks of chronic renal failure in rats
    • Abstract: In chronic renal failure there is a gradual retention of substances in the tissues and body fluids, called as uremic retention toxins, which can bring about a number of biochemical activities in the body. Chronic renal insufficiency also leads to progressive behavioural conflict. Uremic toxins can affect both the central and the peripheral nervous system. Uremic encephalopathy is also associated with problems in cognition and memory. To study the psychomotor functional disorders in rats with progressive chronic renal failure surgical nephrectomy was done by resection method. The animals were grouped into two control groups, Sham control (SC) and normal control (NC) and two uremic groups, moderate uremia (GM) and severe uremia (GS). Psychomotor analysis was done by passive avoidance and open field in these animals at 4, 8, 12, and 16 weeks. After the incubation period, the nephrectomised groups (GM and GS) showed significant changes in exploratory, locomotor and emotional behaviour when compared to the controls (NC and SC). Psychomotor changes involve poor cognition, reduced memory, reduced locomotor activity and decreased exploratory drive and emotional disturbance like increased fear during the initial stages. During the later stages a restless behaviour was noticed, associated with diminished fear.
      PubDate: 2014-10-28
  • Long range temporal correlations in EEG oscillations of subclinically
           depressed individuals: their association with brooding and suppression
    • Abstract: Long-range temporal correlations (LRTC) in brain oscillations have been found to be associated with depression severity in clinically depressed patients. Less is known, however, about the relationships between LRTC and proneness to engage in depression-related cognitive emotion regulation (ER) strategies which characterize both clinically and subclinically depressed (SBD) people. In this study we applied detrended fluctuation analysis to the amplitude envelope of broad band, theta band, and alpha band spontaneous EEG oscillations of a group of SBD individuals and a group of non-depressed individuals (both groups from a sample of healthy adults, N = 120), to whom brooding and thought suppression questionnaires were administered. Between-groups differences were not found for any band scaling exponents at any brain location, but linear correlations pointed out several associations between exponents at frontal, central, parietal, temporal, and occipital sites and maladaptive ER strategies. These results suggest that alterations in brain dynamics are related with the proneness that depressive individuals show to engage in brooding and thought suppression in order to cognitively regulate their emotions.
      PubDate: 2014-10-12
  • Stability analysis of memristor-based fractional-order neural networks
           with different memductance functions
    • Abstract: In this paper, the problem of the existence, uniqueness and uniform stability of memristor-based fractional-order neural networks (MFNNs) with two different types of memductance functions is extensively investigated. Moreover, we formulate the complex-valued memristor-based fractional-order neural networks (CVMFNNs) with two different types of memductance functions and analyze the existence, uniqueness and uniform stability of such networks. By using Banach contraction principle and analysis technique, some sufficient conditions are obtained to ensure the existence, uniqueness and uniform stability of the considered MFNNs and CVMFNNs with two different types of memductance functions. The analysis results establish from the theory of fractional-order differential equations with discontinuous right-hand sides. Finally, four numerical examples are presented to show the effectiveness of our theoretical results.
      PubDate: 2014-10-09
  • Determination of the effects on learning and memory performance and
           related gene expressions of clothianidin in rat models
    • Abstract: Clothianidin (CLO) is one of the pesticides used to protect against insects, and its potential toxic effects on cognitive functions are not clearly known. This study aims to evaluate the possible effects of dose-dependent CLO on learning and memory in infant and adult male rats and the expression of related genes in the hippocampus. Doses of 2, 8 and 24 mg/kg of CLO were administered to newborn infant and adult albino Winstar rats in the form of gavage and dissolved in vehicle matter. Their cognitive and learning functions were evaluated by the Morris water maze and probe tests. Expression levels of N-methyl D-aspartate 1 (GRIN1), muscuranic receptor M1, synoptophysin (SYP) and growth-associated protein 43 (GAP-43) of tissues isolated from the hippocampus were determined using the real-time PCR method. In the Morris water maze test, no change (p > 0.05) was exhibited in the adult and infant rats after CLO was applied, although there was a significant difference (p < 0.05) in performance between infants and the control group after 24 mg/kg was applied in the probe test. Also, expression levels GRIN1, M1, SYP, GAP-43 did not change when compared to the control (p > 0.05). Our study shows that exposure to high doses of CLO causes deterioration of cognitive functions in infant rats.
      PubDate: 2014-10-01
  • Within-session dynamics of theta–gamma coupling and high-frequency
           oscillations during spatial alternation in rat hippocampal area CA1
    • Abstract: Theta–gamma coupling in the hippocampus is thought to be involved in cognitive processes. A large body of research establishes that the hippocampus plays a crucial role in the organization and maintenance of episodic memory, and that sharp-wave ripples (SWR) contribute to memory consolidation processes. Here, we investigated how the local field potentials in the hippocampal CA1 area adapted along with rats’ behavioral changes within a session during a spatial alternation task that included a 1-s fixation and a 1.5-s delay. We observed that, as the session progressed, the duration from fixation onset to nose-poking in the choice hole reduced as well as the number of premature responses during the delay. Parallel with the behavioral transitions, the power of high gamma during the delay period increased whereas that of low gamma decreased later in the session. Furthermore, the strength of theta–gamma modulation later in the session showed significant increase as compared to earlier in the session. Examining SWR during the reward period, we found that the number of SWR events decreased as well as the power in a wide frequency range during SWR events. In addition, the correlation between SWR and gamma oscillations just before SWR events was higher in the earlier trials than in the later trials. Our findings support the notion that the inputs from CA3 and entorhinal cortex play a critical role in memory consolidation as well as in cognitive processes. We suggest that SWR and the inputs from the two areas serve to stabilize the task behavior and neural activities.
      PubDate: 2014-10-01
  • Encoding brain network response to free viewing of videos
    • Abstract: A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain’s responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain’s activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.
      PubDate: 2014-10-01
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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