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Sample records for brain network synchronization

  1. Differential synchronization in default and task-specific networks of the human brain

    Directory of Open Access Journals (Sweden)

    Aaron eKirschner

    2012-05-01

    Full Text Available On a regional scale the brain is organized into dynamic functional networks. The activity within one of these, the default network, can be dissociated from that in other task-specific networks. All brain networks are connected structurally, but evidently are only transiently connected functionally. One hypothesis as to how such transient functional coupling occurs is that network formation and dissolution is mediated, or at least accompanied, by increases and decreases in oscillatory synchronization between constituent brain regions. If so, then we should be able to find transient differences in intra-network synchronization between the default network and a task-specific network. In order to investigate this hypothesis we conducted two experiments in which subjects engaged in a Sustained Attention to Response Task (SART while having brain activity recorded via high-density electroencephalography (EEG. We found that during periods when attention was focused internally (mind-wandering there was significantly more neural phase synchronization between brain regions associated with the default network, whereas during periods when subjects were focused on performing the visual task there was significantly more neural phase synchrony within a task-specific brain network that shared some of the same brain regions. These differences in network synchrony occurred in each of theta, alpha, and gamma frequency bands. A similar pattern of differential oscillatory power changes, indicating modulation of local synchronization by attention state, was also found. These results provide further evidence that the human brain is intrinsically organized into default and task-specific brain networks, and confirm that oscillatory synchronization is a potential mechanism for functional coupling within these networks.

  2. Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks

    Directory of Open Access Journals (Sweden)

    Minkyung Kim

    2017-06-01

    Full Text Available How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are “progressive and earlier” or “abrupt but delayed” account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious

  3. Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

    Science.gov (United States)

    Rosário, R. S.; Cardoso, P. T.; Muñoz, M. A.; Montoya, P.; Miranda, J. G. V.

    2015-12-01

    The major aim of this work was to propose a new association method known as Motif-Synchronization. This method was developed to provide information about the synchronization degree and direction between two nodes of a network by counting the number of occurrences of some patterns between any two time series. The second objective of this work was to present a new methodology for the analysis of dynamic brain networks, by combining the Time-Varying Graph (TVG) method with a directional association method. We further applied the new algorithms to a set of human electroencephalogram (EEG) signals to perform a dynamic analysis of the brain functional networks (BFN).

  4. Structure function relationship in complex brain networks expressed by hierarchical synchronization

    Science.gov (United States)

    Zhou, Changsong; Zemanová, Lucia; Zamora-López, Gorka; Hilgetag, Claus C.; Kurths, Jürgen

    2007-06-01

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

  5. Macroscopic and microscopic spectral properties of brain networks during local and global synchronization

    Science.gov (United States)

    Maksimenko, Vladimir A.; Lüttjohann, Annika; Makarov, Vladimir V.; Goremyko, Mikhail V.; Koronovskii, Alexey A.; Nedaivozov, Vladimir; Runnova, Anastasia E.; van Luijtelaar, Gilles; Hramov, Alexander E.; Boccaletti, Stefano

    2017-07-01

    We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators, which evolve adaptively according to homophilic and homeostatic adaptive principles, we give evidence that the increase of synchronization within groups of nodes (and the corresponding formation of synchronous clusters) causes also the defragmentation of the wavelet energy spectrum of the macroscopic signal. Our methodology is then applied to getting a glance into the microscopic interactions occurring in a neurophysiological system, namely, in the thalamocortical neural network of an epileptic brain of a rat, where the group electrical activity is registered by means of multichannel EEG. We demonstrate that it is possible to infer the degree of interaction between the interconnected regions of the brain during different types of brain activities and to estimate the regions' participation in the generation of the different levels of consciousness.

  6. Brain network dynamics characterization in epileptic seizures. Joint directed graph and pairwise synchronization measures

    Science.gov (United States)

    Rodrigues, A. C.; Machado, B. S.; Florence, G.; Hamad, A. P.; Sakamoto, A. C.; Fujita, A.; Baccalá, L. A.; Amaro, E.; Sameshima, K.

    2014-12-01

    Here we propose and evaluate a new approach to analyse multichannel mesial temporal lobe epilepsy EEG data from eight patients through complex network and synchronization theories. The method employs a Granger causality test to infer the directed connectivity graphs and a wavelet transform based phase synchronization measure whose characteristics allow studying dynamical transitions during epileptic seizures. We present a new combined graph measure that quantifies the level of network hub formation, called network hub out-degree, which closely reflects the level of synchronization observed during the ictus.

  7. Fast sparsely synchronized brain rhythms in a scale-free neural network

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2015-08-01

    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> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 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 synchronization emerges in the whole population because the spatial correlation length between the neuronal pairs covers the whole system. Furthermore, the degree of population synchronization is also measured in terms of two types of realistic statistical-mechanical measures. Only for the partial and sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in 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

  8. Synchronization of networks

    Indian Academy of Sciences (India)

    2015-11-27

    Nov 27, 2015 ... We study the synchronization of coupled dynamical systems on networks. The dynamics is governed by a local nonlinear oscillator for each node of the network and interactions connecting different nodes via the links of the network. We consider existence and stability conditions for both single- and ...

  9. Synchronization in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Arenas, A.; Diaz-Guilera, A.; Moreno, Y.; Zhou, C.; Kurths, J.

    2007-12-12

    Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.

  10. Human Brain Networks: Spiking Neuron Models, Multistability, Synchronization, Thermodynamics, Maximum Entropy Production, and Anesthetic Cascade Mechanisms

    Directory of Open Access Journals (Sweden)

    Wassim M. Haddad

    2014-07-01

    Full Text Available Advances in neuroscience have been closely linked to mathematical modeling beginning with the integrate-and-fire model of Lapicque and proceeding through the modeling of the action potential by Hodgkin and Huxley to the current era. The fundamental building block of the central nervous system, the neuron, may be thought of as a dynamic element that is “excitable”, and can generate a pulse or spike whenever the electrochemical potential across the cell membrane of the neuron exceeds a threshold. A key application of nonlinear dynamical systems theory to the neurosciences is to study phenomena of the central nervous system that exhibit nearly discontinuous transitions between macroscopic states. A very challenging and clinically important problem exhibiting this phenomenon is the induction of general anesthesia. In any specific patient, the transition from consciousness to unconsciousness as the concentration of anesthetic drugs increases is very sharp, resembling a thermodynamic phase transition. This paper focuses on multistability theory for continuous and discontinuous dynamical systems having a set of multiple isolated equilibria and/or a continuum of equilibria. Multistability is the property whereby the solutions of a dynamical system can alternate between two or more mutually exclusive Lyapunov stable and convergent equilibrium states under asymptotically slowly changing inputs or system parameters. In this paper, we extend the theory of multistability to continuous, discontinuous, and stochastic nonlinear dynamical systems. In particular, Lyapunov-based tests for multistability and synchronization of dynamical systems with continuously differentiable and absolutely continuous flows are established. The results are then applied to excitatory and inhibitory biological neuronal networks to explain the underlying mechanism of action for anesthesia and consciousness from a multistable dynamical system perspective, thereby providing a

  11. Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

    Science.gov (United States)

    Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

    2017-02-01

    Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

  12. Development of Network Synchronization Predicts Language Abilities.

    Science.gov (United States)

    Doesburg, Sam M; Tingling, Keriann; MacDonald, Matt J; Pang, Elizabeth W

    2016-01-01

    Synchronization of oscillations among brain areas is understood to mediate network communication supporting cognition, perception, and language. How task-dependent synchronization during word production develops throughout childhood and adolescence, as well as how such network coherence is related to the development of language abilities, remains poorly understood. To address this, we recorded magnetoencephalography while 73 participants aged 4-18 years performed a verb generation task. Atlas-guided source reconstruction was performed, and phase synchronization among regions was calculated. Task-dependent increases in synchronization were observed in the theta, alpha, and beta frequency ranges, and network synchronization differences were observed between age groups. Task-dependent synchronization was strongest in the theta band, as were differences between age groups. Network topologies were calculated for brain regions associated with verb generation and were significantly associated with both age and language abilities. These findings establish the maturational trajectory of network synchronization underlying expressive language abilities throughout childhood and adolescence and provide the first evidence for an association between large-scale neurophysiological network synchronization and individual differences in the development of language abilities.

  13. [Synchronization and propagation of electrical potentials in neural networks of the brain cortex].

    Science.gov (United States)

    Marchenko, V G; Zaĭchenko, M I

    2014-01-01

    On the basis of the published data the scheme is proposed which explains the spread and synhronization of oscillatory activity in cortex. The main property of the neocortex is the existence of vertically oriented functional columns. Within and between the neuronal columns exist a feedforward and feedback morphological and functional connections. At the certain conditions inside the single module temporal windows are created using inhibitory process, and a synchronized activity can be generated of variable frequency (oscillations). The activity in the columns is enhanced by the synchronous involvement of great amount of neurons which is expressed in the form of local field potentials of high amplitude. Further the information about arisen in a single generator activity is transmitted through direct links to the nearby generator or to the group of such generators. In the nearby generator the activity increases and is transferred to the next generator. At the same time the signal is transmitted through feedback to the primary generator, and the activity is terminated until the next cycle will be initiated and so on along the cortex. The most important notion is that in the transfer of activity from one generator to another is involved a small number of elements. The major part of recorded oscillations of different frequency or epileptiform discharges, is not transmitted via the brain, but is generated in each module according to the characteristics transferred to it. The generation of epileptiform spikes occurs in cases when balance of inhibition determined by GABA A and GABA B receptors is disturbed.

  14. Investigating Synchronous Oscillation and Deep Brain Stimulation Treatment in A Model of Cortico-Basal Ganglia Network.

    Science.gov (United States)

    Lu, Meili; Wei, Xile; Loparo, Kenneth A

    2017-11-01

    Altered firing properties and increased pathological oscillations in the basal ganglia have been proven to be hallmarks of Parkinson's disease (PD). Increasing evidence suggests that abnormal synchronous oscillations and suppression in the cortex may also play a critical role in the pathogenic process and treatment of PD. In this paper, a new closed-loop network including the cortex and basal ganglia using the Izhikevich models is proposed to investigate the synchrony and pathological oscillations in motor circuits and their modulation by deep brain stimulation (DBS). Results show that more coherent dynamics in the cortex may cause stronger effects on the synchrony and pathological oscillations of the subthalamic nucleus (STN). The pathological beta oscillations of the STN can both be efficiently suppressed with DBS applied directly to the STN or to cortical neurons, respectively, but the underlying mechanisms by which DBS suppresses the beta oscillations are different. This research helps to understand the dynamics of pathological oscillations in PD-related motor regions and supports the therapeutic potential of stimulation of cortical neurons.

  15. Macroscopic and microscopic spectral properties of brain networks during local and global synchronization

    NARCIS (Netherlands)

    Maksimenko, V.A.; Lüttjohann, A.; Makarov, V.V.; Goremyko, M.V.; Koronovskii, A.A.; Nedaivozov, V.; Runnova, A.E.; Luijtelaar, E.L.J.M. van; Hramov, A.E.; Boccaletti, S.

    2017-01-01

    We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators which evolve

  16. Synchronization of networks

    Indian Academy of Sciences (India)

    with time. Such a time-varying topology can occur in social networks, computer networks, WWW, biological systems, spread of epidemics etc. Here, we investigate ... this periodic rotation is to take projections of different expansions and contrac- tions along different directions. This has the effect of reducing the spread of the.

  17. Synchronization in oscillatory networks

    CERN Document Server

    Osipov, Grigory V; Zhou, Changsong

    2007-01-01

    The formation of collective behavior in large ensembles or networks of coupled oscillatory elements is one of the oldest and most fundamental aspects of dynamical systems theory. Potential and present applications span a vast spectrum of fields ranging from physics, chemistry, geoscience, through life- and neurosciences to engineering, the economic and the social sciences. This work systematically investigates a large number of oscillatory network configurations that are able to describe many real systems such as electric power grids, lasers or the heart muscle - to name but a few. This book is conceived as an introduction to the field for graduate students in physics and applied mathematics as well as being a compendium for researchers from any field of application interested in quantitative models.

  18. Pinning Synchronization of Switched Complex Dynamical Networks

    Directory of Open Access Journals (Sweden)

    Liming Du

    2015-01-01

    Full Text Available Network topology and node dynamics play a key role in forming synchronization of complex networks. Unfortunately there is no effective synchronization criterion for pinning synchronization of complex dynamical networks with switching topology. In this paper, pinning synchronization of complex dynamical networks with switching topology is studied. Two basic problems are considered: one is pinning synchronization of switched complex networks under arbitrary switching; the other is pinning synchronization of switched complex networks by design of switching when synchronization cannot achieved by using any individual connection topology alone. For the two problems, common Lyapunov function method and single Lyapunov function method are used respectively, some global synchronization criteria are proposed and the designed switching law is given. Finally, simulation results verify the validity of the results.

  19. Fitness for synchronization of network motifs

    DEFF Research Database (Denmark)

    Vega, Y.M.; Vázquez-Prada, M.; Pacheco, A.F.

    2004-01-01

    We study the synchronization of Kuramoto's oscillators in small parts of networks known as motifs. We first report on the system dynamics for the case of a scale-free network and show the existence of a non-trivial critical point. We compute the probability that network motifs synchronize, and fi...... that the fitness for synchronization correlates well with motifs interconnectedness and structural complexity. Possible implications for present debates about network evolution in biological and other systems are discussed....

  20. Interneuron Deficit Associates Attenuated Network Synchronization to Mismatch of Energy Supply and Demand in Aging Mouse Brains

    DEFF Research Database (Denmark)

    Jessen, Sanne Barsballe; Mathiesen, Claus; Lind, Barbara Lykke

    2017-01-01

    Higher cognitive functions depend critically on synchronized network activity in the gamma range (30-100 Hz), which results from activity of fast-spiking parvalbumin-positive (PV) interneurons. Here, we examined synaptic activity in the gamma band in relation to PV interneuron activity, stimulation......-induced calcium activity in neurons and astrocytes, and cerebral blood flow and oxygen responses in the somatosensory cortex of young adult and old adult mice in vivo using electrical whisker pad stimulation. Gamma activity was reduced in old adult mice, and associated with reduced calcium activity of PV......-dependent rise in O2 use, that is, the rise in the cerebral metabolic rate of oxygen (CMRO2) evoked by excitatory postsynaptic currents almost doubled in old adult mice. We conclude that PV interneuron function and gamma activity are particularly affected in old adult mice. Alterations in neurovascular coupling...

  1. Synchronized Data Aggregation for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2014-01-01

    Wireless Sensor Networks (WSNs) are used for monitoring and data collection purposes. A key challenge in effective data collection is to schedule and synchronize the activities of the nodes with global clock. This paper proposes the Synchronized Data Aggregation Algorithm (SDA) using spanning tree...... mechanism. It provides network-wide time synchronization for sensor network. In the initial stage algorithm established the hierarchical structure in the network and then perform the pair - wise synchronization. SDA aggregate data with a global time scale throughout the network. The aggregated packets...

  2. Fundamental Dynamical Modes Underlying Human Brain Synchronization

    Directory of Open Access Journals (Sweden)

    Catalina Alvarado-Rojas

    2012-01-01

    Full Text Available Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

  3. A linear model for characterization of synchronization frequencies of neural networks.

    Science.gov (United States)

    Lv, Peili; Hu, Xintao; Lv, Jinglei; Han, Junwei; Guo, Lei; Liu, Tianming

    2014-02-01

    The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.

  4. Disturbed temporal dynamics of brain synchronization in vision loss.

    Science.gov (United States)

    Bola, Michał; Gall, Carolin; Sabel, Bernhard A

    2015-06-01

    Damage along the visual pathway prevents bottom-up visual input from reaching further processing stages and consequently leads to loss of vision. But perception is not a simple bottom-up process - rather it emerges from activity of widespread cortical networks which coordinate visual processing in space and time. Here we set out to study how vision loss affects activity of brain visual networks and how networks' activity is related to perception. Specifically, we focused on studying temporal patterns of brain activity. To this end, resting-state eyes-closed EEG was recorded from partially blind patients suffering from chronic retina and/or optic-nerve damage (n = 19) and healthy controls (n = 13). Amplitude (power) of oscillatory activity and phase locking value (PLV) were used as measures of local and distant synchronization, respectively. Synchronization time series were created for the low- (7-9 Hz) and high-alpha band (11-13 Hz) and analyzed with three measures of temporal patterns: (i) length of synchronized-/desynchronized-periods, (ii) Higuchi Fractal Dimension (HFD), and (iii) Detrended Fluctuation Analysis (DFA). We revealed that patients exhibit less complex, more random and noise-like temporal dynamics of high-alpha band activity. More random temporal patterns were associated with worse performance in static (r = -.54, p = .017) and kinetic perimetry (r = .47, p = .041). We conclude that disturbed temporal patterns of neural synchronization in vision loss patients indicate disrupted communication within brain visual networks caused by prolonged deafferentation. We propose that because the state of brain networks is essential for normal perception, impaired brain synchronization in patients with vision loss might aggravate the functional consequences of reduced visual input. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Synchronization of oscillators in complex networks

    Indian Academy of Sciences (India)

    Theory of identical or complete synchronization of identical oscillators in arbitrary networks is introduced. In addition, several graph theory concepts and results that augment the synchronization theory and a tie in closely to random, semirandom, and regular networks are introduced. Combined theories are used to explore ...

  6. Synchronization analysis of coloured delayed networks under ...

    Indian Academy of Sciences (India)

    Up to now, many network models on synchronization have been put forward, such as, the small-world network, directed network, neural network etc. Previous efforts were mainly to study the outer relationship between the nodes. But, the inner interaction is always overlooked. Afterwards, the coloured network model has ...

  7. Collapse of Synchronization in a Memristive Network

    Science.gov (United States)

    Lü, Mi; Wang, Chun-Ni; Tang, Jun; Ma, Jun

    2015-12-01

    For an oscillating circuit or coupled circuits, damage in electric devices such as inductor, resistance, memristor even capacitor can cause breakdown or collapse of the circuits. These damage could be associated with external attack or aging in electric devices, and then the bifurcation parameters could be deformed from normal values. Resonators or signal generators are often synchronized to produce powerful signal series and this problem could be investigated by using synchronization in network. Complete synchronization could be induced by linear coupling in a two-dimensional network of identical oscillators when the coupling intensity is beyond certain threshold. The collective behavior and synchronization state are much dependent on the bifurcation parameters. Any slight fluctuation in parameter and breakdown in bifurcation parameter can cause transition of synchronization even collapse of synchronization in the network. In this paper, a two-dimensional network composed of the resonators coupled with memristors under nearest-neighbor connection is designed, and the network can reach complete synchronization by carefully selecting coupling intensity. The network keeps synchronization after certain transient period, then a bifurcation parameter in a resonator is switched from the previous value and the adjacent resonators (oscillators) are affected in random. It is found that the synchronization area could be invaded greatly in a diffusive way. The damage area size is much dependent on the selection of diffusive period of damage and deformation degree in the parameter. Indeed, the synchronization area could keep intact at largest size under intermediate deformation degree and coupling intensity. Supported by the National Natural Science of China under Grant Nos. 11265008 and 11365014

  8. Synchronization of coupled chaotic dynamics on networks

    Indian Academy of Sciences (India)

    Abstract. We review some recent work on the synchronization of coupled dynamical systems on a variety of networks. When nodes show synchronized behaviour, two inter- esting phenomena can be observed. First, there are some nodes of the floating type that show intermittent behaviour between getting attached to some ...

  9. Synchronization, non-linear dynamics and low-frequency fluctuations: Analogy between spontaneous brain activity and networked single-transistor chaotic oscillators

    Science.gov (United States)

    Minati, Ludovico; Chiesa, Pietro; Tabarelli, Davide; D'Incerti, Ludovico; Jovicich, Jorge

    2015-03-01

    In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D2), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.

  10. Synchronization, non-linear dynamics and low-frequency fluctuations: Analogy between spontaneous brain activity and networked single-transistor chaotic oscillators

    Energy Technology Data Exchange (ETDEWEB)

    Minati, Ludovico, E-mail: lminati@ieee.org, E-mail: ludovico.minati@unitn.it, E-mail: lminati@istituto-besta.it [Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan (Italy); Center for Mind/Brain Sciences, University of Trento, Trento (Italy); Chiesa, Pietro; Tabarelli, Davide; Jovicich, Jorge [Center for Mind/Brain Sciences, University of Trento, Trento (Italy); D' Incerti, Ludovico [Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan (Italy)

    2015-03-15

    In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D{sub 2}), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.

  11. Synchronization analysis of coloured delayed networks under ...

    Indian Academy of Sciences (India)

    This paper investigates synchronization of coloured delayed networks under decentralized pinning intermittent control. To begin with, the time delays are taken into account in the coloured networks. In addition, we propose a decentralized pinning intermittent control for coloured delayed networks, which is different from that ...

  12. Queue-length synchronization in communication networks.

    Science.gov (United States)

    Mukherjee, Satyam; Gupte, Neelima

    2009-05-01

    We study the synchronization in the context of network traffic on a 2-d communication network with local clustering and geographic separations. The network consists of nodes and randomly distributed hubs where the top five hubs ranked according to their coefficient of betweenness centrality (CBC) are connected by random assortative and gradient mechanisms. For multiple message traffic, messages can trap at the high CBC hubs, and congestion can build up on the network with long queues at the congested hubs. The queue lengths are seen to synchronize in the congested phase. Both complete and phase synchronization are seen, between pairs of hubs. In the decongested phase, the pairs start clearing and synchronization is lost. A cascading master-slave relation is seen between the hubs, with the slower hubs (which are slow to decongest) driving the faster ones. These are usually the hubs of high CBC. Similar results are seen for traffic of constant density. Total synchronization between the hubs of high CBC is also seen in the congested regime. Similar behavior is seen for traffic on a network constructed using the Waxman random topology generator. We also demonstrate the existence of phase synchronization in real internet traffic data.

  13. Synchronization of mobile chaotic oscillator networks

    Energy Technology Data Exchange (ETDEWEB)

    Fujiwara, Naoya, E-mail: fujiwara@csis.u-tokyo.ac.jp [Center for Spatial Information Science, The University of Tokyo, 277-8568 Chiba (Japan); Kurths, Jürgen [Potsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany and Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen (United Kingdom); Díaz-Guilera, Albert [Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain and Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona (Spain)

    2016-09-15

    We study synchronization of systems in which agents holding chaotic oscillators move in a two-dimensional plane and interact with nearby ones forming a time dependent network. Due to the uncertainty in observing other agents' states, we assume that the interaction contains a certain amount of noise that turns out to be relevant for chaotic dynamics. We find that a synchronization transition takes place by changing a control parameter. But this transition depends on the relative dynamic scale of motion and interaction. When the topology change is slow, we observe an intermittent switching between laminar and burst states close to the transition due to small noise. This novel type of synchronization transition and intermittency can happen even when complete synchronization is linearly stable in the absence of noise. We show that the linear stability of the synchronized state is not a sufficient condition for its stability due to strong fluctuations of the transverse Lyapunov exponent associated with a slow network topology change. Since this effect can be observed within the linearized dynamics, we can expect such an effect in the temporal networks with noisy chaotic oscillators, irrespective of the details of the oscillator dynamics. When the topology change is fast, a linearized approximation describes well the dynamics towards synchrony. These results imply that the fluctuations of the finite-time transverse Lyapunov exponent should also be taken into account to estimate synchronization of the mobile contact networks.

  14. Network response synchronization enhanced by synaptic plasticity

    Science.gov (United States)

    Lobov, S.; Simonov, A.; Kastalskiy, I.; Kazantsev, V.

    2016-02-01

    Synchronization of neural network response on spatially localized periodic stimulation was studied. The network consisted of synaptically coupled spiking neurons with spike-timing-dependent synaptic plasticity (STDP). Network connectivity was defined by time evolving matrix of synaptic weights. We found that the steady-state spatial pattern of the weights could be rearranged due to locally applied external periodic stimulation. A method for visualization of synaptic weights as vector field was introduced to monitor the evolving connectivity matrix. We demonstrated that changes in the vector field and associated weight rearrangements underlay an enhancement of synchronization range.

  15. Transient Synchronization in Complex Neuronal Networks

    CERN Document Server

    Costa, Luciano da Fontoura

    2008-01-01

    Transient synchronization in complex neuronal networks as a consequence of activation-conserved dynamics induced by having sources placed at specific neurons is investigated. The basic integrate-and-fire neuron is adopted, and the dynamics is estimated computationally so as to obtain the activation at each node along each instant of time. The dynamics is implemented so as to conserve the total activation entering the system, which is a distinctive feature of the current work. The synchronization of the activation of the network is then quantified along time in terms of its normalized instantaneous entropy. The potential of such concepts and measurements is explored with respect to 6 theoretical models, as well as for the neuronal network of \\emph{C. elegans}. A series of interesting results are obtained and discussed, including the fact that all models led to a transient period of synchronization, whose specific features depend heavily on the topological features of the networks.

  16. Forced synchronization of autonomous dynamical Boolean networks

    Energy Technology Data Exchange (ETDEWEB)

    Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx [División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica A. C., Camino a la Presa San José 2055, Col. Lomas 4 Sección, C.P. 78216, San Luis Potosí, S.L.P. (Mexico); Campos-Cantón, I. [Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, C.P. 78000, San Luis Potosí, S.L.P. (Mexico); Gauthier, Daniel J. [Department of Physics and Center for Nonlinear and Complex Systems, Duke University, Box 90305, Durham, North Carolina 27708 (United States)

    2015-08-15

    We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.

  17. Moving on time: brain network for auditory-motor synchronization is modulated by rhythm complexity and musical training.

    Science.gov (United States)

    Chen, Joyce L; Penhune, Virginia B; Zatorre, Robert J

    2008-02-01

    Much is known about the motor system and its role in simple movement execution. However, little is understood about the neural systems underlying auditory-motor integration in the context of musical rhythm, or the enhanced ability of musicians to execute precisely timed sequences. Using functional magnetic resonance imaging, we investigated how performance and neural activity were modulated as musicians and nonmusicians tapped in synchrony with progressively more complex and less metrically structured auditory rhythms. A functionally connected network was implicated in extracting higher-order features of a rhythm's temporal structure, with the dorsal premotor cortex mediating these auditory-motor interactions. In contrast to past studies, musicians recruited the prefrontal cortex to a greater degree than nonmusicians, whereas secondary motor regions were recruited to the same extent. We argue that the superior ability of musicians to deconstruct and organize a rhythm's temporal structure relates to the greater involvement of the prefrontal cortex mediating working memory.

  18. Clock Synchronization for Multihop Wireless Sensor Networks

    Science.gov (United States)

    Solis Robles, Roberto

    2009-01-01

    In wireless sensor networks, more so generally than in other types of distributed systems, clock synchronization is crucial since by having this service available, several applications such as media access protocols, object tracking, or data fusion, would improve their performance. In this dissertation, we propose a set of algorithms to achieve…

  19. Computer network time synchronization the network time protocol

    CERN Document Server

    Mills, David L

    2006-01-01

    What started with the sundial has, thus far, been refined to a level of precision based on atomic resonance: Time. Our obsession with time is evident in this continued scaling down to nanosecond resolution and beyond. But this obsession is not without warrant. Precision and time synchronization are critical in many applications, such as air traffic control and stock trading, and pose complex and important challenges in modern information networks.Penned by David L. Mills, the original developer of the Network Time Protocol (NTP), Computer Network Time Synchronization: The Network Time Protocol

  20. Polyrhythmic synchronization in bursting networking motifs.

    Science.gov (United States)

    Shilnikov, Andrey; Gordon, René; Belykh, Igor

    2008-09-01

    We study the emergence of polyrhythmic dynamics of motifs which are the building block for small inhibitory-excitatory networks, such as central pattern generators controlling various locomotive behaviors of animals. We discover that the pacemaker determining the specific rhythm of such a network composed of realistic Hodgkin-Huxley-type neurons is identified through the order parameter, which is the ratio of the neurons' burst durations or of duty cycles. We analyze different configurations of the motifs and describe the universal mechanisms for synergetics of the bursting patterns. We discuss also the multistability of inhibitory networks that results in polyrhythmicity of its emergent synchronous behaviors. (c) 2008 American Institute of Physics.

  1. Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity

    Science.gov (United States)

    Ponce-Alvarez, Adrián; Deco, Gustavo; Hagmann, Patric; Romani, Gian Luca; Mantini, Dante; Corbetta, Maurizio

    2015-01-01

    Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain’s anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous. PMID:25692996

  2. Emergent synchronous bursting of oxytocin neuronal network.

    Directory of Open Access Journals (Sweden)

    Enrico Rossoni

    2008-07-01

    Full Text Available When young suckle, they are rewarded intermittently with a let-down of milk that results from reflex secretion of the hormone oxytocin; without oxytocin, newly born young will die unless they are fostered. Oxytocin is made by magnocellular hypothalamic neurons, and is secreted from their nerve endings in the pituitary in response to action potentials (spikes that are generated in the cell bodies and which are propagated down their axons to the nerve endings. Normally, oxytocin cells discharge asynchronously at 1-3 spikes/s, but during suckling, every 5 min or so, each discharges a brief, intense burst of spikes that release a pulse of oxytocin into the circulation. This reflex was the first, and is perhaps the best, example of a physiological role for peptide-mediated communication within the brain: it is coordinated by the release of oxytocin from the dendrites of oxytocin cells; it can be facilitated by injection of tiny amounts of oxytocin into the hypothalamus, and it can be blocked by injection of tiny amounts of oxytocin antagonist. Here we show how synchronized bursting can arise in a neuronal network model that incorporates basic observations of the physiology of oxytocin cells. In our model, bursting is an emergent behaviour of a complex system, involving both positive and negative feedbacks, between many sparsely connected cells. The oxytocin cells are regulated by independent afferent inputs, but they interact by local release of oxytocin and endocannabinoids. Oxytocin released from the dendrites of these cells has a positive-feedback effect, while endocannabinoids have an inhibitory effect by suppressing the afferent input to the cells.

  3. Generalized synchronization in complex dynamical networks via adaptive couplings

    NARCIS (Netherlands)

    Liu, Hui; Chen, Juan; Lu, Jun-an; Cao, Ming

    2010-01-01

    This paper investigates generalized synchronization of three typical classes of complex dynamical networks: scale-free networks, small-world networks. and interpolating networks. The proposed synchronization strategy is to adjust adaptively a node's coupling strength based oil the node's local

  4. Intermittent synchronization in a network of bursting neurons.

    Science.gov (United States)

    Park, Choongseok; Rubchinsky, Leonid L

    2011-09-01

    Synchronized oscillations in networks of inhibitory and excitatory coupled bursting neurons are common in a variety of neural systems from central pattern generators to human brain circuits. One example of the latter is the subcortical network of the basal ganglia, formed by excitatory and inhibitory bursters of the subthalamic nucleus and globus pallidus, involved in motor control and affected in Parkinson's disease. Recent experiments have demonstrated the intermittent nature of the phase-locking of neural activity in this network. Here, we explore one potential mechanism to explain the intermittent phase-locking in a network. We simplify the network to obtain a model of two inhibitory coupled elements and explore its dynamics. We used geometric analysis and singular perturbation methods for dynamical systems to reduce the full model to a simpler set of equations. Mathematical analysis was completed using three slow variables with two different time scales. Intermittently, synchronous oscillations are generated by overlapped spiking which crucially depends on the geometry of the slow phase plane and the interplay between slow variables as well as the strength of synapses. Two slow variables are responsible for the generation of activity patterns with overlapped spiking, and the other slower variable enhances the robustness of an irregular and intermittent activity pattern. While the analyzed network and the explored mechanism of intermittent synchrony appear to be quite generic, the results of this analysis can be used to trace particular values of biophysical parameters (synaptic strength and parameters of calcium dynamics), which are known to be impacted in Parkinson's disease.

  5. Synchronous ethernet and IEEE 1588 in telecoms next generation synchronization networks

    CERN Document Server

    2013-01-01

    This book addresses the multiple technical aspects of the distribution of synchronization in new generation telecommunication networks, focusing in particular on synchronous Ethernet and IEEE1588 technologies. Many packet network engineers struggle with understanding the challenges that precise synchronization distribution can impose on networks. The usual “why”, “when” and particularly “how” can cause problems for many engineers. In parallel to this, some other markets have identical synchronization requirements, but with their own design requirements, generating further questions. This book attempts to respond to the different questions by providing background technical information. Invaluable information on state of-the-art packet network synchronization and timing architectures is provided, as well as an unbiased view on the synchronization technologies that have been internationally standardized over recent years, with the aim of providing the average reader (who is not skilled in the art) wi...

  6. Simple synchronization protocols for heterogeneous networks : beyond passivity

    NARCIS (Netherlands)

    Proskurnikov, A.V.; Mazo Espinosa, M.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    2017-01-01

    Synchronization among autonomous agents via local interactions is one of the benchmark problems in multi-agent control. Whereas synchronization algorithms for identical agents have been thoroughly studied, synchronization of heterogeneous networks still remains a challenging problem. The existing

  7. Self-similarity in explosive synchronization of complex networks

    Science.gov (United States)

    Koronovskii, Alexey A.; Kurovskaya, Maria K.; Moskalenko, Olga I.; Hramov, Alexander; Boccaletti, Stefano

    2017-12-01

    We report that explosive synchronization of networked oscillators (a process through which the transition to coherence occurs without intermediate stages but is rather characterized by a sudden and abrupt jump from the network's asynchronous to synchronous motion) is related to self-similarity of synchronous clusters of different size. Self-similarity is revealed by destructing the network synchronous state during the backward transition and observed with the decrease of the coupling strength between the nodes of the network. As illustrative examples, networks of Kuramoto oscillators with different topologies of links have been considered. For each one of such topologies, the destruction of the synchronous state goes step by step with self-similar configurations of interacting oscillators. At the critical point, the invariance of the phase distribution in the synchronized cluster with respect to the cluster size is reported.

  8. Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Axel Hutt

    Full Text Available Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

  9. Stimulus-dependent synchronization in delayed-coupled neuronal networks.

    Science.gov (United States)

    Esfahani, Zahra G; Gollo, Leonardo L; Valizadeh, Alireza

    2016-03-22

    Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand.

  10. Role of Network Topology in the Synchronization of Power Systems

    CERN Document Server

    Lozano, Sergi; Díaz-Guilera, Albert; 10.1140/epjb/e2012-30209-9

    2012-01-01

    We study synchronization dynamics in networks of coupled oscillators with bimodal distribution of natural frequencies. This setup can be interpreted as a simple model of frequency synchronization dynamics among generators and loads working in a power network. We derive the minimum coupling strength required to ensure global frequency synchronization. This threshold value can be efficiently found by solving a binary optimization problem, even for large networks. In order to validate our procedure, we compare its results with numerical simulations on a realistic network describing the European interconnected high-voltage electricity system, finding a very good agreement. Our synchronization threshold can be used to test the stability of frequency synchronization to link removals. As the threshold value changes only in very few cases when aplied to the European realistic network, we conclude that network is resilient in this regard. Since the threshold calculation depends on the local connectivity, it can also b...

  11. Synchronization in Complex Networks of Nonlinear Dynamical Systems

    CERN Document Server

    Wu, Chai Wah

    2007-01-01

    This book brings together two emerging research areas: synchronization in coupled nonlinear systems and complex networks, and study conditions under which a complex network of dynamical systems synchronizes. While there are many texts that study synchronization in chaotic systems or properties of complex networks, there are few texts that consider the intersection of these two very active and interdisciplinary research areas. The main theme of this book is that synchronization conditions can be related to graph theoretical properties of the underlying coupling topology. The book introduces ide

  12. Directional Networking in GPS Denied Environments - Time Synchronization

    Science.gov (United States)

    2016-03-14

    Directional Networking in GPS Denied Environments—Time Synchronization Derya Cansever and Gilbert Green Army CERDEC Aberdeen Proving Ground MA...when GPS is not available. We show that the Fast RTSR algorithm allows the entire network to achieve time synchronization with convergence time of...RF-based measurements to synchronize time and measure node range.  Satellite Doppler: Using Doppler measurements from multiple satellites along

  13. The hippocampus: hub of brain network communication for memory.

    NARCIS (Netherlands)

    Battaglia, F.P.; Benchenane, K.; Sirota, A.; Pennartz, C.M.A.; Wiener, S.I.

    2011-01-01

    A complex brain network, centered on the hippocampus, supports episodic memories throughout their lifetimes. Classically, upon memory encoding during active behavior, hippocampal activity is dominated by theta oscillations (6-10Hz). During inactivity, hippocampal neurons burst synchronously,

  14. Synchronization enhancement via an oscillatory bath in a network of ...

    Indian Academy of Sciences (India)

    2015-02-05

    Feb 5, 2015 ... injection scheme increases the range of noise amplitude for which synchronization occurs in the network. Keywords. Synchronization; oscillatory bath; self-excited cells; noise injection; network. PACS Nos 87.18. ... oscillators circuit consisting of a condenser C, an inductor L and nonlinear resistor NR, all.

  15. Modular Brain Networks.

    Science.gov (United States)

    Sporns, Olaf; Betzel, Richard F

    2016-01-01

    The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.

  16. Consensus and Synchronization in Complex Networks

    CERN Document Server

    2013-01-01

    Synchronization in complex networks is one of the most captivating cooperative phenomena in nature and has been shown to be of fundamental importance in such varied circumstances as the continued existence of species, the functioning of heart pacemaker cells, epileptic seizures, neuronal firing in the feline visual cortex and cognitive tasks in humans. E.g. coupled visual and acoustic interactions make fireflies flash, crickets chirp, and an audience clap in unison. On the other hand, in distributed systems and networks, it is often necessary for some or all of the nodes to calculate some function of certain parameters, e.g. sink nodes in sensor networks being tasked with calculating the average measurement value of all the sensors or multi-agent systems in which all agents are required to coordinate their speed and direction. When all nodes calculate the same function of the initial values in the system, they are said to reach consensus. Such concepts - sometimes also called state agreement, rendezvous, and ...

  17. Finite-time synchronization of inertial neural networks

    Directory of Open Access Journals (Sweden)

    Na Cui

    2017-10-01

    Full Text Available In this paper, the finite-time synchronization of inertial neural networks is investigated. First, to realize synchronization of the master–slave system, continuous and discontinuous controllers are designed, respectively. By constructing Lyapunov function and using inequalities, some effective criteria are provided to realize synchronization in finite time. Furthermore, in order to achieve synchronization with a fast speed, a new switching controller is presented, and the upper bounds of the settling time of synchronization are estimated. Finally, several numerical simulations are presented to demonstrate the validity of the theoretical results and the effectiveness of the proposed method.

  18. Estimation of degree of synchronization in epileptic brain

    Science.gov (United States)

    Moskalenko, Olga I.; Koronovskii, Alexey A.; Pavlov, Alexey N.; Hramov, Alexander E.; Zhuravlev, Maksim O.

    2016-03-01

    The method for calculation of zero conditional Lyapunov exponent from time series has been proposed. Such method is shown to define the degree of synchronization of the regime realized in the system. It has been applied to real experimental neurophysiological time series represented by electroencephalograms of WAG/Rij rats having genetic predisposition to absence-epilepsy. The degree of synchronization in epileptic brain has been found.

  19. Synchronization properties of heterogeneous neuronal networks with mixed excitability type.

    Science.gov (United States)

    Leone, Michael J; Schurter, Brandon N; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G

    2015-03-01

    We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

  20. Computation emerges from adaptive synchronization of networking neurons.

    Directory of Open Access Journals (Sweden)

    Massimiliano Zanin

    Full Text Available The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.

  1. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  2. System and method for time synchronization in a wireless network

    Science.gov (United States)

    Gonia, Patrick S.; Kolavennu, Soumitri N.; Mahasenan, Arun V.; Budampati, Ramakrishna S.

    2010-03-30

    A system includes multiple wireless nodes forming a cluster in a wireless network, where each wireless node is configured to communicate and exchange data wirelessly based on a clock. One of the wireless nodes is configured to operate as a cluster master. Each of the other wireless nodes is configured to (i) receive time synchronization information from a parent node, (ii) adjust its clock based on the received time synchronization information, and (iii) broadcast time synchronization information based on the time synchronization information received by that wireless node. The time synchronization information received by each of the other wireless nodes is based on time synchronization information provided by the cluster master so that the other wireless nodes substantially synchronize their clocks with the clock of the cluster master.

  3. Inter-subject synchronization of brain responses during natural music listening.

    Science.gov (United States)

    Abrams, Daniel A; Ryali, Srikanth; Chen, Tianwen; Chordia, Parag; Khouzam, Amirah; Levitin, Daniel J; Menon, Vinod

    2013-05-01

    Music is a cultural universal and a rich part of the human experience. However, little is known about common brain systems that support the processing and integration of extended, naturalistic 'real-world' music stimuli. We examined this question by presenting extended excerpts of symphonic music, and two pseudomusical stimuli in which the temporal and spectral structure of the Natural Music condition were disrupted, to non-musician participants undergoing functional brain imaging and analysing synchronized spatiotemporal activity patterns between listeners. We found that music synchronizes brain responses across listeners in bilateral auditory midbrain and thalamus, primary auditory and auditory association cortex, right-lateralized structures in frontal and parietal cortex, and motor planning regions of the brain. These effects were greater for natural music compared to the pseudo-musical control conditions. Remarkably, inter-subject synchronization in the inferior colliculus and medial geniculate nucleus was also greater for the natural music condition, indicating that synchronization at these early stages of auditory processing is not simply driven by spectro-temporal features of the stimulus. Increased synchronization during music listening was also evident in a right-hemisphere fronto-parietal attention network and bilateral cortical regions involved in motor planning. While these brain structures have previously been implicated in various aspects of musical processing, our results are the first to show that these regions track structural elements of a musical stimulus over extended time periods lasting minutes. Our results show that a hierarchical distributed network is synchronized between individuals during the processing of extended musical sequences, and provide new insight into the temporal integration of complex and biologically salient auditory sequences. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  4. Inter-subject synchronization of brain responses during natural music listening

    Science.gov (United States)

    Abrams, Daniel A.; Ryali, Srikanth; Chen, Tianwen; Chordia, Parag; Khouzam, Amirah; Levitin, Daniel J.; Menon, Vinod

    2015-01-01

    Music is a cultural universal and a rich part of the human experience. However, little is known about common brain systems that support the processing and integration of extended, naturalistic ‘real-world’ music stimuli. We examined this question by presenting extended excerpts of symphonic music, and two pseudomusical stimuli in which the temporal and spectral structure of the Natural Music condition were disrupted, to non-musician participants undergoing functional brain imaging and analysing synchronized spatiotemporal activity patterns between listeners. We found that music synchronizes brain responses across listeners in bilateral auditory midbrain and thalamus, primary auditory and auditory association cortex, right-lateralized structures in frontal and parietal cortex, and motor planning regions of the brain. These effects were greater for natural music compared to the pseudo-musical control conditions. Remarkably, inter-subject synchronization in the inferior colliculus and medial geniculate nucleus was also greater for the natural music condition, indicating that synchronization at these early stages of auditory processing is not simply driven by spectro-temporal features of the stimulus. Increased synchronization during music listening was also evident in a right-hemisphere fronto-parietal attention network and bilateral cortical regions involved in motor planning. While these brain structures have previously been implicated in various aspects of musical processing, our results are the first to show that these regions track structural elements of a musical stimulus over extended time periods lasting minutes. Our results show that a hierarchical distributed network is synchronized between individuals during the processing of extended musical sequences, and provide new insight into the temporal integration of complex and biologically salient auditory sequences. PMID:23578016

  5. Synchronization Analysis of Master-Slave Probabilistic Boolean Networks.

    Science.gov (United States)

    Lu, Jianquan; Zhong, Jie; Li, Lulu; Ho, Daniel W C; Cao, Jinde

    2015-08-28

    In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then we investigate synchronization with probability one. By resorting to new approach called semi-tensor product (STP), the master-slave PBNs are expressed in equivalent algebraic forms. Based on the algebraic form, some necessary and sufficient criteria are derived to guarantee synchronization with probability one. Further, we study the synchronization of master-slave PBNs in probability. Synchronization in probability implies that for any initial states, the master BN can be synchronized by the slave BN with certain probability, while synchronization with probability one implies that master BN can be synchronized by the slave BN with probability one. Based on the equivalent algebraic form, some efficient conditions are derived to guarantee synchronization in probability. Finally, several numerical examples are presented to show the effectiveness of the main results.

  6. Node Heterogeneity for Energy Efficient Synchronization for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2016-01-01

    is the introduction of heterogeneous nodes regarding energy, and the other is to synchronize the local clock of the node with the global clock of the network. In this context, the paper proposes Node Heterogeneity aware Energy Efficient Synchronization Algorithm (NHES). It works on the formation of cluster......-based spanning tree (SPT). In the initial stage of the algorithm, the nodes are grouped into the cluster and form the tree. The nodes in the cluster and cluster heads in the network are synchronized with the notion of the global time scale of the network. Also, clock skews may cause the errors and be one......The energy of the node in the Wireless Sensor Networks (WSNs) is scare and causes the variation in the lifetime of the network. Also, the throughput and delay of the network depend on how long the network sustains i.e. energy consumption. One way to increase the sustainability of network...

  7. Node Heterogeneity for Energy Efficient Synchronization for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2016-01-01

    is the introduction of heterogeneous nodes regarding energy, and the other is to synchronize the local clock of the node with the global clock of the network. In this context, the paper proposes Node Heterogeneity aware Energy Efficient Synchronization Algorithm (NHES). It works on the formation of cluster......-based spanning tree (SPT). In the initial stage of the algorithm, the nodes are grouped into the cluster and form the tree. The nodes in the cluster and cluster heads in the network are synchronized with the notion of the global time scale of the network. Also, clock skews may cause the errors and be one...

  8. Mobility-aware Hybrid Synchronization for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2015-01-01

    Random mobility of node causes the frequent changes in the network dynamics causing the increased cost in terms of energy and bandwidth. It needs the additional efforts to synchronize the activities of nodes during data collection and transmission in Wireless Sensor Networks (WSNs). A key challenge...... in maintaining the effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Mobility-aware Hybrid Synchronization Algorithm (MHS) which works on the formation of cluster based on spanning tree mechanism (SPT). Nodes used...... for formation of the network have random mobility and heterogeneous in terms of energy with static sink. The nodes in the cluster and cluster heads in the network are synchronized with the notion of global time scale. In the initial stage, the algorithm establishes the hierarchical structure of the network...

  9. Bandwidth Efficient Hybrid Synchronization for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2015-01-01

    Data collection and transmission are the fundamental operations of Wireless Sensor Networks (WSNs). A key challenge in effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Bandwidth Efficient Hybrid...... Synchronization Data Aggregation Algorithm (BESDA) using spanning tree mechanism (SPT). It uses static sink and mobile nodes in the network. BESDA considers the synchronization of a local clock of node with global clock of the network. In the initial stage algorithm established the hierarchical structure...... in the network and then perform the pair-wise synchronization. With the mobility of node, the structure frequently changes causing an increase in energy consumption. To mitigate the problem BESDA aggregate data with the notion of a global timescale throughout the network and schedule based time-division multiple...

  10. Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

    Science.gov (United States)

    Hjorth, Johannes J J; Dawitz, Julia; Kroon, Tim; Pires, Johny; Dassen, Valerie J; Berkhout, Janna A; Emperador Melero, Javier; Nadadhur, Aish G; Alevra, Mihai; Toonen, Ruud F; Heine, Vivi M; Mansvelder, Huibert D; Meredith, Rhiannon M

    2016-04-01

    Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature "silent" cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity-based pixel correlations to identify cellular-based regions of interest (ROIs) and coincident cell activity. However, using activity-based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell-dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi-automatically detect cells within developing neuronal networks that were imaged using calcium-sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers. © 2015 Wiley Periodicals, Inc.

  11. RBF neural network based H∞ synchronization for unknown chaotic ...

    Indian Academy of Sciences (India)

    MS received 9 February 2010; accepted 24 May 2010. Abstract. In this paper, we propose a new H∞ synchronization strategy, called a. Radial Basis Function Neural Network H∞ synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the pro- posed framework, a ...

  12. IPTV inter-destination synchronization: A network-based approach

    NARCIS (Netherlands)

    Stokking, H.M.; Deventer, M.O. van; Niamut, O.A.; Walraven, F.A.; Mekuria, R.N.

    2010-01-01

    This paper introduces a novel network-based approach to inter-destination media synchronization. The approach meets the need for synchronization in advanced TV concepts like social TV and offers high scalability, unlike conventional end-point based approaches. The solution for interdestination media

  13. Comparison of Synchronization in Small World and Random Networks

    Science.gov (United States)

    Bernard, Tess; Miller, Bruce

    2008-10-01

    There are many models that simulate neuron firing in the brain. These range from the basic integrate-and-fire method to the complex Hodgkin-Huxley model. Eugene Izhikevich (2003) employed the principles of nonlinear dynamics, specifically bifurcation theory, to develop a model that is both simple and powerful, which can be described as an integrate-and-reset model. By changing only a few parameters, this model can simulate all the known types of cortical neuron firing patterns. Using it, we studied the properties of two different types of neural networks. In the first, originally used by Izhikevich, the synaptic connection strengths between the neurons are determined randomly, and each neuron is connected to all of the other neurons in the network. The second is a small world network modeled after the one employed by Alex Roxin, et al. (2004), but expanded to include inhibition. This geometry is an idealized representation of the nervous system. In our investigation we compared the onset of synchronization in each network, as well as its stability in the presence of external currents. We also considered the relevance of these results to real world phenomena such as seizures.

  14. Synchronization in Complex Oscillator Networks and Smart Grids

    Energy Technology Data Exchange (ETDEWEB)

    Dorfler, Florian [Los Alamos National Laboratory; Chertkov, Michael [Los Alamos National Laboratory; Bullo, Francesco [Center for Control, Dynamical Systems and Computation, University of California at Santa Babara, Santa Barbara CA

    2012-07-24

    The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A coupled oscillator network is characterized by a population of heterogeneous oscillators and a graph describing the interaction among them. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here we present a novel, concise, and closed-form condition for synchronization of the fully nonlinear, non-equilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters, or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters, they are statistically correct for almost all networks, and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks such as electric power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex networks scenarios and in smart grid applications.

  15. Synchronization in complex oscillator networks and smart grids.

    Science.gov (United States)

    Dörfler, Florian; Chertkov, Michael; Bullo, Francesco

    2013-02-05

    The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A widely adopted model of a coupled oscillator network is characterized by a population of heterogeneous phase oscillators, a graph describing the interaction among them, and diffusive and sinusoidal coupling. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here, we present a unique, concise, and closed-form condition for synchronization of the fully nonlinear, nonequilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters; they are statistically correct for almost all networks; and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks, such as electrical power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex network scenarios and in smart grid applications.

  16. Nanotomography of brain networks

    Science.gov (United States)

    Saiga, Rino; Mizutani, Ryuta; Takekoshi, Susumu; Osawa, Motoki; Arai, Makoto; Takeuchi, Akihisa; Uesugi, Kentaro; Terada, Yasuko; Suzuki, Yoshio; de Andrade, Vincent; de Carlo, Francesco

    The first step to understanding how the brain functions is to analyze its 3D network. The brain network consists of neurons having micrometer to nanometer sized structures. Therefore, 3D analysis of brain tissue at the relevant resolution is essential for elucidating brain's functional mechanisms. Here, we report 3D structures of human and fly brain networks revealed with synchrotron radiation nanotomography, or nano-CT. Neurons were stained with high-Z elements to visualize their structures with X-rays. Nano-CT experiments were then performed at the 32-ID beamline of the Advanced Photon Source, Argonne National Laboratory and at the BL37XU and BL47XU beamlines of SPring-8. Reconstructed 3D images illustrated precise structures of human neurons, including dendritic spines responsible for synaptic connections. The network of the fly brain hemisphere was traced to build a skeletonized wire model. An article reviewing our study appeared in MIT Technology Review. Movies of the obtained structures can be found in our YouTube channel.

  17. Multilayer Brain Networks

    Science.gov (United States)

    Vaiana, Michael; Muldoon, Sarah Feldt

    2018-01-01

    The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

  18. Synchronization of fractional fuzzy cellular neural networks with interactions

    Science.gov (United States)

    Ma, Weiyuan; Li, Changpin; Wu, Yujiang; Wu, Yongqing

    2017-10-01

    In this paper, we introduce fuzzy theory into the fractional cellular neural networks to dynamically enhance the coupling strength and propose a fractional fuzzy neural network model with interactions. Using the Lyapunov principle of fractional differential equations, we design the adaptive control schemes to realize the synchronization and obtain the synchronization criteria. Finally, we provide some numerical examples to show the effectiveness of our obtained results.

  19. Time-varying multiplex network: Intralayer and interlayer synchronization

    Science.gov (United States)

    Rakshit, Sarbendu; Majhi, Soumen; Bera, Bidesh K.; Sinha, Sudeshna; Ghosh, Dibakar

    2017-12-01

    A large class of engineered and natural systems, ranging from transportation networks to neuronal networks, are best represented by multiplex network architectures, namely a network composed of two or more different layers where the mutual interaction in each layer may differ from other layers. Here we consider a multiplex network where the intralayer coupling interactions are switched stochastically with a characteristic frequency. We explore the intralayer and interlayer synchronization of such a time-varying multiplex network. We find that the analytically derived necessary condition for intralayer and interlayer synchronization, obtained by the master stability function approach, is in excellent agreement with our numerical results. Interestingly, we clearly find that the higher frequency of switching links in the layers enhances both intralayer and interlayer synchrony, yielding larger windows of synchronization. Further, we quantify the resilience of synchronous states against random perturbations, using a global stability measure based on the concept of basin stability, and this reveals that intralayer coupling strength is most crucial for determining both intralayer and interlayer synchrony. Lastly, we investigate the robustness of interlayer synchronization against a progressive demultiplexing of the multiplex structure, and we find that for rapid switching of intralayer links, the interlayer synchronization persists even when a large number of interlayer nodes are disconnected.

  20. Extension of Pairwise Broadcast Clock Synchronization for Multicluster Sensor Networks

    Directory of Open Access Journals (Sweden)

    Bruce W. Suter

    2008-01-01

    Full Text Available Time synchronization is crucial for wireless sensor networks (WSNs in performing a number of fundamental operations such as data coordination, power management, security, and localization. The Pairwise Broadcast Synchronization (PBS protocol was recently proposed to minimize the number of timing messages required for global network synchronization, which enables the design of highly energy-efficient WSNs. However, PBS requires all nodes in the network to lie within the communication ranges of two leader nodes, a condition which might not be available in some applications. This paper proposes an extension of PBS to the more general class of sensor networks. Based on the hierarchical structure of the network, an energy-efficient pair selection algorithm is proposed to select the best pairwise synchronization sequence to reduce the overall energy consumption. It is shown that in a multicluster networking environment, PBS requires a far less number of timing messages than other well-known synchronization protocols and incurs no loss in synchronization accuracy. Moreover, the proposed scheme presents significant energy savings for densely deployed WSNs.

  1. Inter-layer synchronization in multiplex networks of identical layers

    Energy Technology Data Exchange (ETDEWEB)

    Sevilla-Escoboza, R. [Centro Universitario de los Lagos, Universidad de Guadalajara, Jalisco 47460 (Mexico); Sendiña-Nadal, I.; Leyva, I.; Buldú, J. M. [Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid (Spain); Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid (Spain); Gutiérrez, R. [School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD (United Kingdom); Boccaletti, S. [CNR-Institute of Complex Systems, Via Madonna del Piano, 10, 50019 Sesto Fiorentino, Florence (Italy); The Italian Embassy in Israel, 25 Hamered st., 68125 Tel Aviv (Israel)

    2016-06-15

    Inter-layer synchronization is a distinctive process of multiplex networks whereby each node in a given layer evolves synchronously with all its replicas in other layers, irrespective of whether or not it is synchronized with the other units of the same layer. We analytically derive the necessary conditions for the existence and stability of such a state, and verify numerically the analytical predictions in several cases where such a state emerges. We further inspect its robustness against a progressive de-multiplexing of the network, and provide experimental evidence by means of multiplexes of nonlinear electronic circuits affected by intrinsic noise and parameter mismatch.

  2. Small-world networks exhibit pronounced intermittent synchronization

    Science.gov (United States)

    Choudhary, Anshul; Mitra, Chiranjit; Kohar, Vivek; Sinha, Sudeshna; Kurths, Jürgen

    2017-11-01

    We report the phenomenon of temporally intermittently synchronized and desynchronized dynamics in Watts-Strogatz networks of chaotic Rössler oscillators. We consider topologies for which the master stability function (MSF) predicts stable synchronized behaviour, as the rewiring probability (p) is tuned from 0 to 1. MSF essentially utilizes the largest non-zero Lyapunov exponent transversal to the synchronization manifold in making stability considerations, thereby ignoring the other Lyapunov exponents. However, for an N-node networked dynamical system, we observe that the difference in its Lyapunov spectra (corresponding to the N - 1 directions transversal to the synchronization manifold) is crucial and serves as an indicator of the presence of intermittently synchronized behaviour. In addition to the linear stability-based (MSF) analysis, we further provide global stability estimate in terms of the fraction of state-space volume shared by the intermittently synchronized state, as p is varied from 0 to 1. This fraction becomes appreciably large in the small-world regime, which is surprising, since this limit has been otherwise considered optimal for synchronized dynamics. Finally, we characterize the nature of the observed intermittency and its dominance in state-space as network rewiring probability (p) is varied.

  3. Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay

    Science.gov (United States)

    Tang, Longkun; Wu, Xiaoqun; Lü, Jinhu; Lu, Jun-an

    2015-03-01

    Network synchronized regions play an extremely important role in network synchronization according to the master stability function framework. This paper focuses on network synchronous state stability via studying the effects of nodal dynamics, coupling delay, and coupling way on synchronized regions in Logistic map networks. Theoretical and numerical investigations show that (1) network synchronization is closely associated with its nodal dynamics. Particularly, the synchronized region bifurcation points through which the synchronized region switches from one type to another are in good agreement with those of the uncoupled node system, and chaotic nodal dynamics can greatly impede network synchronization. (2) The coupling delay generally impairs the synchronizability of Logistic map networks, which is also dominated by the parity of delay for some nodal parameters. (3) A simple nonlinear coupling facilitates network synchronization more than the linear one does. The results found in this paper will help to intensify our understanding for the synchronous state stability in discrete-time networks with coupling delay.

  4. Altered Synchronizations among Neural Networks in Geriatric Depression.

    Science.gov (United States)

    Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

    2015-01-01

    Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

  5. Synchronization of Lienard-Type Oscillators in Uniform Electrical Networks

    Energy Technology Data Exchange (ETDEWEB)

    Sinha, Mohit; Dorfler, Florian; Johnson, Brian B.; Dhople, Sairaj V.

    2016-08-01

    This paper presents a condition for global asymptotic synchronization of Lienard-type nonlinear oscillators in uniform LTI electrical networks with series R-L circuits modeling interconnections. By uniform electrical networks, we mean that the per-unit-length impedances are identical for the interconnecting lines. We derive conditions for global asymptotic synchronization for a particular feedback architecture where the derivative of the oscillator output current supplements the innate current feedback induced by simply interconnecting the oscillator to the network. Our proof leverages a coordinate transformation to a set of differential coordinates that emphasizes signal differences and the particular form of feedback permits the formulation of a quadratic Lyapunov function for this class of networks. This approach is particularly interesting since synchronization conditions are difficult to obtain by means of quadratic Lyapunov functions when only current feedback is used and for networks composed of series R-L circuits. Our synchronization condition depends on the algebraic connectivity of the underlying network, and reiterates the conventional wisdom from Lyapunov- and passivity-based arguments that strong coupling is required to ensure synchronization.

  6. Synchronized stability in a reaction–diffusion neural network model

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Ling; Zhao, Hongyong, E-mail: hongyongz@126.com

    2014-11-14

    The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability.

  7. Pinning Synchronization of Linear Complex Coupling Synchronous Generators Network of Hydroelectric Generating Set

    Directory of Open Access Journals (Sweden)

    Xuefei Wu

    2014-01-01

    Full Text Available A novel linear complex system for hydroturbine-generator sets in multimachine power systems is suggested in this paper and synchronization of the power-grid networks is studied. The advanced graph theory and stability theory are combined to solve the problem. Here we derive a sufficient condition under which the synchronous state of power-grid networks is stable in disturbance attenuation. Finally, numerical simulations are provided to illustrate the effectiveness of the results by the IEEE 39 bus system.

  8. Emergence of local synchronization in neuronal networks with adaptive couplings.

    Directory of Open Access Journals (Sweden)

    Shilpa Chakravartula

    Full Text Available Local synchronization, both prolonged and transient, of oscillatory neuronal behavior in cortical networks plays a fundamental role in many aspects of perception and cognition. Here we study networks of Hindmarsh-Rose neurons with a new type of adaptive coupling, and show that these networks naturally produce both permanent and transient synchronization of local clusters of neurons. These deterministic systems exhibit complex dynamics with 1/fη power spectra, which appears to be a consequence of a novel form of self-organized criticality.

  9. Emergence of local synchronization in neuronal networks with adaptive couplings.

    Science.gov (United States)

    Chakravartula, Shilpa; Indic, Premananda; Sundaram, Bala; Killingback, Timothy

    2017-01-01

    Local synchronization, both prolonged and transient, of oscillatory neuronal behavior in cortical networks plays a fundamental role in many aspects of perception and cognition. Here we study networks of Hindmarsh-Rose neurons with a new type of adaptive coupling, and show that these networks naturally produce both permanent and transient synchronization of local clusters of neurons. These deterministic systems exhibit complex dynamics with 1/fη power spectra, which appears to be a consequence of a novel form of self-organized criticality.

  10. Time Synchronization and Distribution Mechanisms for Space Networks

    Science.gov (United States)

    Woo, Simon S.; Gao, Jay L.; Clare, Loren P.; Mills, David L.

    2011-01-01

    This work discusses research on the problems of synchronizing and distributing time information between spacecraft based on the Network Time Protocol (NTP), where NTP is a standard time synchronization protocol widely used in the terrestrial network. The Proximity-1 Space Link Interleaved Time Synchronization (PITS) Protocol was designed and developed for synchronizing spacecraft that are in proximity where proximity is less than 100,000 km distant. A particular application is synchronization between a Mars orbiter and rover. Lunar scenarios as well as outer-planet deep space mother-ship-probe missions may also apply. Spacecraft with more accurate time information functions as a time-server, and the other spacecraft functions as a time-client. PITS can be easily integrated and adaptable to the CCSDS Proximity-1 Space Link Protocol with minor modifications. In particular, PITS can take advantage of the timestamping strategy that underlying link layer functionality provides for accurate time offset calculation. The PITS algorithm achieves time synchronization with eight consecutive space network time packet exchanges between two spacecraft. PITS can detect and avoid possible errors from receiving duplicate and out-of-order packets by comparing with the current state variables and timestamps. Further, PITS is able to detect error events and autonomously recover from unexpected events that can possibly occur during the time synchronization and distribution process. This capability achieves an additional level of protocol protection on top of CRC or Error Correction Codes. PITS is a lightweight and efficient protocol, eliminating the needs for explicit frame sequence number and long buffer storage. The PITS protocol is capable of providing time synchronization and distribution services for a more general domain where multiple entities need to achieve time synchronization using a single point-to-point link.

  11. Brain rhythms reveal a hierarchical network organization.

    Directory of Open Access Journals (Sweden)

    G Karl Steinke

    2011-10-01

    Full Text Available Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or "virtual brains", whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic, in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states

  12. An Inter-Networking Mechanism with Stepwise Synchronization for Wireless Sensor Networks

    OpenAIRE

    Masayuki Murata; Naoki Wakamiya; Hiroshi Yamamoto

    2011-01-01

    To realize the ambient information society, multiple wireless networks deployed in the region and devices carried by users are required to cooperate with each other. Since duty cycles and operational frequencies are different among networks, we need a mechanism to allow networks to efficiently exchange messages. For this purpose, we propose a novel inter-networking mechanism where two networks are synchronized with each other in a moderate manner, which we call stepwise synchronization. With ...

  13. Facilitated synchronization of complex networks through a discontinuous coupling strategy

    Science.gov (United States)

    Chen, L.; Qiu, C.; Huang, H. B.; Qi, G. X.; Wang, H. J.

    2010-08-01

    Synchronization stability in complex networks is a topic of theoretical interest and practical importance. Increasing effort has been devoted to the enhancement of synchronizability of networks, or more specifically, the design of synchronizable networks. However, most previous attempts turn the coupling weight/gradient or change the topological interactions, which sometimes is not manageable. In this paper, by adopting a simple kind of discontinuous coupling strategy: the uniform on-off coupling scheme, with on-off period being comparable to the timescale of node dynamics, the problem is solved within the framework of the master stability function. The results show that, this strategy can greatly increase the stable region of synchronization, which means the size of synchronizable networks can be much larger than the traditional case, without any changes of their connections. Furthermore, the synchronization speed can be accelerated considerably, which is even higher than the previous optimal case. The mechanism of the facilitation is revealed and shows that the continuous coupling in fact is one of the worst choices for synchronization in the view of discontinuous coupling strategy. The coupling cost required for synchronization is also examined, which is approximately the same as the continuous coupling.

  14. An Efficient Synchronization Method for Wireless Networks

    Science.gov (United States)

    2013-06-01

    group-wise synchronization which is more e cient than rsync, is possible. This paper describes Dandelion , an algorithm that builds on the ideas of the...which is more efficient than rsync, is possible. This paper describes Dandelion , an algorithm that builds on the ideas of the rsync algorithm to...methods analyzed in this paper are compared using this metric. To meet this goal, this paper defines an epidemic-like algorithm called Dandelion that is

  15. Network algebra for synchronous and asynchronous dataflow

    NARCIS (Netherlands)

    Bergstra, J.A.; Stefanescu, G.

    1994-01-01

    Network algebra (NA) is proposed as a uniform algebraic framework for the description (and analysis) of dataflow networks. The core of this algebraic setting is provided by an equational theory called Basic Network Algebra (BNA). It constitutes a selection of primitives and identities from the

  16. Enhancing Time Synchronization Support in Wireless Sensor Networks.

    Science.gov (United States)

    Tavares Bruscato, Leandro; Heimfarth, Tales; Pignaton de Freitas, Edison

    2017-12-20

    With the emerging Internet of Things (IoT) technology becoming reality, a number of applications are being proposed. Several of these applications are highly dependent on wireless sensor networks (WSN) to acquire data from the surrounding environment. In order to be really useful for most of applications, the acquired data must be coherent in terms of the time in which they are acquired, which implies that the entire sensor network presents a certain level of time synchronization. Moreover, to efficiently exchange and forward data, many communication protocols used in WSN rely also on time synchronization among the sensor nodes. Observing the importance in complying with this need for time synchronization, this work focuses on the second synchronization problem, proposing, implementing and testing a time synchronization service for low-power WSN using low frequency real-time clocks in each node. To implement this service, three algorithms based on different strategies are proposed: one based on an auto-correction approach, the second based on a prediction mechanism, while the third uses an analytical correction mechanism. Their goal is the same, i.e., to make the clocks of the sensor nodes converge as quickly as possible and then to keep them most similar as possible. This goal comes along with the requirement to keep low energy consumption. Differently from other works in the literature, the proposal here is independent of any specific protocol, i.e., it may be adapted to be used in different protocols. Moreover, it explores the minimum number of synchronization messages by means of a smart clock update strategy, allowing the trade-off between the desired level of synchronization and the associated energy consumption. Experimental results, which includes data acquired from simulations and testbed deployments, provide evidence of the success in meeting this goal, as well as providing means to compare these three approaches considering the best synchronization

  17. FROM BRAIN DRAIN TO BRAIN NETWORKING

    Directory of Open Access Journals (Sweden)

    Irina BONCEA

    2015-06-01

    Full Text Available Scientific networking is the most accessible way a country can turn the brain drain into brain gain. Diaspora’s members offer valuable information, advice or financial support from the destination country, without being necessary to return. This article aims to investigate Romania’s potential of turning brain drain into brain networking, using evidence from the medical sector. The main factors influencing the collaboration with the country of origin are investigated. The conclusions suggest that Romania could benefit from the diaspora option, through an active implication at institutional level and the implementation of a strategy in this area.

  18. Training brain networks and states.

    Science.gov (United States)

    Tang, Yi-Yuan; Posner, Michael I

    2014-07-01

    Brain training refers to practices that alter the brain in a way that improves cognition, and performance in domains beyond those involved in the training. We argue that brain training includes network training through repetitive practice that exercises specific brain networks and state training, which changes the brain state in a way that influences many networks. This opinion article considers two widely used methods - working memory training (WMT) and meditation training (MT) - to demonstrate the similarities and differences between network and state training. These two forms of training involve different areas of the brain and different forms of generalization. We propose a distinction between network and state training methods to improve understanding of the most effective brain training. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Generalized Mutual Synchronization between Two Controlled Interdependent Networks

    Directory of Open Access Journals (Sweden)

    Quan Xu

    2014-01-01

    Full Text Available This paper mainly focuses on the generalized mutual synchronization between two controlled interdependent networks. First, we propose the general model of controlled interdependent networks A and B with time-varying internetwork delays coupling. Then, by constructing Lyapunov functions and utilizing adaptive control technique, some sufficient conditions are established to ensure that the mutual synchronization errors between the state variables of networks A and B can asymptotically converge to zero. Finally, two numerical examples are given to illustrate the effectiveness of the theoretical results and to explore potential application in future smart grid. The simulation results also show how interdependent topologies and internetwork coupling delays influence the mutual synchronizability, which help to design interdependent networks with optimal mutual synchronizability.

  20. Synergistic effect of repulsive inhibition in synchronization of excitatory networks

    Science.gov (United States)

    Belykh, Igor; Reimbayev, Reimbay; Zhao, Kun

    2015-06-01

    We show that the addition of pairwise repulsive inhibition to excitatory networks of bursting neurons induces synchrony, in contrast to one's expectations. Through stability analysis, we reveal the mechanism underlying this purely synergistic phenomenon and demonstrate that it originates from the transition between different types of bursting, caused by excitatory-inhibitory synaptic coupling. This effect is generic and observed in different models of bursting neurons and fast synaptic interactions. We also find a universal scaling law for the synchronization stability condition for large networks in terms of the number of excitatory and inhibitory inputs each neuron receives, regardless of the network size and topology. This general law is in sharp contrast with linearly coupled networks with positive (attractive) and negative (repulsive) coupling where the placement and structure of negative connections heavily affect synchronization.

  1. Synchronization-optimized networks for coupled nearly identical ...

    Indian Academy of Sciences (India)

    173–182. Synchronization-optimized networks for coupled nearly identical oscillators and their structural analysis. SUMAN ACHARYYA1,∗ and R E AMRITKAR1,2. 1Theoretical Physics Division, Physical Research Laboratory, Ahmedabad 380 009, India. 2Institute of Infrastructure Technology Research and Management, ...

  2. Clock Synchronization in Wireless Sensor Networks: Analysis and Design of Error Precision Based on Lossy Networked Control Perspective

    Directory of Open Access Journals (Sweden)

    Wang Ting

    2015-01-01

    Full Text Available Motivated by the importance of the clock synchronization in wireless sensor networks (WSNs, due to the packet loss, the synchronization error variance is a random variable and may exceed the designed boundary of the synchronization variance. Based on the clock synchronization state space model, this paper establishes the model of synchronization error variance analysis and design issues. In the analysis issue, assuming sensor nodes exchange clock information in the network with packet loss, we find a minimum clock information packet arrival rate in order to guarantee the synchronization precision at synchronization node. In the design issue, assuming sensor node freely schedules whether to send the clock information, we look for an optimal clock information exchange rate between synchronization node and reference node which offers the optimal tradeoff between energy consumption and synchronization precision at synchronization node. Finally, simulations further verify the validity of clock synchronization analysis and design from the perspective of synchronization error variance.

  3. Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Maharatna, Koushik; Pan, Indranil; Kuyucu, Doga

    2015-09-01

    Degree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3-5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta (β) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating toward different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modeling the functional brain connectivity through the synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration

  4. Event-based cluster synchronization of coupled genetic regulatory networks

    Science.gov (United States)

    Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

    2017-09-01

    In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

  5. Robust adaptive synchronization of general dynamical networks ...

    Indian Academy of Sciences (India)

    1School of Information Science & Engineering, Northeastern University, Shenyang,. Liaoning, 110819, People's ... Introduction. Complex networks exist extensively in ecosystems, power grids, food webs and in many other spheres in our daily lives. Over the course of the past 30 years, technological revolu- tions of complex ...

  6. Synchronization of oscillators in complex networks

    Indian Academy of Sciences (India)

    This being the most conservative assumption will cover the largest class of oscillators including those which have multiple, disjoint α regions of stability as can ..... intake of food and energy which will be fruitful only if the new networks are a great improvement and provide the organisms some evolutionary advantages. In.

  7. Behavioral synchronization induced by epidemic spread in complex networks

    Science.gov (United States)

    Sun, Mengfeng; Lou, Yijun; Duan, Jinqiao; Fu, Xinchu

    2017-06-01

    During the spread of an epidemic, individuals in realistic networks may exhibit collective behaviors. In order to characterize this kind of phenomenon and explore the correlation between collective behaviors and epidemic spread, in this paper, we construct several mathematical models (including without delay, with a coupling delay, and with double delays) of epidemic synchronization by applying the adaptive feedback motivated by real observations. By using Lyapunov function methods, we obtain the conditions for local and global stability of these epidemic synchronization models. Then, we illustrate that quenched mean-field theory is more accurate than heterogeneous mean-field theory in the prediction of epidemic synchronization. Finally, some numerical simulations are performed to complement our theoretical results, which also reveal some unexpected phenomena, for example, the coupling delay and epidemic delay influence the speed of epidemic synchronization. This work makes further exploration on the relationship between epidemic dynamics and synchronization dynamics, in the hope of being helpful to the study of other dynamical phenomena in the process of epidemic spread.

  8. From baseline to epileptiform activity: A path to synchronized rhythmicity in large-scale neural networks

    Science.gov (United States)

    Shusterman, Vladimir; Troy, William C.

    2008-06-01

    In large-scale neural networks in the brain the emergence of global behavioral patterns, manifested by electroencephalographic activity, is driven by the self-organization of local neuronal groups into synchronously functioning ensembles. However, the laws governing such macrobehavior and its disturbances, in particular epileptic seizures, are poorly understood. Here we use a mean-field population network model to describe a state of baseline physiological activity and the transition from the baseline state to rhythmic epileptiform activity. We describe principles which explain how this rhythmic activity arises in the form of spatially uniform self-sustained synchronous oscillations. In addition, we show how the rate of migration of the leading edge of the synchronous oscillations can be theoretically predicted, and compare the accuracy of this prediction with that measured experimentally using multichannel electrocorticographic recordings obtained from a human subject experiencing epileptic seizures. The comparison shows that the experimentally measured rate of migration of the leading edge of synchronous oscillations is within the theoretically predicted range of values. Computer simulations have been performed to investigate the interactions between different regions of the brain and to show how organization in one spatial region can promote or inhibit organization in another. Our theoretical predictions are also consistent with the results of functional magnetic resonance imaging (fMRI), in particular with observations that lower-frequency electroencephalographic (EEG) rhythms entrain larger areas of the brain than higher-frequency rhythms. These findings advance the understanding of functional behavior of interconnected populations and might have implications for the analysis of diverse classes of networks.

  9. An Inter-Networking Mechanism with Stepwise Synchronization for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Masayuki Murata

    2011-08-01

    Full Text Available To realize the ambient information society, multiple wireless networks deployed in the region and devices carried by users are required to cooperate with each other. Since duty cycles and operational frequencies are different among networks, we need a mechanism to allow networks to efficiently exchange messages. For this purpose, we propose a novel inter-networking mechanism where two networks are synchronized with each other in a moderate manner, which we call stepwise synchronization. With our proposal, to bridge the gap between intrinsic operational frequencies, nodes near the border of networks adjust their operational frequencies in a stepwise fashion based on the pulse-coupled oscillator model as a fundamental theory of synchronization. Through simulation experiments, we show that the communication delay and the energy consumption of border nodes are reduced, which enables wireless sensor networks to communicate longer with each other.

  10. An inter-networking mechanism with stepwise synchronization for wireless sensor networks.

    Science.gov (United States)

    Yamamoto, Hiroshi; Wakamiya, Naoki; Murata, Masayuki

    2011-01-01

    To realize the ambient information society, multiple wireless networks deployed in the region and devices carried by users are required to cooperate with each other. Since duty cycles and operational frequencies are different among networks, we need a mechanism to allow networks to efficiently exchange messages. For this purpose, we propose a novel inter-networking mechanism where two networks are synchronized with each other in a moderate manner, which we call stepwise synchronization. With our proposal, to bridge the gap between intrinsic operational frequencies, nodes near the border of networks adjust their operational frequencies in a stepwise fashion based on the pulse-coupled oscillator model as a fundamental theory of synchronization. Through simulation experiments, we show that the communication delay and the energy consumption of border nodes are reduced, which enables wireless sensor networks to communicate longer with each other.

  11. Adaptive Synchronization of Fractional Neural Networks with Unknown Parameters and Time Delays

    Directory of Open Access Journals (Sweden)

    Weiyuan Ma

    2014-12-01

    Full Text Available In this paper, the parameters identification and synchronization problem of fractional-order neural networks with time delays are investigated. Based on some analytical techniques and an adaptive control method, a simple adaptive synchronization controller and parameter update laws are designed to synchronize two uncertain complex networks with time delays. Besides, the system parameters in the uncertain network can be identified in the process of synchronization. To demonstrate the validity of the proposed method, several illustrative examples are presented.

  12. SynUTC - high precision time synchronization over ethernet networks

    CERN Document Server

    Höller, R; Horauer, M; Kerö, N; Schmid, U; Schossmaier, K

    2002-01-01

    This article describes our SynUTC (Synchronized Universal Time Coordinated) technology, which enables high-accuracy distribution of GPS time and time synchronization of network nodes connected via standard Ethernet LANs. By means of exchanging data packets in conjunction with moderate hardware support at nodes and switches, an overall worst-case accuracy in the range of some 100 ns can be achieved, with negligible communication overhead. Our technology thus improves the 1 ms-range accuracy achievable by conventional, software-based approaches like NTP by 4 orders of magnitude. Applications can use the high-accuracy global time provided by SynUTC for event timestamping and event generation both at hardware and software level. SynUTC is based upon inserting highly accurate time information into dedicated data packets at the media-independent interface (MII) between the physical layer transceiver and the network controller upon packet transmission and reception, respectively. As a consequence, every node has acc...

  13. Brain networks for integrative rhythm formation.

    Directory of Open Access Journals (Sweden)

    Michael H Thaut

    2008-05-01

    Full Text Available Performance of externally paced rhythmic movements requires brain and behavioral integration of sensory stimuli with motor commands. The underlying brain mechanisms to elaborate beat-synchronized rhythm and polyrhythms that musicians readily perform may differ. Given known roles in perceiving time and repetitive movements, we hypothesized that basal ganglia and cerebellar structures would have greater activation for polyrhythms than for on-the-beat rhythms.Using functional MRI methods, we investigated brain networks for performing rhythmic movements paced by auditory cues. Musically trained participants performed rhythmic movements at 2 and 3 Hz either at a 1:1 on-the-beat or with a 3:2 or a 2:3 stimulus-movement structure. Due to their prior musical experience, participants performed the 3:2 or 2:3 rhythmic movements automatically. Both the isorhythmic 1:1 and the polyrhythmic 3:2 or 2:3 movements yielded the expected activation in contralateral primary motor cortex and related motor areas and ipsilateral cerebellum. Direct comparison of functional MRI signals obtained during 3:2 or 2:3 and on-the-beat rhythms indicated activation differences bilaterally in the supplementary motor area, ipsilaterally in the supramarginal gyrus and caudate-putamen and contralaterally in the cerebellum.The activated brain areas suggest the existence of an interconnected brain network specific for complex sensory-motor rhythmic integration that might have specificity for elaboration of musical abilities.

  14. Stability and synchronization control of stochastic neural networks

    CERN Document Server

    Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing

    2016-01-01

    This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

  15. Computer network time synchronization the network time protocol on earth and in space

    CERN Document Server

    Mills, David L

    2010-01-01

    Carefully coordinated, reliable, and accurate time synchronization is vital to a wide spectrum of fields-from air and ground traffic control, to buying and selling goods and services, to TV network programming. Ill-gotten time could even lead to the unimaginable and cause DNS caches to expire, leaving the entire Internet to implode on the root servers.Written by the original developer of the Network Time Protocol (NTP), Computer Network Time Synchronization: The Network Time Protocol on Earth and in Space, Second Edition addresses the technological infrastructure of time dissemination, distrib

  16. Quantum synchronization and quantum state sharing in an irregular complex network.

    Science.gov (United States)

    Li, Wenlin; Li, Chong; Song, Heshan

    2017-02-01

    We investigate the quantum synchronization phenomenon of the complex network constituted by coupled optomechanical systems and prove that the unknown identical quantum states can be shared or distributed in the quantum network even though the topology is varying. Considering a channel constructed by quantum correlation, we show that quantum synchronization can sustain and maintain high levels in Markovian dissipation for a long time. We also analyze the state-sharing process between two typical complex networks, and the results predict that linked nodes can be directly synchronized, but the whole network will be synchronized only if some specific synchronization conditions are satisfied. Furthermore, we give the synchronization conditions analytically through analyzing network dynamics. This proposal paves the way for studying multi-interaction synchronization and achieving effective quantum information processing in a complex network.

  17. Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.

    Science.gov (United States)

    Li, Ning; Cao, Jinde

    2016-03-01

    This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.

  18. Functional network interactions during sensorimotor synchronization in musicians and non-musicians.

    Science.gov (United States)

    Krause, Vanessa; Schnitzler, Alfons; Pollok, Bettina

    2010-08-01

    Precise timing as determined by sensorimotor synchronization is crucial for a wide variety of activities. Although it is well-established that musicians show superior timing as compared to non-musicians, the neurophysiological foundations - in particular the underlying functional brain network - remain to be characterized. To this end, drummers, professional pianists and non-musicians performed an auditory synchronization task while neuromagnetic activity was measured using a 122-channel whole-head magnetoencephalography (MEG) system. The underlying functional brain network was determined using the beamformer approach Dynamic Imaging of Coherent Sources (DICS). Behaviorally, drummers performed less variably than non-musicians. Neuromagnetic analysis revealed a cerebello-thalamo-cortical network in all subjects comprising bilateral primary sensorimotor cortices (S1/M1), contralateral supplementary motor and premotor regions (SMA and PMC), thalamus, posterior parietal cortex (PPC), ipsilateral cerebellum and bilateral auditory cortices. Stronger PMC-thalamus and PPC-thalamus interactions at alpha and beta frequencies were evident in drummers as compared to non-musicians. In professional pianists stronger PMC-thalamus interaction as compared to non-musicians at beta frequency occurred. The present data suggest that precise timing is associated with increased functional interaction within a PMC-thalamus-PPC network. The PMC-thalamus connectivity at beta frequency might be related to musical expertise, whereas the PPC-thalamus interaction might have specific relevance for precise timing. Copyright 2010 Elsevier Inc. All rights reserved.

  19. Structure identification and adaptive synchronization of uncertain general complex dynamical networks

    Energy Technology Data Exchange (ETDEWEB)

    Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

    2009-12-28

    This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

  20. Synchronization

    Indian Academy of Sciences (India)

    Synchronization, in simple terms, is the adjustment of rhythrns of two mutually interacting systems, such as a pair of coupled oscillators. Synchronization was discov- ered in the seventeenth century by Christiaan Huygens who observed it when working with clocks (see Box 1). He saw that two clocks (pendulums) suspended ...

  1. Synchronous Ethernet- Considerations and Implementation of the Packet Network Management Scheme

    Science.gov (United States)

    Gundale, A. S.; Aradhye, Ashwini

    2010-11-01

    Packet technologies were designed to work in asynchronous mode, where the oscillators in the equipment are free running. Although this allows the underlying infrastructure to operate, many applications exist that require frequency synchronization. Also, the ability to distribute synchronization from center to edge of network declines as infrastructure evolves toward a packet-based architecture. Synchronous Ethernet (SyncE) is a key development of the evolution of Ethernet into a carrier grade technology suitable for the WAN environment where frequency synchronization is required. The time of the day distribution in synchronized network at the physical layer enables many useful propositions in packet handling policies and other network management aspects.

  2. Shapley ratings in brain networks

    Directory of Open Access Journals (Sweden)

    Rolf Kötter

    2007-11-01

    Full Text Available Recent applications of network theory to brain networks as well as the expanding empirical databases of brain architecture spawn an interest in novel techniques for analyzing connectivity patterns in the brain. Treating individual brain structures as nodes in a directed graph model permits the application of graph theoretical concepts to the analysis of these structures within their large-scale connectivity networks. In this paper, we explore the application of concepts from graph and game theory toward this end. Specifically, we utilize the Shapley value principle, which assigns a rank to players in a coalition based upon their individual contributions to the collective profit of that coalition, to assess the contributions of individual brain structures to the graph derived from the global connectivity network. We report Shapley values for variations of a prefrontal network, as well as for a visual cortical network, which had both been extensively investigated previously. This analysis highlights particular nodes as strong or weak contributors to global connectivity. To understand the nature of their contribution, we compare the Shapley values obtained from these networks and appropriate controls to other previously described nodal measures of structural connectivity. We find a strong correlation between Shapley values and both betweenness centrality and connection density. Moreover, a stepwise multiple linear regression analysis indicates that approximately 79% of the variance in Shapley values obtained from random networks can be explained by betweenness centrality alone. Finally, we investigate the effects of local lesions on the Shapley ratings, showing that the present networks have an immense structural resistance to degradation. We discuss our results highlighting the use of such measures for characterizing the organization and functional role of brain networks.

  3. Amplification of asynchronous inhibition-mediated synchronization by feedback in recurrent networks.

    Directory of Open Access Journals (Sweden)

    Sashi Marella

    2010-02-01

    Full Text Available Synchronization of 30-80 Hz oscillatory activity of the principle neurons in the olfactory bulb (mitral cells is believed to be important for odor discrimination. Previous theoretical studies of these fast rhythms in other brain areas have proposed that principle neuron synchrony can be mediated by short-latency, rapidly decaying inhibition. This phasic inhibition provides a narrow time window for the principle neurons to fire, thus promoting synchrony. However, in the olfactory bulb, the inhibitory granule cells produce long lasting, small amplitude, asynchronous and aperiodic inhibitory input and thus the narrow time window that is required to synchronize spiking does not exist. Instead, it has been suggested that correlated output of the granule cells could serve to synchronize uncoupled mitral cells through a mechanism called "stochastic synchronization", wherein the synchronization arises through correlation of inputs to two neural oscillators. Almost all work on synchrony due to correlations presumes that the correlation is imposed and fixed. Building on theory and experiments that we and others have developed, we show that increased synchrony in the mitral cells could produce an increase in granule cell activity for those granule cells that share a synchronous group of mitral cells. Common granule cell input increases the input correlation to the mitral cells and hence their synchrony by providing a positive feedback loop in correlation. Thus we demonstrate the emergence and temporal evolution of input correlation in recurrent networks with feedback. We explore several theoretical models of this idea, ranging from spiking models to an analytically tractable model.

  4. Mirror node correlations tuning synchronization in multiplex networks

    Science.gov (United States)

    Kumar, Anil; Baptista, Murilo S.; Zaikin, Alexey; Jalan, Sarika

    2017-12-01

    We show that the degree-degree correlations have a major impact on global synchronizability (GS) of multiplex networks, enabling the specification of synchronizability by only changing the degree-degree correlations of the mirror nodes while maintaining the connection architecture of the individual layer unaltered. If individual layers have nodes that are mildly correlated, the multiplex network is best synchronizable when the mirror degrees are strongly negatively correlated. If individual layers have nodes with strong degree-degree correlations, mild correlations among the degrees of mirror nodes are the best strategy for the optimization of GS. Global synchronization also depend on the density of connections, a phenomenon not observed in a single layer network. The results are crucial to understand, predict, and specify behavior of systems having multiple types of connections among the interacting units.

  5. Assortative and modular networks are shaped by adaptive synchronization processes.

    Science.gov (United States)

    Avalos-Gaytán, Vanesa; Almendral, Juan A; Papo, David; Schaeffer, Satu Elisa; Boccaletti, Stefano

    2012-07-01

    Modular organization and degree-degree correlations are ubiquitous in the connectivity structure of biological, technological, and social interacting systems. So far most studies have concentrated on unveiling both features in real world networks, but a model that succeeds in generating them simultaneously is needed. We consider a network of interacting phase oscillators, and an adaptation mechanism for the coupling that promotes the connection strengths between those elements that are dynamically correlated. We show that, under these circumstances, the dynamical organization of the oscillators shapes the topology of the graph in such a way that modularity and assortativity features emerge spontaneously and simultaneously. In turn, we prove that such an emergent structure is associated with an asymptotic arrangement of the collective dynamical state of the network into cluster synchronization.

  6. Decentralized Network-level Synchronization in Mobile Ad Hoc Networks

    NARCIS (Netherlands)

    Voulgaris, Spyros; Dobson, Matthew; van Steen, Martinus Richardus

    Energy is the scarcest resource in ad hoc wireless networks, particularly in wireless sensor networks requiring a long lifetime. Intermittently switching the radio on and off is widely adopted as the most effective way to keep energy consumption low. This, however, prevents the very goal of

  7. Pinning control of complex networked systems synchronization, consensus and flocking of networked systems via pinning

    CERN Document Server

    Su, Housheng

    2013-01-01

    Synchronization, consensus and flocking are ubiquitous requirements in networked systems. Pinning Control of Complex Networked Systems investigates these requirements by using the pinning control strategy, which aims to control the whole dynamical network with huge numbers of nodes by imposing controllers for only a fraction of the nodes. As the direct control of every node in a dynamical network with huge numbers of nodes might be impossible or unnecessary, it’s then very important to use the pinning control strategy for the synchronization of complex dynamical networks. The research on pinning control strategy in consensus and flocking of multi-agent systems can not only help us to better understand the mechanisms of natural collective phenomena, but also benefit applications in mobile sensor/robot networks. This book offers a valuable resource for researchers and engineers working in the fields of control theory and control engineering.   Housheng Su is an Associate Professor at the Department of Contro...

  8. Finite-Time Bounded Synchronization of the Growing Complex Network with Nondelayed and Delayed Coupling

    Directory of Open Access Journals (Sweden)

    Yuhua Xu

    2017-01-01

    Full Text Available The objective of this paper is to discuss finite-time bounded synchronization for a class of the growing complex network with nondelayed and delayed coupling. In order to realize finite-time synchronization of complex networks, a new finite-time stable theory is proposed; effective criteria are developed to realize synchronization of the growing complex dynamical network in finite time. Moreover, the error of two growing networks is bounded simultaneously in the process of finite-time synchronization. Finally, some numerical examples are provided to verify the theoretical results established in this paper.

  9. Subthalamic stimulation modulates cortical motor network activity and synchronization in Parkinson's disease.

    Science.gov (United States)

    Weiss, Daniel; Klotz, Rosa; Govindan, Rathinaswamy B; Scholten, Marlieke; Naros, Georgios; Ramos-Murguialday, Ander; Bunjes, Friedemann; Meisner, Christoph; Plewnia, Christian; Krüger, Rejko; Gharabaghi, Alireza

    2015-03-01

    -cortical coherence in the beta band was significantly attenuated over the bilateral sensorimotor areas. Similarly, the global cortico-cortical phase synchronization was attenuated, and the topographic differentiation revealed stronger desynchronization over the (ipsilateral) right-hemispheric prefrontal, premotor and sensorimotor areas compared to 'stimulation off'. We further demonstrated that the cortico-cortical phase synchronization was largely dominated by genuine neuronal coupling. The clinical improvement with 'stimulation on' compared to 'stimulation off' could be predicted from this cortical decoupling with multiple regressions, and the reduction of synchronization over the right prefrontal area showed a linear univariate correlation with clinical improvement. Our study demonstrates wide-spread activity and synchronization modulations of the cortical motor network, and highlights subthalamic stimulation as a network-modulating therapy. Accordingly, subthalamic stimulation may release bilateral cortical computational resources by facilitating movement-related desynchronization. Moreover, the subthalamic nucleus is critical to balance inhibitory and facilitatory cortical players within the motor program. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  10. Successive lag synchronization on dynamical networks with communication delay

    Science.gov (United States)

    Xin-Jian, Zhang; Ai-Ju, Wei; Ke-Zan, Li

    2016-03-01

    In this paper, successive lag synchronization (SLS) on a dynamical network with communication delay is investigated. In order to achieve SLS on the dynamical network with communication delay, we design linear feedback control and adaptive control, respectively. By using the Lyapunov function method, we obtain some sufficient conditions for global stability of SLS. To verify these results, some numerical examples are further presented. This work may find potential applications in consensus of multi-agent systems. Project supported by the National Natural Science Foundation of China (Grant No. 61004101), the Natural Science Foundation Program of Guangxi Province, China (Grant No. 2015GXNSFBB139002), the Graduate Innovation Project of Guilin University of Electronic Technology, China (Grant No. GDYCSZ201472), and the Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, China.

  11. Microscopic interactions lead to mutual synchronization in a network of networks

    Science.gov (United States)

    Hung, Yao-Chen

    2011-07-01

    This Letter proposes a stochastic coupling scheme to study the collective dynamics in a network that comprises random Boolean networks. Based on microscopic interactions, which are understood as the exchange of information among nodes, mutual synchronization can be achieved when the product of the assigning probability and influence probability exceeds a critical threshold. A mean field model is developed to approximate the dynamical behaviors of the original system. The effect of finite system size can be further mimicked by incorporating a noise term into the model. The dependence of the synchronization threshold on the degrees of connectivity and coupling configuration is analyzed.

  12. A proportional integral estimator-based clock synchronization protocol for wireless sensor networks.

    Science.gov (United States)

    Yang, Wenlun; Fu, Minyue

    2017-11-01

    Clock synchronization is an issue of vital importance in applications of WSNs. This paper proposes a proportional integral estimator-based protocol (EBP) to achieve clock synchronization for wireless sensor networks. As each local clock skew gradually drifts, synchronization accuracy will decline over time. Compared with existing consensus-based approaches, the proposed synchronization protocol improves synchronization accuracy under time-varying clock skews. Moreover, by restricting synchronization error of clock skew into a relative small quantity, it could reduce periodic re-synchronization frequencies. At last, a pseudo-synchronous implementation for skew compensation is introduced as synchronous protocol is unrealistic in practice. Numerical simulations are shown to illustrate the performance of the proposed protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Synchronization challenges in packet-based Cloud-RAN fronthaul for mobile networks

    DEFF Research Database (Denmark)

    Checko, Aleksandra; Juul, Anders Christian; Christiansen, Henrik Lehrmann

    2015-01-01

    In this paper, we look at reusing existing packet-based network (e.g. Ethernet) to possibly decrease deployment costs of fronthaul Cloud Radio Access Network (C-RAN) network and cost of Baseband Unit (BBU) resources. The challenge of this solution is that it requires mobile traffic (until now...... transmitted over synchronous protocols) to traverse the asynchronous Ethernet without losing synchronization. We analyze synchronization requirements of mobile networks and present an overview of solutions that fulfill them in traditional mobile networks. Then we elaborate on challenges that packet...... bridge the gap between Ethernet and mobile network domains creating a comprehensive architectural analysis....

  14. Synchronization of Switched Interval Networks and Applications to Chaotic Neural Networks

    Directory of Open Access Journals (Sweden)

    Jinde Cao

    2013-01-01

    Full Text Available This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchronization criteria are derived for switched interval networks under the arbitrary switching rule, which are easy to verify in practice. Moreover, as an application, the proposed scheme is then applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

  15. Chaotic synchronization of nearest-neighbor diffusive coupling Hindmarsh-Rose neural networks in noisy environments

    Energy Technology Data Exchange (ETDEWEB)

    Fang Xiaoling [Institute of Mechanobiology and Medical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, P.O. Box 888, 800 Dongchuan Road, Minhang, Shanghai 200240 (China); Yu Hongjie [Department of Engineering Mechanics, Shanghai Jiao Tong University, Shanghai 200240 (China); Jiang Zonglai [Institute of Mechanobiology and Medical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, P.O. Box 888, 800 Dongchuan Road, Minhang, Shanghai 200240 (China)], E-mail: zljiang@sjtu.edu.cn

    2009-03-15

    The chaotic synchronization of Hindmarsh-Rose neural networks linked by a nonlinear coupling function is discussed. The HR neural networks with nearest-neighbor diffusive coupling form are treated as numerical examples. By the construction of a special nonlinear-coupled term, the chaotic system is coupled symmetrically. For three and four neurons network, a certain region of coupling strength corresponding to full synchronization is given, and the effect of network structure and noise position are analyzed. For five and more neurons network, the full synchronization is very difficult to realize. All the results have been proved by the calculation of the maximum conditional Lyapunov exponent.

  16. A Practical Solution for Time Synchronization in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    COCA, E.

    2012-11-01

    Full Text Available Time synchronization in wireless sensor node networks is a hot topic. Many papers present various software algorithms and hardware solutions to keep accurate time information on mobile nodes. In terms of real life applications wireless sensor nodes are preferred in many domains, starting with simple room monitoring and finishing with pipeline surveillance projects. Positioning applications are far more restrictive on timekeeping accuracy, as for the velocity of nodes calculations precise time or time difference values are needed. The accuracy of time information on nodes has to be always correlated with the application requirements. In this paper, we present some considerations regarding time synchronization linked with specific needs for individual practical applications. A practical low energy method of time keeping at node level is proposed and tested. The performances of the proposed solution in terms of short and long term stability and energy requirements are analyzed and compared with existing solutions. Simulation and experimental results, some advantages and disadvantages of the method are presented at the end of the paper.

  17. Insensitivity of synchronization to network structure in chaotic pendulum systems with time-delay coupling

    Science.gov (United States)

    Yao, Chenggui; Zhan, Meng; Shuai, Jianwei; Ma, Jun; Kurths, Jürgen

    2017-12-01

    It has been generally believed that both time delay and network structure could play a crucial role in determining collective dynamical behaviors in complex systems. In this work, we study the influence of coupling strength, time delay, and network topology on synchronization behavior in delay-coupled networks of chaotic pendulums. Interestingly, we find that the threshold value of the coupling strength for complete synchronization in such networks strongly depends on the time delay in the coupling, but appears to be insensitive to the network structure. This lack of sensitivity was numerically tested in several typical regular networks, such as different locally and globally coupled ones as well as in several complex networks, such as small-world and scale-free networks. Furthermore, we find that the emergence of a synchronous periodic state induced by time delay is of key importance for the complete synchronization.

  18. Robust fixed-time synchronization of delayed Cohen-Grossberg neural networks.

    Science.gov (United States)

    Wan, Ying; Cao, Jinde; Wen, Guanghui; Yu, Wenwu

    2016-01-01

    The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Synchronization control for large-scale network systems

    CERN Document Server

    Wu, Yuanqing; Su, Hongye; Shi, Peng; Wu, Zheng-Guang

    2017-01-01

    This book provides recent advances in analysis and synthesis of Large-scale network systems (LSNSs) with sampled-data communication and non-identical nodes. In its first chapter of the book presents an introduction to Synchronization of LSNSs and Algebraic Graph Theory as well as an overview of recent developments of LSNSs with sampled data control or output regulation control. The main text of the book is organized into two main parts - Part I: LSNSs with sampled-data communication and Part II: LSNSs with non-identical nodes. This monograph provides up-to-date advances and some recent developments in the analysis and synthesis issues for LSNSs with sampled-data communication and non-identical nodes. It describes the constructions of the adaptive reference generators in the first stage and the robust regulators in the second stage. Examples are presented to show the effectiveness of the proposed design techniques.

  20. Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

    Science.gov (United States)

    Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

    2017-10-01

    This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

  1. A general fractional-order dynamical network: synchronization behavior and state tuning.

    Science.gov (United States)

    Wang, Junwei; Xiong, Xiaohua

    2012-06-01

    A general fractional-order dynamical network model for synchronization behavior is proposed. Different from previous integer-order dynamical networks, the model is made up of coupled units described by fractional differential equations, where the connections between individual units are nondiffusive and nonlinear. We show that the synchronous behavior of such a network cannot only occur, but also be dramatically different from the behavior of its constituent units. In particular, we find that simple behavior can emerge as synchronized dynamics although the isolated units evolve chaotically. Conversely, individually simple units can display chaotic attractors when the network synchronizes. We also present an easily checked criterion for synchronization depending only on the eigenvalues distribution of a decomposition matrix and the fractional orders. The analytic results are complemented with numerical simulations for two networks whose nodes are governed by fractional-order Lorenz dynamics and fractional-order Rössler dynamics, respectively.

  2. Organization of anti-phase synchronization pattern in neural networks: what are the key factors?

    Directory of Open Access Journals (Sweden)

    Dong eLi

    2011-12-01

    Full Text Available Anti-phase oscillation has been widely observed in cortical neuralnetwork. Elucidating the mechanism underlying the organization ofanti-phase pattern is of significance for better understanding morecomplicated pattern formations in brain networks. In dynamicalsystems theory, the organization of anti-phase oscillation patternhas usually been considered to relate to time-delay in coupling.This is consistent to conduction delays in real neural networks inthe brain due to finite propagation velocity of action potentials.However, other structural factors in cortical neural network, suchas modular organization (connection density and the coupling types(excitatory or inhibitory, could also play an important role. Inthis work, we investigate the anti-phase oscillation patternorganized on a two-module network of either neuronal cell model orneural mass model, and analyze the impact of the conduction delaytimes, the connection densities, and coupling types. Our resultsshow that delay times and coupling types can play key roles in thisorganization. The connection densities may have an influence on thestability if an anti-phase pattern exists due to the other factors.Furthermore, we show that anti-phase synchronization of slowoscillations can be achieved with small delay times if there isinteraction between slow and fast oscillations. These results aresignificant for further understanding more realistic spatiotemporaldynamics of cortico-cortical communications.

  3. Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network.

    Science.gov (United States)

    Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun

    2017-03-08

    Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.

  4. Factors affecting the cerebral network in brain tumor patients.

    Science.gov (United States)

    Heimans, Jan J; Reijneveld, Jaap C

    2012-06-01

    Brain functions, including cognitive functions, are frequently disturbed in brain tumor patients. These disturbances may result from the tumor itself, but also from the treatment directed against the tumor. Surgery, radiotherapy and chemotherapy all may affect cerebral functioning, both in a positive as well as in a negative way. Apart from the anti-tumor treatment, glioma patients often receive glucocorticoids and anti-epileptic drugs, which both also have influence on brain functioning. The effect of a brain tumor on cerebral functioning is often more global than should be expected on the basis of the local character of the disease, and this is thought to be a consequence of disturbance of the cerebral network as a whole. Any network, whether it be a neural, a social or an electronic network, can be described in parameters assessing the topological characteristics of that particular network. Repeated assessment of neural network characteristics in brain tumor patients during their disease course enables study of the dynamics of neural networks and provides more insight into the plasticity of the diseased brain. Functional MRI, electroencephalography and especially magnetoencephalography are used to measure brain function and the signals that are being registered with these techniques can be analyzed with respect to network characteristics such as "synchronization" and "clustering". Evidence accumulates that loss of optimal neural network architecture negatively impacts complex cerebral functioning and also decreases the threshold to develop epileptic seizures. Future research should be focused on both plasticity of neural networks and the factors that have impact on that plasticity as well as the possible role of assessment of neural network characteristics in the determination of cerebral function during the disease course.

  5. Study of consensus-based time synchronization in wireless sensor networks.

    Science.gov (United States)

    He, Jianping; Li, Hao; Chen, Jiming; Cheng, Peng

    2014-03-01

    Recently, various consensus-based protocols have been developed for time synchronization in wireless sensor networks. However, due to the uncertainties lying in both the hardware fabrication and network communication processes, it is not clear how most of the protocols will perform in real implementations. In order to reduce such gap, this paper investigates whether and how the typical consensus-based time synchronization protocols can tolerate the uncertainties in practical sensor networks through extensive testbed experiments. For two typical protocols, i.e., Average Time Synchronization (ATS) and Maximum Time Synchronization (MTS), we first analyze how the time synchronization accuracy will be affected by various uncertainties in the system. Then, we implement both protocols on our sensor network testbed consisted of Micaz nodes, and investigate the time synchronization performance and robustness under various network settings. Noticing that the synchronized clocks under MTS may be slightly faster than the desirable clock, by adopting both maximum consensus and minimum consensus, we propose a modified protocol, MMTS, which is able to drive the synchronized clocks closer to the desirable clock while maintaining the convergence rate and synchronization accuracy of MTS. © 2013 ISA. Published by ISA. All rights reserved.

  6. Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

    Directory of Open Access Journals (Sweden)

    Liu Heng

    Full Text Available This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as a drive network, and a known response complex network with an input controller is designed to identify the drive network. Under the proposed input controller, the drive network and the response network can achieve anticipatory projective synchronization when the system is steady. Lyapunov theorem and Barbǎlat's lemma guarantee the stability of synchronization manifold between two networks. When the synchronization is achieved, the system parameters and topology in response network can be changed to equal with the parameters and topology in drive network. A numerical example is given to show the effectiveness of the proposed method.

  7. Visual analytics of brain networks.

    Science.gov (United States)

    Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

    2012-05-15

    Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

  8. Chaotic Synchronization in Nearest-Neighbor Coupled Networks of 3D CNNs

    Directory of Open Access Journals (Sweden)

    H. Serrano-Guerrero

    2013-02-01

    Full Text Available In this paper, a synchronization of Cellular Neural Networks (CNNs in nearest-neighbor coupled arrays, is numerically studied. Synchronization of multiple chaotic CNNs is achieved by appealing to complex systems theory. In particular, we consider dynamical networks composed by 3D CNNs, as interconnected nodes, where the interactions in the networks are defined by coupling the first state of each node. Four cases of interest are considered: i synchronization without chaotic master, ii master-slave configuration (directed ring, iii open ring configuration (a path, and iv directed path configuration. In addition, an application to chaotic communication networks is given.

  9. State feedback controller design for the synchronization of Boolean networks with time delays

    Science.gov (United States)

    Li, Fangfei; Li, Jianning; Shen, Lijuan

    2018-01-01

    State feedback control design to make the response Boolean network synchronize with the drive Boolean network is far from being solved in the literature. Motivated by this, this paper studies the feedback control design for the complete synchronization of two coupled Boolean networks with time delays. A necessary condition for the existence of a state feedback controller is derived first. Then the feedback control design procedure for the complete synchronization of two coupled Boolean networks is provided based on the necessary condition. Finally, an example is given to illustrate the proposed design procedure.

  10. Adaptive synchronization of the complex dynamical network with non-derivative and derivative coupling

    Energy Technology Data Exchange (ETDEWEB)

    Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teachers' College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Sun Wen [School of Mathematics and Information, Yangtze University, Hubei Jingzhou 434023 (China)

    2010-04-05

    This Letter investigates the synchronization of a general complex dynamical network with non-derivative and derivative coupling. Based on LaSalle's invariance principle, adaptive synchronization criteria are obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-derivative and derivative coupling can asymptotically synchronize to a given trajectory, and several useful criteria for synchronization are given. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, simulations results show the method is effective.

  11. Emergence of synchronization induced by the interplay between two prisoner's dilemma games with volunteering in small-world networks.

    Science.gov (United States)

    Chen, Yong; Qin, Shao-Meng; Yu, Lianchun; Zhang, Shengli

    2008-03-01

    We studied synchronization between prisoner's dilemma games with voluntary participation in two Newman-Watts small-world networks. It was found that there are three kinds of synchronization: partial phase synchronization, total phase synchronization, and complete synchronization, for varied coupling factors. Besides, two games can reach complete synchronization for the large enough coupling factor. We also discussed the effect of the coupling factor on the amplitude of oscillation of cooperator density.

  12. Altered Distant Synchronization of Background Network in Mild Cognitive Impairment during an Executive Function Task

    Directory of Open Access Journals (Sweden)

    Pengyun Wang

    2017-09-01

    Full Text Available Few studies to date have investigated the background network in the cognitive state relying on executive function in mild cognitive impairment (MCI patients. Using the index of degree of centrality (DC, we explored distant synchronization of background network in MCI during a hybrid delayed-match-to-sample task (DMST, which mainly relies on the working memory component of executive function. We observed significant interactions between group and cognitive state in the bilateral posterior cingulate cortex (PCC and the ventral subregion of precuneus. For normal control (NC group, the long distance functional connectivity (FC of the PCC/precuneus with the other regions of the brain was higher in rest state than that working memory state. For MCI patients, however, this pattern altered. There was no significant difference between rest and working memory state. The similar pattern was observed in the other cluster located in the right angular gyrus. To examine whether abnormal DC in PCC/precuneus and angular gyrus partially resulted from the deficit of FC between these regions and the other parts in the whole brain, we conducted a seed-based correlation analysis with these regions as seeds. The results indicated that the FC between bilateral PCC/precuneus and the right inferior parietal lobule (IPL increased from rest to working memory state for NC participants. For MCI patients, however, there was no significant change between rest and working memory state. The similar pattern was observed for the FC between right angular gyrus and right anterior insula. However, there was no difference between MCI and NC groups in global efficiency and modularity. It may indicate a lack of efficient reorganization from rest state to a working memory state in the brain network of MCI patients. The present study demonstrates the altered distant synchronization of background network in MCI during a task relying on executive function. The results provide a new

  13. Adaptive Asymptotical Synchronization for Stochastic Complex Networks with Time-Delay and Markovian Switching

    Directory of Open Access Journals (Sweden)

    Xueling Jiang

    2014-01-01

    Full Text Available The problem of adaptive asymptotical synchronization is discussed for the stochastic complex dynamical networks with time-delay and Markovian switching. By applying the stochastic analysis approach and the M-matrix method for stochastic complex networks, several sufficient conditions to ensure adaptive asymptotical synchronization for stochastic complex networks are derived. Through the adaptive feedback control techniques, some suitable parameters update laws are obtained. Simulation result is provided to substantiate the effectiveness and characteristics of the proposed approach.

  14. Multi-scale brain networks

    CERN Document Server

    Betzel, Richard F

    2016-01-01

    The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales -- of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuros...

  15. Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism.

    Science.gov (United States)

    Li, Lulu; Ho, Daniel W C; Cao, Jinde; Lu, Jianquan

    2016-04-01

    Cluster synchronization is a typical collective behavior in coupled dynamical systems, where the synchronization occurs within one group, while there is no synchronization among different groups. In this paper, under event-based mechanism, pinning cluster synchronization in an array of coupled neural networks is studied. A new event-triggered sampled-data transmission strategy, where only local and event-triggering states are utilized to update the broadcasting state of each agent, is proposed to realize cluster synchronization of the coupled neural networks. Furthermore, a self-triggered pinning cluster synchronization algorithm is proposed, and a set of iterative procedures is given to compute the event-triggered time instants. Hence, this will reduce the computational load significantly. Finally, an example is given to demonstrate the effectiveness of the theoretical results. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  16. Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

    Science.gov (United States)

    Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

    2017-01-01

    Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

  17. Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

    Directory of Open Access Journals (Sweden)

    Chuan Chen

    Full Text Available Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs with both discrete delay and distributed delay (mixed delays. By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

  18. Multi-scale brain networks.

    Science.gov (United States)

    Betzel, Richard F; Bassett, Danielle S

    2016-11-11

    The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  19. Adaptive Synchronization of Complex Dynamical Networks Governed by Local Lipschitz Nonlinearlity on Switching Topology

    Directory of Open Access Journals (Sweden)

    Bo Liu

    2013-01-01

    Full Text Available This paper investigates the adaptive synchronization of complex dynamical networks satisfying the local Lipschitz condition with switching topology. Based on differential inclusion and nonsmooth analysis, it is proved that all nodes can converge to the synchronous state, even though only one node is informed by the synchronous state via introducing decentralized adaptive strategies to the coupling strengths and feedback gains. Finally, some numerical simulations are worked out to illustrate the analytical results.

  20. Surgical Treatment for Non-small Cell Lung Cancer Patients with Synchronous Solitary Brain Metastasis

    Directory of Open Access Journals (Sweden)

    Hao BAI

    2013-12-01

    Full Text Available Background and objective Brain metastases are common in non-small cell lung cancer. Usual treatments include radiotherapy and chemotherapy. However, these methods result in poor patient prognosis. The aim of this study is to assess the effectiveness of surgical resection in the multimodality management of non-small cell lung cancer patients with synchronous solitary brain metastasis. Methods The clinical data of 46 non-small cell lung cancer patients with synchronous solitary brain metastasis were retrospectively reviewed. All patients underwent surgical resection of primary lung tumor, followed by whole brain radiotherapy and chemotherapy. In addition, 13 out of the 46 patients underwent resection of brain metastasis, whereas the remaining 33 patients received stereotactic radiosurgery. Results The median survival time of the enrolled patients was 16.8 months. The 1-, 2-, and 3-year survival rates were 76.1%, 20.9%, and 4.7%, respectively. The median survival times of the patients with brain metastasis resection or stereotactic radiosurgery were 18.3 and 15.8 months, respectively (P=0.091,2. Conclusion Surgical resection of primary lung tumor and brain metastasis may improve prognosis of non-small cell lung cancer patients with synchronous solitary brain metastasis. However, the survival benefit of surgical resection over brain metastasis resection or stereotactic radiosurgery is uncertain.

  1. SYNCHRONIZATION OF NATIONAL GRID NETWORK WITH THE ELECTRICITY SHIPS NETWORK IN THE "SHORE TO SHIP" SYSTEM

    Directory of Open Access Journals (Sweden)

    Dariusz TARNAPOWICZ

    2013-07-01

    Full Text Available ‘Shore to ship’ system – ships’ power supply from the local electrical substations – is one of the effective ways to limit the negative impact of the ships lying in ports on the environment. Energy infrastructure of the port installation necessary to provide ships with power supply has to be designed so that different types of ships can use it. The important issue concerning ‘shore to ship’ system is the quality of power supply. This can be achieved via sustaining continuity of power supply while switching from the ships’ electrical network over to the national grid. In this article the author presents the way of synchronizing the national grid with the ships’ electrical network during ship’s lying in port. Such synchronization would allow for uninterruptible work of the ship’s electrical devices.

  2. Atypical Bilateral Brain Synchronization in the Early Stage of Human Voice Auditory Processing in Young Children with Autism.

    Directory of Open Access Journals (Sweden)

    Toshiharu Kurita

    Full Text Available Autism spectrum disorder (ASD has been postulated to involve impaired neuronal cooperation in large-scale neural networks, including cortico-cortical interhemispheric circuitry. In the context of ASD, alterations in both peripheral and central auditory processes have also attracted a great deal of interest because these changes appear to represent pathophysiological processes; therefore, many prior studies have focused on atypical auditory responses in ASD. The auditory evoked field (AEF, recorded by magnetoencephalography, and the synchronization of these processes between right and left hemispheres was recently suggested to reflect various cognitive abilities in children. However, to date, no previous study has focused on AEF synchronization in ASD subjects. To assess global coordination across spatially distributed brain regions, the analysis of Omega complexity from multichannel neurophysiological data was proposed. Using Omega complexity analysis, we investigated the global coordination of AEFs in 3-8-year-old typically developing (TD children (n = 50 and children with ASD (n = 50 in 50-ms time-windows. Children with ASD displayed significantly higher Omega complexities compared with TD children in the time-window of 0-50 ms, suggesting lower whole brain synchronization in the early stage of the P1m component. When we analyzed the left and right hemispheres separately, no significant differences in any time-windows were observed. These results suggest lower right-left hemispheric synchronization in children with ASD compared with TD children. Our study provides new evidence of aberrant neural synchronization in young children with ASD by investigating auditory evoked neural responses to the human voice.

  3. Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching.

    Science.gov (United States)

    Wu, Yuanyuan; Cao, Jinde; Li, Qingbo; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-01-01

    This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

    Science.gov (United States)

    Sheng, Yin; Zhang, Hao; Zeng, Zhigang

    2017-10-01

    This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

  5. Adaptive synchronization of drive-response fractional-order complex dynamical networks with uncertain parameters

    Science.gov (United States)

    Yang, Li-xin; Jiang, Jun

    2014-05-01

    This paper investigates the adaptive synchronization in the drive-response fractional-order dynamical networks with uncertain parameters. By means of both the stability theory of fractional-order differential system and the adaptive control technique, a novel adaptive synchronization controller is developed with a more general and simpler analytical expression, which does not contain the parameters of the complex network, and effective adaptive laws of parameters. Furthermore, the very strong and conservative uniformly Lipschitz condition on the node dynamics of complex network is released. To demonstrate the validity of the proposed method, the examples for the synchronization of systems with the chaotic and hyper-chaotic node dynamics are presented.

  6. Synchronization-based computation through networks of coupled oscillators

    Directory of Open Access Journals (Sweden)

    Daniel eMalagarriga

    2015-08-01

    Full Text Available The mesoscopic activity of the brain is strongly dynamical, while at the sametime exhibiting remarkable computational capabilities. In order to examinehow these two features coexist, here we show that the patterns of synchronizedoscillations displayed by networks of neural mass models, representing cortical columns, can be usedas substrates for Boolean computation. Our results reveal that different logicaloperations can be implemented by the same neural mass network at different timesfollowing the dynamics of the input. The results are reproduced experimentallywith electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the oscillators responsible for the functioning of the gates. We also show that theinformation-processing capabilities of coupled oscillations go beyond thesimple juxtaposition of logic gates.

  7. Brain networks of social comparison.

    Science.gov (United States)

    Kedia, Gayannée; Lindner, Michael; Mussweiler, Thomas; Ihssen, Niklas; Linden, David E J

    2013-03-27

    Social comparison, that is, the process of comparing oneself to other people, is a ubiquitous social cognitive mechanism; however, so far its neural correlates have remained unknown. The present study tested the hypothesis that social comparisons are supported by partly dissociated networks, depending on whether the dimension under comparison concerns a physical or a psychological attribute. We measured brain activity with functional MRI, whereas participants were comparing their own height or intelligence to that of individuals they personally know. Height comparisons were associated with higher activity in a frontoparietal network involved in spatial and numerical cognition. Conversely, intelligence comparisons recruited a network of midline areas that have been previously implicated in the attribution of mental states to oneself and others (Theory of mind). These findings suggest that social comparisons rely on diverse domain-specific mechanisms rather than on one unitary process.

  8. Synchronized RACH-less Handover Solution for LTE Heterogeneous Networks

    DEFF Research Database (Denmark)

    Barbera, Simone; Pedersen, Klaus I.; Rosa, Claudio

    2015-01-01

    Some of the most recent LTE features require synchronous base stations, and time-synchronized base stations also offer opportunities for improved handover mechanisms by introducing a new synchronized RACH-less handover scheme. The synchronized RACH-less handover solution offers significant...... reductions in the data connectivity interruption time at each handover, no need for random access in the target cell, and reduced overall handover execution time. Laboratory handover measurement results, using commercial LTE equipment, are presented and analyzed to justify the latency benefits...

  9. Dynamic reorganization of functional brain networks during picture naming.

    Science.gov (United States)

    Hassan, Mahmoud; Benquet, Pascal; Biraben, Arnaud; Berrou, Claude; Dufor, Olivier; Wendling, Fabrice

    2015-12-01

    For efficient information processing during cognitive activity, functional brain networks have to rapidly and dynamically reorganize on a sub-second time scale. Tracking the spatiotemporal dynamics of large scale networks over this short time duration is a very challenging issue. Here, we tackle this problem by using dense electroencephalography (EEG) recorded during a picture naming task. We found that (i) the picture naming task can be divided into six brain network states (BNSs) characterized by significantly high synchronization of gamma (30-45 Hz) oscillations, (ii) fast transitions occur between these BNSs that last from 30 msec to 160 msec, (iii) based on the state of the art of the picture naming task, we consider that the spatial location of their nodes and edges, as well as the timing of transitions, indicate that each network can be associated with one or several specific function (from visual processing to articulation) and (iv) the comparison with previously-used approach aimed at localizing the sources showed that the network-based approach reveals networks that are more specific to the performed task. We speculate that the persistence of several brain regions in successive BNSs participates to fast and efficient information processing in the brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Social and Virtual Networks: Evaluating Synchronous Online Interviewing Using Instant Messenger

    Science.gov (United States)

    Hinchcliffe, Vanessa; Gavin, Helen

    2009-01-01

    This paper describes an evaluation of the quality and utility of synchronous online interviewing for data collection in social network research. Synchronous online interviews facilitated by Instant Messenger as the communication medium, were undertaken with ten final year university students. Quantitative and qualitative content analysis of…

  11. $\\mathcal{H}_{\\infty}$ almost output synchronization for heterogeneous networks without exchange of controller states

    NARCIS (Netherlands)

    Zhang, Meirong; Saberi, Ali; Grip, H°avard Fjær; Stoorvogel, Antonie Arij

    2015-01-01

    We consider the H∞ almost output synchronization and regulated output synchronization problem for heterogeneous directed networks with external disturbances where agents are introspective (i.e., agents have access to a part of their own states). A decentralized protocol is designed for each agent,

  12. Time synchronization for an Ethernet-based real-time token network

    NARCIS (Netherlands)

    Hanssen, F.T.Y.; van den Boom, Joost; Jansen, P.G.; Scholten, Johan

    We present a distributed clock synchronization algorithm. It performs clock synchronization on an Ethernet-based real-time token local area network, without the use of an external clock source. It is used to enable the token schedulers in each node to agree upon a common time. Its intended use is in

  13. Self-organization of synchronous activity propagation in neuronal networks driven by local excitation.

    Science.gov (United States)

    Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen

    2015-01-01

    Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence.

  14. Modeling synchronization in networks of delay-coupled fiber ring lasers.

    Science.gov (United States)

    Lindley, Brandon S; Schwartz, Ira B

    2011-11-21

    We study the onset of synchronization in a network of N delay-coupled stochastic fiber ring lasers with respect to various parameters when the coupling power is weak. In particular, for groups of three or more ring lasers mutually coupled to a central hub laser, we demonstrate a robust tendency toward out-of-phase (achronal) synchronization between the N-1 outer lasers and the single inner laser. In contrast to the achronal synchronization, we find the outer lasers synchronize with zero-lag (isochronal) with respect to each other, thus forming a set of N-1 coherent fiber lasers. © 2011 Optical Society of America

  15. Synchronization of fractional-order linear complex networks with directed coupling topology

    Science.gov (United States)

    Fang, Qingxiang; Peng, Jigen

    2018-01-01

    The synchronization of fractional-order complex networks with general linear dynamics under directed connected topology is investigated. The synchronization problem is converted to an equivalent simultaneous stability problem of corresponding independent subsystems by use of a pseudo-state transformation technique and real Jordan canonical form of matrix. Sufficient conditions in terms of linear matrix inequalities for synchronization are established according to stability theory of fractional-order differential equations. In a certain range of fractional order, the effects of the fractional order on synchronization is clearly revealed. Conclusions obtained in this paper generalize the existing results. Three numerical examples are provided to illustrate the validity of proposed conclusions.

  16. SYNCHRONIZATION OF HETEROGENEOUS OSCILLATORS UNDER NETWORK MODIFICATIONS: PERTURBATION AND OPTIMIZATION OF THE SYNCHRONY ALIGNMENT FUNCTION.

    Science.gov (United States)

    Taylor, Dane; Skardal, Per Sebastian; Sun, Jie

    2016-01-01

    Synchronization is central to many complex systems in engineering physics (e.g., the power-grid, Josephson junction circuits, and electro-chemical oscillators) and biology (e.g., neuronal, circadian, and cardiac rhythms). Despite these widespread applications-for which proper functionality depends sensitively on the extent of synchronization-there remains a lack of understanding for how systems can best evolve and adapt to enhance or inhibit synchronization. We study how network modifications affect the synchronization properties of network-coupled dynamical systems that have heterogeneous node dynamics (e.g., phase oscillators with non-identical frequencies), which is often the case for real-world systems. Our approach relies on a synchrony alignment function (SAF) that quantifies the interplay between heterogeneity of the network and of the oscillators and provides an objective measure for a system's ability to synchronize. We conduct a spectral perturbation analysis of the SAF for structural network modifications including the addition and removal of edges, which subsequently ranks the edges according to their importance to synchronization. Based on this analysis, we develop gradient-descent algorithms to efficiently solve optimization problems that aim to maximize phase synchronization via network modifications. We support these and other results with numerical experiments.

  17. Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network

    Science.gov (United States)

    Zhu, Darui; Liu, Chongxin; Yan, Bingnan

    2014-01-01

    We introduce a chaos model for a permanent-magnet synchronous motor and construct a coupled chaotic motor in a complex dynamic network using the Newman-Watts small-world network algorithm. We apply adaptive pinning control theory for complex networks to obtain suitable adaptive feedback gain and the number of nodes to be pinned. Nodes of low degree are pinned to realize global asymptotic synchronization in the complex network. The proposed adaptive pinning controller is added to the complex motor network for simulation and verification.

  18. Pattern recognition via synchronization in phase-locked loop neural networks.

    Science.gov (United States)

    Hoppensteadt, F C; Izhikevich, E M

    2000-01-01

    We propose a novel architecture of an oscillatory neural network that consists of phase-locked loop (PLL) circuits. It stores and retrieves complex oscillatory patterns as synchronized states with appropriate phase relations between neurons.

  19. Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

    Science.gov (United States)

    Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

    2016-12-01

    This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Size-dependent regulation of synchronized activity in living neuronal networks.

    Science.gov (United States)

    Yamamoto, Hideaki; Kubota, Shigeru; Chida, Yudai; Morita, Mayu; Moriya, Satoshi; Akima, Hisanao; Sato, Shigeo; Hirano-Iwata, Ayumi; Tanii, Takashi; Niwano, Michio

    2016-07-01

    We study the effect of network size on synchronized activity in living neuronal networks. Dissociated cortical neurons form synaptic connections in culture and generate synchronized spontaneous activity within 10 days in vitro. Using micropatterned surfaces to extrinsically control the size of neuronal networks, we show that synchronized activity can emerge in a network as small as 12 cells. Furthermore, a detailed comparison of small (∼20 cells), medium (∼100 cells), and large (∼400 cells) networks reveal that synchronized activity becomes destabilized in the small networks. A computational modeling of neural activity is then employed to explore the underlying mechanism responsible for the size effect. We find that the generation and maintenance of the synchronized activity can be minimally described by: (1) the stochastic firing of each neuron in the network, (2) enhancement in the network activity in a positive feedback loop of excitatory synapses, and (3) Ca-dependent suppression of bursting activity. The model further shows that the decrease in total synaptic input to a neuron that drives the positive feedback amplification of correlated activity is a key factor underlying the destabilization of synchrony in smaller networks. Spontaneous neural activity plays a critical role in cortical information processing, and our work constructively clarifies an aspect of the structural basis behind this.

  1. The formation of synchronization cliques during the development of modular neural networks

    Science.gov (United States)

    Fuchs, Einat; Ayali, Amir; Ben-Jacob, Eshel; Boccaletti, Stefano

    2009-09-01

    Modular organization is a special feature shared by many biological and social networks alike. It is a hallmark for systems exhibiting multitasking, in which individual tasks are performed by separated and yet coordinated functional groups. Understanding how networks of segregated modules develop to support coordinated multitasking functionalities is the main topic of the current study. Using simulations of biologically inspired neuronal networks during development, we study the formation of functional groups (cliques) and inter-neuronal synchronization. The results indicate that synchronization cliques first develop locally according to the explicit network topological organization. Later on, at intermediate connectivity levels, when networks have both local segregation and long-range integration, new synchronization cliques with distinctive properties are formed. In particular, by defining a new measure of synchronization centrality, we identify at these developmental stages dominant neurons whose functional centrality largely exceeds the topological one. These are generated mainly in a few dominant clusters that become the centers of the newly formed synchronization cliques. We show that by the local synchronization properties at the very early developmental stages, it is possible to predict with high accuracy which clusters will become dominant in later stages of network development.

  2. Synchronization of a Class of Fractional-Order Chaotic Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Chai

    2013-08-01

    Full Text Available The synchronization problem is studied in this paper for a class of fractional-order chaotic neural networks. By using the Mittag-Leffler function, M-matrix and linear feedback control, a sufficient condition is developed ensuring the synchronization of such neural models with the Caputo fractional derivatives. The synchronization condition is easy to verify, implement and only relies on system structure. Furthermore, the theoretical results are applied to a typical fractional-order chaotic Hopfield neural network, and numerical simulation demonstrates the effectiveness and feasibility of the proposed method.

  3. Adaptive Synchronization for a Class of Uncertain Fractional-Order Neural Networks

    Directory of Open Access Journals (Sweden)

    Heng Liu

    2015-10-01

    Full Text Available In this paper, synchronization for a class of uncertain fractional-order neural networks subject to external disturbances and disturbed system parameters is studied. Based on the fractional-order extension of the Lyapunov stability criterion, an adaptive synchronization controller is designed, and fractional-order adaptation law is proposed to update the controller parameter online. The proposed controller can guarantee that the synchronization errors between two uncertain fractional-order neural networks converge to zero asymptotically. By using some proposed lemmas, the quadratic Lyapunov functions are employed in the stability analysis. Finally, numerical simulations are presented to confirm the effectiveness of the proposed method.

  4. Adaptative synchronization in multi-output fractional-order complex dynamical networks and secure communications

    Science.gov (United States)

    Mata-Machuca, Juan L.; Aguilar-López, Ricardo

    2018-01-01

    This work deals with the adaptative synchronization of complex dynamical networks with fractional-order nodes and its application in secure communications employing chaotic parameter modulation. The complex network is composed of multiple fractional-order systems with mismatch parameters and the coupling functions are given to realize the network synchronization. We introduce a fractional algebraic synchronizability condition (FASC) and a fractional algebraic identifiability condition (FAIC) which are used to know if the synchronization and parameters estimation problems can be solved. To overcome these problems, an adaptative synchronization methodology is designed; the strategy consists in proposing multiple receiver systems which tend to follow asymptotically the uncertain transmitters systems. The coupling functions and parameters of the receiver systems are adjusted continually according to a convenient sigmoid-like adaptative controller (SLAC), until the measurable output errors converge to zero, hence, synchronization between transmitter and receivers is achieved and message signals are recovered. Indeed, the stability analysis of the synchronization error is based on the fractional Lyapunov direct method. Finally, numerical results corroborate the satisfactory performance of the proposed scheme by means of the synchronization of a complex network consisting of several fractional-order unified chaotic systems.

  5. Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

    Science.gov (United States)

    Yang, Shiju; Li, Chuandong; Huang, Tingwen

    2016-03-01

    The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. A Study on the Distributed Antenna Based Heterogeneous Cognitive Wireless Network Synchronous MAC Protocol

    Directory of Open Access Journals (Sweden)

    Lian-Fen Huang

    2015-01-01

    Full Text Available This paper introduces distributed antennas into a cognitive radio network and presents a heterogeneous network. The best contribution of this paper is that it designs a synchronous cognitive MAC protocol (DAHCWNS-MAC protocol: distributed antenna based heterogeneous cognitive wireless network synchronous MAC protocol. The novel protocol aims at combining the advantages of cognitive radio and distributed antennas to fully utilize the licensed spectrum, broaden the communication range, and improve throughput. This paper carries out the mathematical modeling and performance simulation to demonstrate its superiority in improving the network throughput at the cost of increasing antenna hardware costs.

  7. Exponential Synchronization of Stochastic Complex Dynamical Networks with Impulsive Perturbations and Markovian Switching

    Directory of Open Access Journals (Sweden)

    Wuneng Zhou

    2014-01-01

    Full Text Available This paper investigates the exponential synchronization problem of stochastic complex dynamical networks with impulsive perturbation and Markovian switching. The complex dynamical networks consist of κ modes, and the networks switch from one mode to another according to a Markovian chain with known transition probability. Based on the Lyapunov function method and stochastic analysis, by employing M-matrix approach, some sufficient conditions are presented to ensure the exponential synchronization of stochastic complex dynamical networks with impulsive perturbation and Markovian switching, and the upper bound of impulsive gain is evaluated. At the end of this paper, two numerical examples are included to show the effectiveness of our results.

  8. Pinning synchronization of memristor-based neural networks with time-varying delays.

    Science.gov (United States)

    Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng

    2017-09-01

    In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Synchronization of General Complex Networks with Hybrid Couplings and Unknown Perturbations

    Directory of Open Access Journals (Sweden)

    Xinsong Yang

    2013-01-01

    Full Text Available The issue of synchronization for a class of hybrid coupled complex networks with mixed delays (discrete delays and distributed delays and unknown nonstochastic external perturbations is studied. The perturbations do not disappear even after all the dynamical nodes have reached synchronization. To overcome the bad effects of such perturbations, a simple but all-powerful robust adaptive controller is designed to synchronize the complex networks even without knowing a priori the functions and bounds of the perturbations. Based on Lyapunov stability theory, integral inequality Barbalat lemma, and Schur Complement lemma, rigorous proofs are given for synchronization of the complex networks. Numerical simulations verify the effectiveness of the new robust adaptive controller.

  10. Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control

    Science.gov (United States)

    Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong

    2017-09-01

    In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council

  11. Synchronous transitions of up and down states in a network model based on stimulations.

    Science.gov (United States)

    Xu, Xuying; Ni, Li; Wang, Rubin

    2017-01-07

    The phenomenon of spontaneous periodic up and down transitions is considered to be a significant characteristic of slow oscillations. Our previous theoretical studies have shown that the single neuron and network model can both exhibit spontaneous up and down transitions. Another characteristic of up and down dynamics is the synchronicity. So in this paper, we focused on the synchronized characteristic of up and down transitions in the network based on stimulations. Spontaneous activities showed no synchronous transitions between neurons. However, the external stimulation, mainly the stimulation frequency and the number of neurons stimulated on were related to the synchronous transitions of up and down states. The simulation results suggested that simultaneous high frequency excitation or firing of neurons in the network was responsible for the generation of synchronous transitions of up and down states. Through the observation and analysis of the findings, we have tried to explain the reason for synchronous up and down transitions and to lay the foundation for further work on the role of these synchronized transitions in cortex activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays.

    Science.gov (United States)

    Wei, Ruoyu; Cao, Jinde; Alsaedi, Ahmed

    2018-02-01

    This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results.

  13. The effects of music on brain functional networks: a network analysis.

    Science.gov (United States)

    Wu, J; Zhang, J; Ding, X; Li, R; Zhou, C

    2013-10-10

    The human brain can dynamically adapt to the changing surroundings. To explore this issue, we adopted graph theoretical tools to examine changes in electroencephalography (EEG) functional networks while listening to music. Three different excerpts of Chinese Guqin music were played to 16 non-musician subjects. For the main frequency intervals, synchronizations between all pair-wise combinations of EEG electrodes were evaluated with phase lag index (PLI). Then, weighted connectivity networks were created and their organizations were characterized in terms of an average clustering coefficient and characteristic path length. We found an enhanced synchronization level in the alpha2 band during music listening. Music perception showed a decrease of both normalized clustering coefficient and path length in the alpha2 band. Moreover, differences in network measures were not observed between musical excerpts. These experimental results demonstrate an increase of functional connectivity as well as a more random network structure in the alpha2 band during music perception. The present study offers support for the effects of music on human brain functional networks with a trend toward a more efficient but less economical architecture. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  14. Modeling of synchronization behavior of bursting neurons at nonlinearly coupled dynamical networks.

    Science.gov (United States)

    Çakir, Yüksel

    2016-01-01

    Synchronization behaviors of bursting neurons coupled through electrical and dynamic chemical synapses are investigated. The Izhikevich model is used with random and small world network of bursting neurons. Various currents which consist of diffusive electrical and time-delayed dynamic chemical synapses are used in the simulations to investigate the influences of synaptic currents and couplings on synchronization behavior of bursting neurons. The effects of parameters, such as time delay, inhibitory synaptic strengths, and decay time on synchronization behavior are investigated. It is observed that in random networks with no delay, bursting synchrony is established with the electrical synapse alone, single spiking synchrony is observed with hybrid coupling. In small world network with no delay, periodic bursting behavior with multiple spikes is observed when only chemical and only electrical synapse exist. Single-spike and multiple-spike bursting are established with hybrid couplings. A decrease in the synchronization measure is observed with zero time delay, as the decay time is increased in random network. For synaptic delays which are above active phase period, synchronization measure increases with an increase in synaptic strength and time delay in small world network. However, in random network, it increases with only an increase in synaptic strength.

  15. Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller.

    Science.gov (United States)

    Ding, Zhixia; Shen, Yi

    2016-04-01

    This paper investigates global projective synchronization of nonidentical fractional-order neural networks (FNNs) based on sliding mode control technique. We firstly construct a fractional-order integral sliding surface. Then, according to the sliding mode control theory, we design a sliding mode controller to guarantee the occurrence of the sliding motion. Based on fractional Lyapunov direct methods, system trajectories are driven to the proposed sliding surface and remain on it evermore, and some novel criteria are obtained to realize global projective synchronization of nonidentical FNNs. As the special cases, some sufficient conditions are given to ensure projective synchronization of identical FNNs, complete synchronization of nonidentical FNNs and anti-synchronization of nonidentical FNNs. Finally, one numerical example is given to demonstrate the effectiveness of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Introduction to focus issue: Synchronization in large networks and continuous media-data, models, and supermodels.

    Science.gov (United States)

    Duane, Gregory S; Grabow, Carsten; Selten, Frank; Ghil, Michael

    2017-12-01

    The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.

  17. Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels

    Science.gov (United States)

    Duane, Gregory S.; Grabow, Carsten; Selten, Frank; Ghil, Michael

    2017-12-01

    The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.

  18. Brain network clustering with information flow motifs

    NARCIS (Netherlands)

    Märtens, M.; Meier, J.M.; Hillebrand, Arjan; Tewarie, Prejaas; Van Mieghem, P.F.A.

    2017-01-01

    Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands.

  19. Chaotic Synchronization in Nearest-Neighbor Coupled Networks of 3D CNNs

    Directory of Open Access Journals (Sweden)

    R.A. Chávez- Pérez

    2013-01-01

    Full Text Available In this paper, a synchronization of Cellular Neural Networks (CNNs in nearest-neighbor coupled arrays, is numericallystudied. Synchronization of multiple chaotic CNNs is achieved by appealing to complex systems theory. In particular,we consider dynamical networks composed by 3D CNNs, as interconnected nodes, where the interactions in thenetworks are defined by coupling the first state of each node. Four cases of interest are considered: i synchronizationwithout chaotic master, ii master-slave configuration (directed ring, iii open ring configuration (a path, and ivdirected path configuration. In addition, an application to chaotic communication networks is given.

  20. Explosive synchronization in clustered scale-free networks: Revealing the existence of chimera state

    Science.gov (United States)

    Berec, V.

    2016-02-01

    The collective dynamics of Kuramoto oscillators with a positive correlation between the incoherent and fully coherent domains in clustered scale-free networks is studied. Emergence of chimera states for the onsets of explosive synchronization transition is observed during an intermediate coupling regime when degree-frequency correlation is established for the hubs with the highest degrees. Diagnostic of the abrupt synchronization is revealed by the intrinsic spectral properties of the network graph Laplacian encoded in the heterogeneous phase space manifold, through extensive analytical investigation, presenting realistic MC simulations of nonlocal interactions in discrete time dynamics evolving on the network.

  1. Projective Exponential Synchronization for a Class of Complex PDDE Networks with Multiple Time Delays

    Directory of Open Access Journals (Sweden)

    Chengdong Yang

    2015-10-01

    Full Text Available This paper addresses the problem of projective exponential synchronization for a class of complex spatiotemporal networks with multiple time delays satisfying the homogeneous Neumann boundary conditions, where the network is modeled by coupled partial differential-difference equations (PDDEs. A distributed proportional-spatial derivative (P-sD controller is designed by employing Lyapunov’s direct method and Kronecker product. The controller ensures the projective exponential synchronization of the PDDE network. The main result of this paper is presented in terms of standard linear matrix inequality (LMI. A numerical example is provided to show the effectiveness of the proposed design method.

  2. Mean-Square Exponential Synchronization of Stochastic Complex Dynamical Networks with Switching Topology by Impulsive Control

    Directory of Open Access Journals (Sweden)

    Xuefei Wu

    2013-01-01

    Full Text Available This paper investigates the mean-square exponential synchronization issues of delayed stochastic complex dynamical networks with switching topology and impulsive control. By using the Lyapunov functional method, impulsive control theory, and linear matrix inequality (LMI approaches, some sufficient conditions are derived to guarantee the mean-square exponential synchronization of delay complex dynamical network with switch topology, which are independent of the network size and switch topology. Numerical simulations are given to illustrate the effectiveness of the obtained results in the end.

  3. An energy efficient and dynamic time synchronization protocol for wireless sensor networks

    Science.gov (United States)

    Zhang, Anran; Bai, Fengshan

    2017-01-01

    Time synchronization is an important support technology of WSN(Wireless Sensor Network), and plays an irreplaceable role in the development of WSN. In view of the disadvantage of the traditional timing sync protocol for sensor networks (TPSN), we present a Physical Timing-sync Protocol (PTPSN) that aims at reducing the energy consumption of the synchronization process and realizes a dynamic Network. The algorithm broadcasts reference message to select some nodes in specific area. The receiver calculate offset of every selected node, and then calculate the average of offset to compensate for clock skew . At the same time ,we add time-filter process to ensure the security of the algorithm for time synchronization. The experiment results show that our algorithm is efficient in both saving energy consumption and dynamic network, and it can effectively resist attacks.

  4. Impacts of link addition and removal on synchronization of an elementary power network

    Science.gov (United States)

    Yang, Li-xin; Jiang, Jun

    2017-08-01

    This paper is devoted to the impacts of link addition and removal on synchronization of an elementary oscillatory network. By means of numerical simulations, it is found that the characteristics of edge have important consequences to synchronization in oscillatory networks. To be specific, we find that the heterogeneous (generators to consumer nodes and vice versa) edge additions whether in a separate community or between communities is beneficial for the enhancement of the synchronization. Nevertheless, the influence of homogeneous (generators to generators or consumers to consumers) edge additions is not obvious on the synchronizability of the power network. Furthermore, it is observed that a heterogeneous edge is deleted, the network exhibits weak synchronizability. However, the removal of a homogeneous edge does not affect the synchronizability. Besides, the numerical simulation results indicate that whereas the impact of a link additional and removal in a separate community become more sensitive to synchronizability than different communities.

  5. Fixed-Time Synchronization for Hybrid Coupled Dynamical Networks with Multilinks and Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    Baolin Qiu

    2017-01-01

    Full Text Available This paper concerns the problem of fixed/finite-time synchronization of hybrid coupled dynamical networks. The considered dynamical networks with multilinks contain only one transmittal time-varying delay for each subnetwork, which makes us get hold of more interesting and practical points. Two kinds of delay-dependent feedback controllers with multilinks as well as appropriate Lyapunov functions are defined to achieve the goal of fixed-time synchronization and finite-time synchronization for the networks. Some novel and effective criteria of hybrid coupled networks are derived based on fixed-time and finite-time stability analysis. Finally, two numerical simulation examples are given to show the effectiveness of the results proposed in our paper.

  6. Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

    Science.gov (United States)

    Bertolotti, Elena; Burioni, Raffaella; di Volo, Matteo; Vezzani, Alessandro

    2017-01-01

    We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons fI and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on fI, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.

  7. Self-synchronization in networked teams : Initializing and monitoring interteam collaborations.

    NARCIS (Netherlands)

    Bezooijen, B.J.A. van; Essens, P.J.M.D.

    2008-01-01

    Networked teams do not only have to make decisions that are in line with the overall goal, but face additional problems because teams have to synchronize decisions and actions with other teams in the network, Experiments have demonstrated that training teams for collaborating with other teams

  8. Interindividual synchronization of brain activity during live verbal communication.

    Science.gov (United States)

    Spiegelhalder, Kai; Ohlendorf, Sabine; Regen, Wolfram; Feige, Bernd; Tebartz van Elst, Ludger; Weiller, Cornelius; Hennig, Jürgen; Berger, Mathias; Tüscher, Oliver

    2014-01-01

    Verbal social interaction plays an important role both in the etiology and treatment of psychiatric disorders. However, the neural basis of social interaction has primarily been studied in the individual brain, neglecting the inter-individual perspective. Here, we show inter-individual neuronal coupling of brain activity during live verbal interaction, by investigating 11 pairs of good female friends who were instructed to speak about autobiographical life events during simultaneous fMRI acquisition. The analysis revealed that the time course of neural activity in areas associated with speech production was coupled with the time course of neural activity in the interlocutor's auditory cortex. This shows the feasibility of the new methodology, which may help elucidate basic reciprocal mechanisms of social interaction and the underpinnings of disordered communication. In particular, it may serve to study the process of psychotherapy on a neuronal level. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. Robust outer synchronization between two complex networks with fractional order dynamics.

    Science.gov (United States)

    Asheghan, Mohammad Mostafa; Míguez, Joaquín; Hamidi-Beheshti, Mohammad T; Tavazoei, Mohammad Saleh

    2011-09-01

    Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider two systems consisting of interconnected nodes. The state variables of each node evolve with time according to a set of (possibly nonlinear and chaotic) fractional-order differential equations. One of the networks plays the role of a master system and drives the second network by way of an open-plus-closed-loop (OPCL) scheme. Starting from a simple analysis of the synchronization error and a basic lemma on the eigenvalues of matrices resulting from Kronecker products, we establish various sets of conditions for outer synchronization, i.e., for ensuring that the errors between the state variables of the master and response systems can asymptotically vanish with time. Then, we address the problem of robust outer synchronization, i.e., how to guarantee that the states of the nodes converge to common values when the parameters of the master and response networks are not identical, but present some perturbations. Assuming that these perturbations are bounded, we also find conditions for outer synchronization, this time given in terms of sets of linear matrix inequalities (LMIs). Most of the analytical results in this paper are valid both for fractional-order and integer-order dynamics. The assumptions on the inner (coupling) structure of the networks are mild, involving, at most, symmetry and diffusivity. The analytical results are complemented with numerical examples. In particular, we show examples of generalized and robust outer synchronization for networks whose nodes are governed by fractional-order Lorenz dynamics.

  10. Cooperative Control of Heterogeneous Uncertain Dynamical Networks: An Adaptive Explicit Synchronization Framework.

    Science.gov (United States)

    Wang, Bohui; Wang, Jingcheng; Zhang, Langwen; Zhang, Bin; Li, Xiaocheng

    2017-06-01

    This paper proposes an adaptive explicit synchronization framework to address the cooperative control for heterogeneous uncertain dynamical networks under switching communication topologies. The main contribution is to develop an adaptive explicit synchronization algorithm, in which the synchronization state can be completely tracked by each agent in real time rather than only be measured after the synchronization process of all agents is over. By introducing appropriate assumptions, a class of adaptive explicit synchronization protocols is designed by using a combination of the virtual leader's states, the neighboring agents' relative information, distributed feedback gain, and distributed average weighted parameters. It is proved in the sense of Lyapunov that, if the dwell time is larger than a positive threshold, the cooperative control problem for the closed-loop heterogeneous uncertain dynamical networks under switching of strongly-connected communication topologies can be solved by the proposed adaptive explicit synchronization algorithm. Furthermore, by assuming that the topology is frequently strongly-connected, it shows that intermittent adaptive explicit synchronization can be achieved with well-designed control parameters. Two examples are presented to demonstrate the effectiveness of the proposed theory.

  11. Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions.

    Science.gov (United States)

    Ding, Xiaoshuai; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E; Hayat, Tasawar

    2017-06-01

    This paper is concerned with the fixed-time synchronization for a class of complex-valued neural networks in the presence of discontinuous activation functions and parameter uncertainties. Fixed-time synchronization not only claims that the considered master-slave system realizes synchronization within a finite time segment, but also requires a uniform upper bound for such time intervals for all initial synchronization errors. To accomplish the target of fixed-time synchronization, a novel feedback control procedure is designed for the slave neural networks. By means of the Filippov discontinuity theories and Lyapunov stability theories, some sufficient conditions are established for the selection of control parameters to guarantee synchronization within a fixed time, while an upper bound of the settling time is acquired as well, which allows to be modulated to predefined values independently on initial conditions. Additionally, criteria of modified controller for assurance of fixed-time anti-synchronization are also derived for the same system. An example is included to illustrate the proposed methodologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Distributed synchronization of networked drive-response systems: A nonlinear fixed-time protocol.

    Science.gov (United States)

    Zhao, Wen; Liu, Gang; Ma, Xi; He, Bing; Dong, Yunfeng

    2017-11-01

    The distributed synchronization of networked drive-response systems is investigated in this paper. A novel nonlinear protocol is proposed to ensure that the tracking errors converge to zeros in a fixed-time. By comparison with previous synchronization methods, the present method considers more practical conditions and the synchronization time is not dependent of arbitrary initial conditions but can be offline pre-assign according to the task assignment. Finally, the feasibility and validity of the presented protocol have been illustrated by a numerical simulation. Copyright © 2017. Published by Elsevier Ltd.

  13. Synchronization of delayed complex networks via intermittent control with non-period

    Science.gov (United States)

    Liang, Yi; Qi, Xiaolong; Wei, Qiang

    2018-02-01

    In this paper, a pinning synchronization scheme of nonlinear coupled complex networks is investigated via non-periodically intermittent control method, in which dynamical system is delayed nonlinear system, and its coupling matrices can be nonsymmetric. In the case that control ratio of control width to total time width is equal in any time interval, the control scheme is studied, and some sufficient conditions are given to ensure global exponential synchronization. Furthermore, two main corollaries are derived. At last, numerical simulations show effectiveness of the synchronization scheme.

  14. Exponential cluster synchronization in directed community networks via adaptive nonperiodically intermittent pinning control

    Science.gov (United States)

    Zhou, Peipei; Cai, Shuiming; Jiang, Shengqin; Liu, Zengrong

    2018-02-01

    In this paper, the problem of exponential cluster synchronization for a class of directed community networks is investigated via adaptive nonperiodically intermittent pinning control. By constructing a novel piecewise continuous Lyapunov function, some sufficient conditions to guarantee globally exponential cluster synchronization are derived. It is noted that the derived cluster synchronization criteria rely on the control rates, but not the control widths or the control periods, which facilitates the choice of the control periods in practical applications. A numerical example is finally presented to show the effectiveness of the obtained theoretical results.

  15. Synchronization-based parameter estimation of fractional-order neural networks

    Science.gov (United States)

    Gu, Yajuan; Yu, Yongguang; Wang, Hu

    2017-10-01

    This paper focuses on the parameter estimation problem of fractional-order neural network. By combining the adaptive control and parameter update law, we generalize the synchronization-based identification method that has been reported in several literatures on identifying unknown parameters of integer-order systems. With this method, parameter identification and synchronization can be achieved simultaneously. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.

  16. Synchronization-optimized networks for coupled nearly identical ...

    Indian Academy of Sciences (India)

    2014-01-24

    Jan 24, 2014 ... The extension of the master stability function (MSF) to analyse stability of generalized synchronization for coupled nearly identical oscillators is discussed. The nearly identical nature of the coupled oscillators is due to some parameter mismatch while the dynamical equations are the same for all the ...

  17. EEG classification of emotions using emotion-specific brain functional network.

    Science.gov (United States)

    Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

    2015-08-01

    The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

  18. An efficient algorithm for computing attractors of synchronous and asynchronous Boolean networks.

    Science.gov (United States)

    Zheng, Desheng; Yang, Guowu; Li, Xiaoyu; Wang, Zhicai; Liu, Feng; He, Lei

    2013-01-01

    Biological networks, such as genetic regulatory networks, often contain positive and negative feedback loops that settle down to dynamically stable patterns. Identifying these patterns, the so-called attractors, can provide important insights for biologists to understand the molecular mechanisms underlying many coordinated cellular processes such as cellular division, differentiation, and homeostasis. Both synchronous and asynchronous Boolean networks have been used to simulate genetic regulatory networks and identify their attractors. The common methods of computing attractors are that start with a randomly selected initial state and finish with exhaustive search of the state space of a network. However, the time complexity of these methods grows exponentially with respect to the number and length of attractors. Here, we build two algorithms to achieve the computation of attractors in synchronous and asynchronous Boolean networks. For the synchronous scenario, combing with iterative methods and reduced order binary decision diagrams (ROBDD), we propose an improved algorithm to compute attractors. For another algorithm, the attractors of synchronous Boolean networks are utilized in asynchronous Boolean translation functions to derive attractors of asynchronous scenario. The proposed algorithms are implemented in a procedure called geneFAtt. Compared to existing tools such as genYsis, geneFAtt is significantly [Formula: see text] faster in computing attractors for empirical experimental systems. The software package is available at https://sites.google.com/site/desheng619/download.

  19. Cross-Frequency Coupling in Real and Virtual Brain Networks

    Directory of Open Access Journals (Sweden)

    Viktor eJirsa

    2013-07-01

    Full Text Available Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC. It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of cross-frequency coupling under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii bispectrum and bicoherence correctly detect the CFCs in simulated data, (iii empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic bispectrum and bicoherence. This coupling was mostly asymmetric (directed and generally higher in the eyes-closed than in the eyes-open condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed.

  20. Cross-frequency coupling in real and virtual brain networks

    Science.gov (United States)

    Jirsa, Viktor; Müller, Viktor

    2013-01-01

    Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed. PMID:23840188

  1. Synchronous Infra-Slow Bursting in the Mouse Accessory Olfactory Bulb Emerge from Interplay between Intrinsic Neuronal Dynamics and Network Connectivity.

    Science.gov (United States)

    Zylbertal, Asaph; Yarom, Yosef; Wagner, Shlomo

    2017-03-08

    Rhythmic neuronal activity of multiple frequency bands has been described in many brain areas and attributed to numerous brain functions. Among these, little is known about the mechanism and role of infra-slow oscillations, which have been demonstrated recently in the mouse accessory olfactory bulb (AOB). Along with prolonged responses to stimuli and distinct network connectivity, they inexplicably affect the AOB processing of social relevant stimuli. Here, we show that assemblies of AOB mitral cells are synchronized by lateral interactions through chemical and electrical synapses. Using a network model, we demonstrate that the synchronous oscillations in these assemblies emerge from interplay between intrinsic membrane properties and network connectivity. As a consequence, the AOB network topology, in which each mitral cell receives input from multiple glomeruli, enables integration of chemosensory stimuli over extended time scales by interglomerular synchrony of infra-slow bursting. These results provide a possible functional significance for the distinct AOB physiology and topology. Beyond the AOB, this study presents a general model for synchronous infra-slow bursting in neuronal networks.SIGNIFICANCE STATEMENT Infra-slow rhythmic neuronal activity with a very long (>10 s) duration has been described in many brain areas, but little is known about the role of this activity and the mechanisms that produce it. Here, we combine experimental and computational methods to show that synchronous infra-slow bursting activity in mitral cells of the mouse accessory olfactory bulb (AOB) emerges from interplay between intracellular dynamics and network connectivity. In this novel mechanism, slow intracellular Na+ dynamics endow AOB mitral cells with a weak tendency to burst, which is further enhanced and stabilized by chemical and electrical synapses between them. Combined with the unique topology of the AOB network, infra-slow bursting enables integration and binding of

  2. Scaling in topological properties of brain networks

    NARCIS (Netherlands)

    Singh, S.S.; Khundrakpam, B.S.; Reid, A.T.; Lewis, J.D.; Evans, A.C.; Ishrat, R.; Sharma, B.I.; Singh, R.K.B.

    2016-01-01

    The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks.

  3. Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks.

    Science.gov (United States)

    Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

    2017-05-01

    In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay pdelay, whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

  4. Cluster synchronization of community network with distributed time delays via impulsive control

    Science.gov (United States)

    Leng, Hui; Wu, Zhao-Yan

    2016-11-01

    Cluster synchronization is an important dynamical behavior in community networks and deserves further investigations. A community network with distributed time delays is investigated in this paper. For achieving cluster synchronization, an impulsive control scheme is introduced to design proper controllers and an adaptive strategy is adopted to make the impulsive controllers unified for different networks. Through taking advantage of the linear matrix inequality technique and constructing Lyapunov functions, some synchronization criteria with respect to the impulsive gains, instants, and system parameters without adaptive strategy are obtained and generalized to the adaptive case. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results. Project supported by the National Natural Science Foundation of China (Grant No. 61463022), the Natural Science Foundation of Jiangxi Province, China (Grant No. 20161BAB201021), and the Natural Science Foundation of Jiangxi Educational Committee, China (Grant No. GJJ14273).

  5. Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    Huaiqin Wu

    2013-01-01

    Full Text Available This paper investigates the complete periodic synchronization of memristor-based neural networks with time-varying delays. Firstly, under the framework of Filippov solutions, by using M-matrix theory and the Mawhin-like coincidence theorem in set-valued analysis, the existence of the periodic solution for the network system is proved. Secondly, complete periodic synchronization is considered for memristor-based neural networks. According to the state-dependent switching feature of the memristor, the error system is divided into four cases. Adaptive controller is designed such that the considered model can realize global asymptotical synchronization. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.

  6. Noise and Synchronization Analysis of the Cold-Receptor Neuronal Network Model

    Directory of Open Access Journals (Sweden)

    Ying Du

    2014-01-01

    Full Text Available This paper analyzes the dynamics of the cold receptor neural network model. First, it examines noise effects on neuronal stimulus in the model. From ISI plots, it is shown that there are considerable differences between purely deterministic simulations and noisy ones. The ISI-distance is used to measure the noise effects on spike trains quantitatively. It is found that spike trains observed in neural models can be more strongly affected by noise for different temperatures in some aspects; meanwhile, spike train has greater variability with the noise intensity increasing. The synchronization of neuronal network with different connectivity patterns is also studied. It is shown that chaotic and high period patterns are more difficult to get complete synchronization than the situation in single spike and low period patterns. The neuronal network will exhibit various patterns of firing synchronization by varying some key parameters such as the coupling strength. Different types of firing synchronization are diagnosed by a correlation coefficient and the ISI-distance method. The simulations show that the synchronization status of neurons is related to the network connectivity patterns.

  7. FPGA based, modular, configurable controller with fast synchronous optical network

    Energy Technology Data Exchange (ETDEWEB)

    Graczyk, R.; Pozniak, K.T.; Romaniuk, R.S. [Warsaw Univ. of Technology (Poland). Inst. of Electronic Systems

    2006-07-01

    The paper describes a configurable controller equipped with programmable VLSI FPGA circuit, universal expansion modules PMC, synchronous, optical, multi-gigabit links, commonly used industrial and computer communication interfaces, Ethernet 100TB, system of automatic initialization ACE etc. There are characterized the basic functional characteristics of the device. The possibilities of its usage in various work modes were presented. Realization of particular blocks of the device were discussed. Resulting, during the realization of this project, new hardware layer solutions were also characterized. (orig.)

  8. Effective Synchronization of EEG and EMG for Mobile Brain/Body Imaging in Clinical Settings

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    Fiorenzo Artoni

    2018-01-01

    Full Text Available Mobile Brain/Body Imaging (MoBI is rapidly gaining traction as a new imaging modality to study how cognitive processes support locomotion. Electroencephalogram (EEG and electromyogram (EMG, due to their time resolution, non-invasiveness and portability are the techniques of choice for MoBI, but synchronization requirements among others restrict its use to high-end research facilities. Here we test the effectiveness of a technique that enables us to achieve MoBI-grade synchronization of EEG and EMG, even when other strategies (such as Lab Streaming Layer (LSL cannot be used e.g., due to the unavailability of proprietary Application Programming Interfaces (APIs, which is often the case in clinical settings. The proposed strategy is that of aligning several spikes at the beginning and end of the session. We delivered a train of spikes to the EEG amplifier and EMG electrodes every 2 s over a 10-min time period. We selected a variable number of spikes (from 1 to 10 both at the beginning and end of the time series and linearly resampled the data so as to align them. We then compared the misalignment of the “middle” spikes over the whole recording to test for jitter and synchronization drifts, highlighting possible nonlinearities (due to hardware filters and estimated the maximum length of the recording to achieve a [−5 to 5] ms misalignment range. We demonstrate that MoBI-grade synchronization can be achieved within 10-min recordings with a 1.7 ms jitter and [−5 5] ms misalignment range. We show that repeated spike delivery can be used to test online synchronization options and to troubleshoot synchronization issues over EEG and EMG. We also show that synchronization cannot rely only on the equipment sampling rate advertised by manufacturers. The synchronization strategy described can be used virtually in every clinical environment, and may increase the interest among a broader spectrum of clinicians and researchers in the MoBI framework

  9. Pain: A Distributed Brain Information Network?

    Science.gov (United States)

    Mano, Hiroaki; Seymour, Ben

    2015-01-01

    Understanding how pain is processed in the brain has been an enduring puzzle, because there doesn't appear to be a single “pain cortex” that directly codes the subjective perception of pain. An emerging concept is that, instead, pain might emerge from the coordinated activity of an integrated brain network. In support of this view, Woo and colleagues present evidence that distinct brain networks support the subjective changes in pain that result from nociceptive input and self-directed cognitive modulation. This evidence for the sensitivity of distinct neural subsystems to different aspects of pain opens up the way to more formal computational network theories of pain. PMID:25562782

  10. Why do we fall into sync with others? Interpersonal synchronization and the brain's optimization principle.

    Science.gov (United States)

    Koban, Leonie; Ramamoorthy, Anand; Konvalinka, Ivana

    2017-11-08

    Spontaneous interpersonal synchronization of rhythmic behavior such as gait or hand clapping is a ubiquitous phenomenon in human interactions, and is potentially important for social relationships and action understanding. Although several authors have suggested a role of the mirror neuron system in interpersonal coupling, the underlying brain mechanisms are not well understood. Here we argue that more general theories of neural computations, namely predictive coding and the Free Energy Principle, could explain interpersonal coordination dynamics. Each brain minimizes coding costs by reducing the mismatch between the representations of observed and own motor behavior. Continuous mutual prediction and alignment result in an overall minimization of free energy, thus forming a stable attractor state.

  11. Synchronicity from Synchronized Chaos

    Directory of Open Access Journals (Sweden)

    Gregory S. Duane

    2015-03-01

    Full Text Available The synchronization of loosely-coupled chaotic oscillators, a phenomenon investigated intensively for the last two decades, may realize the philosophical concept of “synchronicity”—the commonplace notion that related eventsmysteriously occur at the same time. When extended to continuous media and/or large discrete arrays, and when general (non-identical correspondences are considered between states, intermittent synchronous relationships indeed become ubiquitous. Meaningful synchronicity follows naturally if meaningful events are identified with coherent structures, defined by internal synchronization between remote degrees of freedom; a condition that has been posited as necessary for synchronizability with an external system. The important case of synchronization between mind and matter is realized if mind is analogized to a computer model, synchronizing with a sporadically observed system, as in meteorological data assimilation. Evidence for the ubiquity of synchronization is reviewed along with recent proposals that: (1 synchronization of different models of the same objective process may be an expeditious route to improved computational modeling and may also describe the functioning of conscious brains; and (2 the nonlocality in quantum phenomena implied by Bell’s theorem may be explained in a variety of deterministic (hidden variable interpretations if the quantum world resides on a generalized synchronization “manifold”.

  12. Brain network science needs to become predictive. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    Science.gov (United States)

    Hilgetag, Claus C.; von Luxburg, Ulrike

    2014-09-01

    In his thought-provoking review of current concepts in neuroscience, Pessoa [1] addresses the ongoing paradigm shift of the field, in which the perspective has moved from individual nodes to distributed networks in order to account for distributed brain function. Within this perspective, Pessoa describes diverse aspects and topological features of brain networks that are potentially relevant for brain function. As he notes, however, the shift to networks does not solve all problems of linking brain function to structure.

  13. Graph Theory-Based Pinning Synchronization of Stochastic Complex Dynamical Networks.

    Science.gov (United States)

    Li, Xiao-Jian; Yang, Guang-Hong

    2017-02-01

    This paper is concerned with the adaptive pinning synchronization problem of stochastic complex dynamical networks (CDNs). Based on algebraic graph theory and Lyapunov theory, pinning controller design conditions are derived, and the rigorous convergence analysis of synchronization errors in the probability sense is also conducted. Compared with the existing results, the topology structures of stochastic CDN are allowed to be unknown due to the use of graph theory. In particular, it is shown that the selection of nodes for pinning depends on the unknown lower bounds of coupling strengths. Finally, an example on a Chua's circuit network is given to validate the effectiveness of the theoretical results.

  14. On business cycles synchronization in Europe: A note on network analysis

    Science.gov (United States)

    Matesanz, David; Ortega, Guillermo J.

    2016-11-01

    In this paper we examine synchronization in European business cycles from 1950 to 2013. Herein we further investigate previous and controversial results that arise from complex network analysis of this topic. By focusing on the importance of different configurations in the commonly used rolling windows and threshold significance levels, we find that selections are critical to obtaining accurate networks. Output co-movement and connectivity show no appreciable changes during the beginning of the Euro period, but rather dramatic jumps are observed since the outbreak of the global financial crisis. At this time, previous lead/lag effects disappeared and in-phase synchronization across Europe was observed.

  15. Three-Level Direct Torque Control Based on Artificial Neural Network of Double Star Synchronous Machine

    Directory of Open Access Journals (Sweden)

    Elakhdar BENYOUSSEF

    2014-02-01

    Full Text Available This paper presents a direct torque control strategy for double star synchronous machine fed by two three-level inverters. The analysis of the torque and the stator flux linkage reference frame shows that the concept of direct torque control can be extended easily to double star synchronous machine. The proposed approach consists to replace the switching tables by one artificial neural networks controller. The output switching states vectors of the artificial neural networks controller are used to control the two three-level inverters. Simulations results are given to show the effectiveness and the robustness of the suggested control method.

  16. The putative role of neuronal network synchronization as a potential biomarker for bipolar disorder: A review of EEG studies.

    Science.gov (United States)

    Maggioni, E; Bianchi, A M; Altamura, A C; Soares, Jair C; Brambilla, P

    2017-04-01

    Impaired intra-hemispheric and inter-hemispheric communication play a major role in the pathophysiology and cognitive disturbances of bipolar disorder (BD). Brain connectivity in BD has been largely investigated using magnetic resonance imaging (MRI) techniques, which have found alterations in prefronto-limbic coupling. In contrast, evidence for functional neural circuitry abnormalities in BD is less consistent. Indeed, just a few studies employing the electroencephalographic (EEG) technique, enabling the exploration of oscillatory brain dynamics, addressed this issue. Therefore, in the present review we summarize the results from EEG studies examining connectivity in patients with BD, to further clarify the putative role of neuronal network synchronization as a potential biomarker of this disabling mental illness. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

    Science.gov (United States)

    Gong, Yubing; Wang, Baoying; Xie, Huijuan

    2016-12-01

    In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate Ap of STDP and become strongest at a certain Ap value, and the Ap value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when Ap increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  18. Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm.

    Science.gov (United States)

    Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo

    2015-07-28

    In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

  19. H ∞ Cluster Synchronization for a Class of Neutral Complex Dynamical Networks with Markovian Switching

    Science.gov (United States)

    2014-01-01

    H ∞ cluster synchronization problem for a class of neutral complex dynamical networks (NCDNs) with Markovian switching is investigated in this paper. Both the retarded and neutral delays are considered to be interval mode dependent and time varying. The concept of H ∞ cluster synchronization is proposed to quantify the attenuation level of synchronization error dynamics against the exogenous disturbance of the NCDNs. Based on a novel Lyapunov functional, by employing some integral inequalities and the nature of convex combination, mode delay-range-dependent H ∞ cluster synchronization criteria are derived in the form of linear matrix inequalities which depend not only on the disturbance attenuation but also on the initial values of the NCDNs. Finally, numerical examples are given to demonstrate the feasibility and effectiveness of the proposed theoretical results. PMID:24892088

  20. Global Phase Synchronization for a Class of Dynamical Complex Networks with Time-Varying Coupling Delays

    Directory of Open Access Journals (Sweden)

    Li XinBin

    2010-01-01

    Full Text Available Global phase synchronization for a class of dynamical complex networks composed of multiinput multioutput pendulum-like systems with time-varying coupling delays is investigated. The problem of the global phase synchronization for the complex networks is equivalent to the problem of the asymptotical stability for the corresponding error dynamical networks. For reducing the conservation, no linearization technique is involved, but by Kronecker product, the problem of the asymptotical stability of the high dimensional error dynamical networks is reduced to the same problem of a class of low dimensional error systems. The delay-dependent criteria guaranteeing global asymptotical stability for the error dynamical complex networks in terms of Liner Matrix Inequalities (LMIs are derived based on free-weighting matrices technique and Lyapunov function. According to the convex characterization, a simple criterion is proposed. A numerical example is provided to demonstrate the effectiveness of the proposed results.

  1. General scaling of maximum degree of synchronization in noisy complex networks

    Science.gov (United States)

    Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

    2014-11-01

    The effects of white noise and global coupling strength on the maximum degree of synchronization in complex networks are explored. We perform numerical simulations of generic oscillator models with both linear and non-linear coupling functions on a broad spectrum of network topologies. The oscillator models include the Fitzhugh-Nagumo model, the Izhikevich model and the Kuramoto phase oscillator model. The network topologies range from regular, random and highly modular networks to scale-free and small-world networks, with both directed and undirected edges. We then study the dependency of the maximum degree of synchronization on the global coupling strength and the noise intensity. We find a general scaling of the synchronizability, and quantify its validity by fitting a regression model to the numerical data.

  2. Impact of Loss Synchronization on Reliable High Speed Networks: A Model Based Simulation

    Directory of Open Access Journals (Sweden)

    Suman Kumar

    2014-01-01

    Full Text Available Contemporary nature of network evolution demands for simulation models which are flexible, scalable, and easily implementable. In this paper, we propose a fluid based model for performance analysis of reliable high speed networks. In particular, this paper aims to study the dynamic relationship between congestion control algorithms and queue management schemes, in order to develop a better understanding of the causal linkages between the two. We propose a loss synchronization module which is user configurable. We validate our model through simulations under controlled settings. Also, we present a performance analysis to provide insights into two important issues concerning 10 Gbps high speed networks: (i impact of bottleneck buffer size on the performance of 10 Gbps high speed network and (ii impact of level of loss synchronization on link utilization-fairness tradeoffs. The practical impact of the proposed work is to provide design guidelines along with a powerful simulation tool to protocol designers and network developers.

  3. An adaptive complex network model for brain functional networks.

    Directory of Open Access Journals (Sweden)

    Ignacio J Gomez Portillo

    Full Text Available Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.

  4. Multilayer motif analysis of brain networks

    Science.gov (United States)

    Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

    2017-04-01

    In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

  5. Effects of Methylphenidate on Default-Mode Network/Task-Positive Network Synchronization in Children With ADHD.

    Science.gov (United States)

    Querne, Laurent; Fall, Sidy; Le Moing, Anne-Gaëlle; Bourel-Ponchel, Emilie; Delignières, Aline; Simonnot, Anais; de Broca, Alain; Gondry-Jouet, Catherine; Boucart, Muriel; Berquin, Patrick

    2017-12-01

    A failure of the anti-phase synchronization between default-mode (DMN) and task-positive networks (TPN) may be involved in a main manifestation of ADHD: moment-to-moment variability. The study investigated whereby methylphenidate may improve TPN/DMN synchronization in ADHD. Eleven drug-naive ADHD children and 11 typically developing (TD) children performed a flanker task during functional magnetic resonance imaging. The ADHD group was scanned without and 1 month later with methylphenidate. The signal was analyzed by independent component analysis. The TD group showed anti-phase DMN/TPN synchronization. The unmedicated ADHD group showed synchronous activity in the posterior DMN only, which was positively correlated with response time variability for the flanker task. Methylphenidate initiated a partial anti-phase TPN/DMN synchronization, reduced variability, and abolished the variability/DMN correlation. Although results should be interpreted cautiously because the sample size is small, they suggest that a failure of the TPN/DMN synchronization could be involved in the moment-to-moment variability in ADHD. Methylphenidate initiated TPN/DMN synchronization, which in turn appeared to reduce variability.

  6. Nicotine increases brain functional network efficiency.

    Science.gov (United States)

    Wylie, Korey P; Rojas, Donald C; Tanabe, Jody; Martin, Laura F; Tregellas, Jason R

    2012-10-15

    Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. Published by Elsevier Inc.

  7. Unified synchronization criteria in an array of coupled neural networks with hybrid impulses.

    Science.gov (United States)

    Wang, Nan; Li, Xuechen; Lu, Jianquan; Alsaadi, Fuad E

    2018-02-07

    This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be effective for impulsively coupled neural networks simultaneously with synchronizing impulses and desynchronizing impulses, and we do not need to discuss these two kinds of impulses separately. Moreover, by using our average impulsive interval method, we can obtain an interesting and valuable result for the case of average impulsive interval T a =∞. For some sparse impulsive sequences with T a =∞, the impulses can happen for infinite number of times, but they do not have essential influence on the synchronization property of networks. Finally, numerical examples including scale-free networks are exploited to illustrate our theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. Adaptive control of dynamical synchronization on evolving networks with noise disturbances

    Science.gov (United States)

    Yuan, Wu-Jie; Zhou, Jian-Fang; Sendiña-Nadal, Irene; Boccaletti, Stefano; Wang, Zhen

    2018-02-01

    In real-world networked systems, the underlying structure is often affected by external and internal unforeseen factors, making its evolution typically inaccessible. An adaptive strategy was introduced for maintaining synchronization on unpredictably evolving networks [Sorrentino and Ott, Phys. Rev. Lett. 100, 114101 (2008), 10.1103/PhysRevLett.100.114101], which yet does not consider the noise disturbances widely existing in networks' environments. We provide here strategies to control dynamical synchronization on slowly and unpredictably evolving networks subjected to noise disturbances which are observed at the node and at the communication channel level. With our strategy, the nodes' coupling strength is adaptively adjusted with the aim of controlling synchronization, and according only to their received signal and noise disturbances. We first provide a theoretical analysis of the control scheme by introducing an error potential function to seek for the minimization of the synchronization error. Then, we show numerical experiments which verify our theoretical results. In particular, it is found that our adaptive strategy is effective even for the case in which the dynamics of the uncontrolled network would be explosive (i.e., the states of all the nodes would diverge to infinity).

  9. Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators

    Science.gov (United States)

    Papadopoulos, Lia; Kim, Jason Z.; Kurths, Jürgen; Bassett, Danielle S.

    2017-07-01

    Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior, and it is interesting to ask how the structural organization of network interactions influences this process. Several studies have explored and uncovered optimal topologies for synchronization by making purposeful alterations to a network. On the other hand, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. However, this co-evolution and the extent to which the dynamics of the individual units can shape the organization of the network itself are less well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. In particular, the system employs a co-evolutionary rewiring strategy that depends only on the instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple rule—which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators—can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding a dependence on the intrinsic frequencies of the oscillators, the global coupling, and the network density, in terms of how the adaptive mechanism reorganizes the network and influences the dynamics. Importantly, for large enough coupling and after sufficient adaptation, the resulting networks exhibit interesting characteristics, including degree-frequency and frequency-neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions in which the networks were constructed using global information. On the contrary, by

  10. Brain networks underlying novel metaphor production.

    Science.gov (United States)

    Beaty, Roger E; Silvia, Paul J; Benedek, Mathias

    2017-02-01

    Metaphors are widely used to convey abstract concepts and emotions in the arts and everyday life. Neuroimaging research suggests that dynamic interactions among large-scale brain networks, including the default and executive control networks, support the production of such creative ideas. However, the extent to which these networks interact to support other forms of creative language production such as metaphor remains unknown. Using functional magnetic resonance imaging (fMRI), we explored this question by assessing functional interactions between brain regions during novel metaphor production. Whole-brain functional connectivity analysis revealed a distributed network associated with metaphor production, including several nodes of the default (precuneus and left angular gyrus; AG) and executive control (right intraparietal sulcus; IPS) networks. Seed-based analyses showed increased connectivity between these network hubs, and temporal connectivity analysis found early coupling of default (left AG) and salience (right anterior insula) regions that preceded later coupling of the left AG and left DLPFC, pointing to a potential switching mechanism underlying default and executive network interaction. The results extend recent work on the cooperative role of large-scale networks in creative cognition, and suggest that metaphor production involves similar brain network dynamics as other forms of goal-directed, self-generated cognition. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. The development of brain network architecture

    NARCIS (Netherlands)

    Wierenga, Lara M.; van den Heuvel, Martijn P.; van Dijk, Sarai; Rijks, Yvonne; de Reus, Marcel A.; Durston, Sarah

    2016-01-01

    Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes

  12. Local and global synchronization transitions induced by time delays in small-world neuronal networks with chemical synapses.

    Science.gov (United States)

    Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile

    2015-02-01

    Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.

  13. Water diffusion reveals networks that modulate multiregional morphological plasticity after repetitive brain stimulation.

    Science.gov (United States)

    Abe, Mitsunari; Fukuyama, Hidenao; Mima, Tatsuya

    2014-03-25

    Repetitive brain stimulation protocols induce plasticity in the stimulated site in brain slice models. Recent evidence from network models has indicated that additional plasticity-related changes occur in nonstimulated remote regions. Despite increasing use of brain stimulation protocols in experimental and clinical settings, the neural substrates underlying the additional effects in remote regions are unknown. Diffusion-weighted MRI (DWI) probes water diffusion and can be used to estimate morphological changes in cortical tissue that occur with the induction of plasticity. Using DWI techniques, we estimated morphological changes induced by application of repetitive transcranial magnetic stimulation (rTMS) over the left primary motor cortex (M1). We found that rTMS altered water diffusion in multiple regions including the left M1. Notably, the change in water diffusion was retained longest in the left M1 and remote regions that had a correlation of baseline fluctuations in water diffusion before rTMS. We conclude that synchronization of water diffusion at rest between stimulated and remote regions ensures retention of rTMS-induced changes in water diffusion in remote regions. Synchronized fluctuations in the morphology of cortical microstructures between stimulated and remote regions might identify networks that allow retention of plasticity-related morphological changes in multiple regions after brain stimulation protocols. These results increase our understanding of the effects of brain stimulation-induced plasticity on multiregional brain networks. DWI techniques could provide a tool to evaluate treatment effects of brain stimulation protocols in patients with brain disorders.

  14. Voltage Control of PM Synchronous Motor Driven PM Synchronous Generator System Using Recurrent Wavelet Neural Network Controller

    Directory of Open Access Journals (Sweden)

    C.H. Lin

    2013-04-01

    Full Text Available In this paper the two novel recurrent wavelet neural network (RWNN controllers are proposed for controlling output direct current (DC voltage of the rectifier and output alternate current (AC voltage of the inverter. The output power of the rectifier and the inverter is provided by three-phase permanent magnet synchronous generator (PMSG system directly-driven by permanent magnet synchronous motor (PMSM. Firstly, the field-oriented mechanism is implemented for controlling output of the PMSG system. Then, one RWNN controller is developed for controlling rectifier to convert AC voltage into DC link voltage and the other RWNN controller is implemented for controlling inverter to convert DC link voltage into AC line voltage. Moreover, two online trained RWNNs using backpropagation learning algorithms are developed for regulating both the DC link voltage of the rectifier and the AC line voltage of the inverter. Finally, the effectiveness and advantages of the proposed two RWNN controllers are demonstrated in comparison with the two PI controllers from some experimental results.

  15. A scheme of de-synchronization in globally coupled neural networks and its possible implications for vagus nerve stimulation

    Energy Technology Data Exchange (ETDEWEB)

    Li Yanlong [Institute of Theoretical Physics, Lanzhou University of Technology, Lanzhou 730050 (China) and Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)], E-mail: liyl20031@126.com; Wu Min; Ma Jun [Institute of Theoretical Physics, Lanzhou University of Technology, Lanzhou 730050 (China); Chen Zhaoyang [Department of Chemistry, George Washington University, Washington, DC 20052 (United States); Wang Yinghai [Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)

    2009-02-15

    A scheme of de-synchronization via pulse stimulation is numerically investigated in the Hindmarsh Rose globally coupled neural networks. The simulations show that synchronization evolves into de-synchronization in the globally coupled HR neural network when a part (about 10%) of neurons are stimulated with a pulse current signal. The network de-synchronization appears to be sensitive to the stimulation parameters. For the case of the same stimulation intensity, those weakly coupled networks reach de-synchronization more easily than strongly coupled networks. There exists a homologous asymptotic behavior in the region of higher frequency, and exist the optimal stimulation interval and period of continuous stimulation time when other stimulation parameters remain invariable.

  16. Synchronization of

    National Research Council Canada - National Science Library

    Schrader, Jared M; Shapiro, Lucy

    2015-01-01

    .... Cell cycle synchronization experiments have been used to establish the molecular events governing chromosome replication and segregation, to map a genetic regulatory network controlling cell cycle...

  17. Collective frequency variation in network synchronization and reverse PageRank

    Science.gov (United States)

    Skardal, Per Sebastian; Taylor, Dane; Sun, Jie; Arenas, Alex

    2016-04-01

    A wide range of natural and engineered phenomena rely on large networks of interacting units to reach a dynamical consensus state where the system collectively operates. Here we study the dynamics of self-organizing systems and show that for generic directed networks the collective frequency of the ensemble is not the same as the mean of the individuals' natural frequencies. Specifically, we show that the collective frequency equals a weighted average of the natural frequencies, where the weights are given by an outflow centrality measure that is equivalent to a reverse PageRank centrality. Our findings uncover an intricate dependence of the collective frequency on both the structural directedness and dynamical heterogeneity of the network, and also reveal an unexplored connection between synchronization and PageRank, which opens the possibility of applying PageRank optimization to synchronization. Finally, we demonstrate the presence of collective frequency variation in real-world networks by considering the UK and Scandinavian power grids.

  18. Collective frequency variation in network synchronization and reverse PageRank.

    Science.gov (United States)

    Skardal, Per Sebastian; Taylor, Dane; Sun, Jie; Arenas, Alex

    2016-04-01

    A wide range of natural and engineered phenomena rely on large networks of interacting units to reach a dynamical consensus state where the system collectively operates. Here we study the dynamics of self-organizing systems and show that for generic directed networks the collective frequency of the ensemble is not the same as the mean of the individuals' natural frequencies. Specifically, we show that the collective frequency equals a weighted average of the natural frequencies, where the weights are given by an outflow centrality measure that is equivalent to a reverse PageRank centrality. Our findings uncover an intricate dependence of the collective frequency on both the structural directedness and dynamical heterogeneity of the network, and also reveal an unexplored connection between synchronization and PageRank, which opens the possibility of applying PageRank optimization to synchronization. Finally, we demonstrate the presence of collective frequency variation in real-world networks by considering the UK and Scandinavian power grids.

  19. Effects of glial release and somatic receptors on bursting in synchronized neuronal networks

    Science.gov (United States)

    Zhan, Xuan; Lai, Pik-Yin; Chan, C. K.

    2011-07-01

    A model is constructed to study the phenomenon of bursting in cultured neuronal networks by considering the effects of glial release and the extrasynaptic receptors on neurons. In the frequently observed situations of synchronized bursting, the whole neuronal network can be described by a mean-field model. In this model, the dynamics of the synchronized network in the presence of glia is represented by an effective two-compartment neuron with stimulations on both the dendrite and soma. Numerical simulations of this model show that most of the experimental observations in bursting, in particular the high plateau and the slow repolarization, can be reproduced. Our findings suggest that the effects of glia release and extrasynaptic receptors, which are usually neglected in neuronal models, can become important in intense network activities. Furthermore, simulations of the model are also performed for the case of glia-suppressed cultures to compare with recent experimental results.

  20. Reduced Synchronization Persistence in Neural Networks Derived from Atm-Deficient Mice

    Science.gov (United States)

    Levine-Small, Noah; Yekutieli, Ziv; Aljadeff, Jonathan; Boccaletti, Stefano; Ben-Jacob, Eshel; Barzilai, Ari

    2011-01-01

    Many neurodegenerative diseases are characterized by malfunction of the DNA damage response. Therefore, it is important to understand the connection between system level neural network behavior and DNA. Neural networks drawn from genetically engineered animals, interfaced with micro-electrode arrays allowed us to unveil connections between networks’ system level activity properties and such genome instability. We discovered that Atm protein deficiency, which in humans leads to progressive motor impairment, leads to a reduced synchronization persistence compared to wild type synchronization, after chemically imposed DNA damage. Not only do these results suggest a role for DNA stability in neural network activity, they also establish an experimental paradigm for empirically determining the role a gene plays on the behavior of a neural network. PMID:21519382

  1. Sub-Synchronous Interaction Analysis between DFIG Based Wind Farm and Series Compensated Network

    DEFF Research Database (Denmark)

    Wang, Yun; Wu, Qiuwei; Kang, Shaoli

    2016-01-01

    This paper analyzes the sub-synchronous interaction (SSI) phenomenon between the doubly fed induction generator (DFIG) based wind farm (WF) and the series capacitor compensated network. The possible types of SSI in the DFIG based WF are studied. The factors influencing the SSI of DFIG based WF...

  2. Global Stability of a Synchronous Regime in Hub Clusters of the Power Networks

    Science.gov (United States)

    Dmitrichev, A. S.; Zakharov, D. G.; Nekorkin, V. I.

    2017-11-01

    We study stability of a synchronous regime in hub clusters of the power networks, which are simulated by ensembles of phase oscillators. An approach allowing one to estimate the regions in the parameter space, which correspond to the global asymptotic stability of this regime, is presented. The method is illustrated by an example of a hub cluster consisting of one generator and two consumers.

  3. Synchronization in cross-docking networks : A research classification and framework

    NARCIS (Netherlands)

    Buijs, Paul; Vis, Iris F. A.; Carlo, Hector J.

    2014-01-01

    Cross-docking is a distribution strategy that enables the consolidation of less-than-truckload shipments into full truckloads without long-term storage. Due to the absence of a storage buffer inside a cross-dock, local and network-wide cross-docking operations need to be carefully synchronized. This

  4. Output synchronization for heterogeneous networks of introspective right-invertible agents

    NARCIS (Netherlands)

    Yang, Tao; Saberi, Ali; Stoorvogel, Antonie Arij; Grip, H°avard Fjær

    2014-01-01

    In this paper, we consider the output synchronization problem for heterogeneous networks of right-invertible linear agents. We assume that all the agents are introspective, meaning that they have access to their own local measurements. Under this assumption, we then propose a decentralized control

  5. Synchronization transitions induced by the fluctuation of adaptive coupling strength in delayed Newman-Watts neuronal networks.

    Science.gov (United States)

    Wang, Qi; Gong, Yubing; Wu, Yanan

    2015-11-01

    Introducing adaptive coupling in delayed neuronal networks and regulating the dissipative parameter (DP) of adaptive coupling by noise, we study the effect of fluctuations of the changing rate of adaptive coupling on the synchronization of the neuronal networks. It is found that time delay can induce synchronization transitions for intermediate DP values, and the synchronization transitions become strongest when DP is optimal. As the intensity of DP noise is varied, the neurons can also exhibit synchronization transitions, and the phenomenon is delay-dependent and is enhanced for certain time delays. Moreover, the synchronization transitions change with the change of DP and become strongest when DP is optimal. These results show that randomly changing adaptive coupling can considerably change the synchronization of the neuronal networks, and hence could play a crucial role in the information processing and transmission in neural systems. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  6. Inter-layer synchronization in non-identical multi-layer networks

    Science.gov (United States)

    Leyva, I.; Sevilla-Escoboza, R.; Sendiña-Nadal, I.; Gutiérrez, R.; Buldú, J. M.; Boccaletti, S.

    2017-04-01

    Inter-layer synchronization is a dynamical process occurring in multi-layer networks composed of identical nodes. This process emerges when all layers are synchronized, while nodes in each layer do not necessarily evolve in unison. So far, the study of such inter-layer synchronization has been restricted to the case in which all layers have an identical connectivity structure. When layers are not identical, the inter-layer synchronous state is no longer a stable solution of the system. Nevertheless, when layers differ in just a few links, an approximate treatment is still feasible, and allows one to gather information on whether and how the system may wander around an inter-layer synchronous configuration. We report the details of an approximate analytical treatment for a two-layer multiplex, which results in the introduction of an extra inertial term accounting for structural differences. Numerical validation of the predictions highlights the usefulness of our approach, especially for small or moderate topological differences in the intra-layer coupling. Moreover, we identify a non-trivial relationship connecting the betweenness centrality of the missing links and the intra-layer coupling strength. Finally, by the use of multiplexed layers of electronic circuits, we study the inter-layer synchronization as a function of the removed links.

  7. GTSO: Global Trace Synchronization and Ordering Mechanism for Wireless Sensor Network Monitoring Platforms.

    Science.gov (United States)

    Navia, Marlon; Campelo, José Carlos; Bonastre, Alberto; Ors, Rafael

    2017-12-23

    Monitoring is one of the best ways to evaluate the behavior of computer systems. When the monitored system is a distributed system-such as a wireless sensor network (WSN)-the monitoring operation must also be distributed, providing a distributed trace for further analysis. The temporal sequence of occurrence of the events registered by the distributed monitoring platform (DMP) must be correctly established to provide cause-effect relationships between them, so the logs obtained in different monitor nodes must be synchronized. Many of synchronization mechanisms applied to DMPs consist in adjusting the internal clocks of the nodes to the same value as a reference time. However, these mechanisms can create an incoherent event sequence. This article presents a new method to achieve global synchronization of the traces obtained in a DMP. It is based on periodic synchronization signals that are received by the monitor nodes and logged along with the recorded events. This mechanism processes all traces and generates a global post-synchronized trace by scaling all times registered proportionally according with the synchronization signals. It is intended to be a simple but efficient offline mechanism. Its application in a WSN-DMP demonstrates that it guarantees a correct ordering of the events, avoiding the aforementioned issues.

  8. Statistical analysis of the pulse-coupled synchronization strategy for wireless sensor networks

    Science.gov (United States)

    Wang, Yongqiang; Núñez, Felipe; Doyle, Francis J.

    2013-01-01

    Pulse-coupled synchronization is attracting increased attention in the sensor network community. Yet its properties have not been fully investigated. Using statistical analysis, we prove analytically that by controlling the number of connections at each node, synchronization can be guaranteed for generally pulse-coupled oscillators even in the presence of a refractory period. The approach does not require the initial phases to reside in half an oscillation cycle, which improves existing results. We also find that a refractory period can be strategically included to reduce idle listening at nearly no sacrifice to the synchronization probability. Given that reduced idle listening leads to higher energy efficiency in the synchronization process, the strategically added refractory period makes the synchronization scheme appealing to cheap sensor nodes, where energy is a precious system resource. We also analyzed the pulse-coupled synchronization in the presence of unreliable communication links and obtained similar results. QualNet experimental results are given to confirm the effectiveness of the theoretical predictions. PMID:24324322

  9. Cluster-Based Maximum Consensus Time Synchronization for Industrial Wireless Sensor Networks.

    Science.gov (United States)

    Wang, Zhaowei; Zeng, Peng; Zhou, Mingtuo; Li, Dong; Wang, Jintao

    2017-01-13

    Time synchronization is one of the key technologies in Industrial Wireless Sensor Networks (IWSNs), and clustering is widely used in WSNs for data fusion and information collection to reduce redundant data and communication overhead. Considering IWSNs' demand for low energy consumption, fast convergence, and robustness, this paper presents a novel Cluster-based Maximum consensus Time Synchronization (CMTS) method. It consists of two parts: intra-cluster time synchronization and inter-cluster time synchronization. Based on the theory of distributed consensus, the proposed method utilizes the maximum consensus approach to realize the intra-cluster time synchronization, and adjacent clusters exchange the time messages via overlapping nodes to synchronize with each other. A Revised-CMTS is further proposed to counteract the impact of bounded communication delays between two connected nodes, because the traditional stochastic models of the communication delays would distort in a dynamic environment. The simulation results show that our method reduces the communication overhead and improves the convergence rate in comparison to existing works, as well as adapting to the uncertain bounded communication delays.

  10. Structure and function of complex brain networks

    Science.gov (United States)

    Sporns, Olaf

    2013-01-01

    An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed. PMID:24174898

  11. Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements.

    Science.gov (United States)

    Kapucu, Fikret E; Välkki, Inkeri; Mikkonen, Jarno E; Leone, Chiara; Lenk, Kerstin; Tanskanen, Jarno M A; Hyttinen, Jari A K

    2016-01-01

    Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations and in vitro microelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied on in vitro MEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the

  12. Solving Problems in Software Applications through Data Synchronization in Case of Absence of the Network

    OpenAIRE

    Isak Shabani; Betim Cico; Agni Dika

    2012-01-01

    In this paper, we have presented an algorithm for data synchronization based on Web Services (WS), which allows software applications to work well on both configurations Online and "Offline", in the absence of the network. For this purpose is in use Electronic Student Management System (ESMS) at University of Prishtina (UP) with the appropriate module. Since the use of ESMS, because of a uncertain supply of electricity, disconnecting the network and for other reasons which are not under the c...

  13. A comment on "α-stability and α-synchronization for fractional-order neural networks".

    Science.gov (United States)

    Kexue, Li; Jigen, Peng; Jinghuai, Gao

    2013-12-01

    In this paper, we point out that an inequality in the paper [J. Yu, C. Hu, H. Jiang, α-stability and α-synchronization for fractional-order neural networks, Neural Networks 35 (2012) 82-87] is not correct. The main theorems in this paper are not valid, since they are proved by this inequality. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

    Science.gov (United States)

    Wan, Ying; Cao, Jinde; Wen, Guanghui

    In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

  15. Synchronization in Array of Coupled Neural Networks with Unbounded Distributed Delay and Limited Transmission Efficiency

    Directory of Open Access Journals (Sweden)

    Xinsong Yang

    2013-01-01

    Full Text Available This paper investigates global synchronization in an array of coupled neural networks with time-varying delays and unbounded distributed delays. In the coupled neural networks, limited transmission efficiency between coupled nodes, which makes the model more practical, is considered. Based on a novel integral inequality and the Lyapunov functional method, sufficient synchronization criteria are derived. The derived synchronization criteria are formulated by linear matrix inequalities (LMIs and can be easily verified by using Matlab LMI Toolbox. It is displayed that, when some of the transmission efficiencies are limited, the dynamics of the synchronized state are different from those of the isolated node. Furthermore, the transmission efficiency and inner coupling matrices between nodes play important roles in the final synchronized state. The derivative of the time-varying delay can be any given value, and the time-varying delay can be unbounded. The outer-coupling matrices can be symmetric or asymmetric. Numerical simulations are finally given to demonstrate the effectiveness of the theoretical results.

  16. Hierarchical modularity in human brain functional networks

    CERN Document Server

    Meunier, D; Fornito, A; Ersche, K D; Bullmore, E T; 10.3389/neuro.11.037.2009

    2010-01-01

    The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or "modules-within-modules") decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at ...

  17. Synchronized brain activity during rehearsal and short-term memory disruption by irrelevant speech is affected by recall mode.

    Science.gov (United States)

    Kopp, Franziska; Schröger, Erich; Lipka, Sigrid

    2006-08-01

    EEG coherence as a measure of synchronization of brain activity was used to investigate effects of irrelevant speech. In a delayed serial recall paradigm 21 healthy participants retained verbal items over a 10-s delay with and without interfering irrelevant speech. Recall after the delay was varied in two modes (spoken vs. written). Behavioral data showed the classic irrelevant speech effect and a superiority of written over spoken recall mode. Coherence, however, was more sensitive to processing characteristics and showed interactions between the irrelevant speech effect and recall mode during the rehearsal delay in theta (4-7.5 Hz), alpha (8-12 Hz), beta (13-20 Hz), and gamma (35-47 Hz) frequency bands. For gamma, a rehearsal-related decrease of the duration of high coherence due to presentation of irrelevant speech was found in a left-lateralized fronto-central and centro-temporal network only in spoken but not in written recall. In theta, coherence at predominantly fronto-parietal electrode combinations was indicative for memory demands and varied with individual working memory capacity assessed by digit span. Alpha coherence revealed similar results and patterns as theta coherence. In beta, a left-hemispheric network showed longer high synchronizations due to irrelevant speech only in written recall mode. EEG results suggest that mode of recall is critical for processing already during the retention period of a delayed serial recall task. Moreover, the finding that different networks are engaged with different recall modes shows that the disrupting effect of irrelevant speech is not a unitary mechanism.

  18. Dynamic Networks in the Emotional Brain.

    Science.gov (United States)

    Pessoa, Luiz; McMenamin, Brenton

    2016-10-25

    Research on the emotional brain has often focused on a few structures thought to be central to this type of processing-hypothalamus, amygdala, insula, and so on. Conceptual thinking about emotion has viewed this mental faculty as linked to broader brain circuits, too, including early ideas by Papez and others. In this article, we discuss research that embraces a distributed view of emotion circuits and efforts to unravel the impact on emotional manipulations on the processing of several large-scale brain networks that are chiefly important for mental operations traditionally labeled with terms such as "perception," "action," and "cognition." Furthermore, we describe networks as dynamic processes and how emotion-laden stimuli strongly affect network structure. As networks are not static entities, their organization unfolds temporally, such that specific brain regions affiliate with them in a time-varying fashion. Thus, at a specific moment, brain regions participate more strongly in some networks than others. In this dynamic view of brain function, emotion has broad, distributed effects on processing in a manner that transcends traditional boundaries and inflexible labels, such as "emotion" and "cognition." What matters is the coordinated action that supports behaviors. © The Author(s) 2016.

  19. Time and frequency-dependent modulation of local field potential synchronization by deep brain stimulation.

    Directory of Open Access Journals (Sweden)

    Clinton B McCracken

    Full Text Available High-frequency electrical stimulation of specific brain structures, known as deep brain stimulation (DBS, is an effective treatment for movement disorders, but mechanisms of action remain unclear. We examined the time-dependent effects of DBS applied to the entopeduncular nucleus (EP, the rat homolog of the internal globus pallidus, a target used for treatment of both dystonia and Parkinson's disease (PD. We performed simultaneous multi-site local field potential (LFP recordings in urethane-anesthetized rats to assess the effects of high-frequency (HF, 130 Hz; clinically effective, low-frequency (LF, 15 Hz; ineffective and sham DBS delivered to EP. LFP activity was recorded from dorsal striatum (STR, ventroanterior thalamus (VA, primary motor cortex (M1, and the stimulation site in EP. Spontaneous and acute stimulation-induced LFP oscillation power and functional connectivity were assessed at baseline, and after 30, 60, and 90 minutes of stimulation. HF EP DBS produced widespread alterations in spontaneous and stimulus-induced LFP oscillations, with some effects similar across regions and others occurring in a region- and frequency band-specific manner. Many of these changes evolved over time. HF EP DBS produced an initial transient reduction in power in the low beta band in M1 and STR; however, phase synchronization between these regions in the low beta band was markedly suppressed at all time points. DBS also enhanced low gamma synchronization throughout the circuit. With sustained stimulation, there were significant reductions in low beta synchronization between M1-VA and STR-VA, and increases in power within regions in the faster frequency bands. HF DBS also suppressed the ability of acute EP stimulation to induce beta oscillations in all regions along the circuit. This dynamic pattern of synchronizing and desynchronizing effects of EP DBS suggests a complex modulation of activity along cortico-BG-thalamic circuits underlying the therapeutic

  20. Time and frequency-dependent modulation of local field potential synchronization by deep brain stimulation.

    Science.gov (United States)

    McCracken, Clinton B; Kiss, Zelma H T

    2014-01-01

    High-frequency electrical stimulation of specific brain structures, known as deep brain stimulation (DBS), is an effective treatment for movement disorders, but mechanisms of action remain unclear. We examined the time-dependent effects of DBS applied to the entopeduncular nucleus (EP), the rat homolog of the internal globus pallidus, a target used for treatment of both dystonia and Parkinson's disease (PD). We performed simultaneous multi-site local field potential (LFP) recordings in urethane-anesthetized rats to assess the effects of high-frequency (HF, 130 Hz; clinically effective), low-frequency (LF, 15 Hz; ineffective) and sham DBS delivered to EP. LFP activity was recorded from dorsal striatum (STR), ventroanterior thalamus (VA), primary motor cortex (M1), and the stimulation site in EP. Spontaneous and acute stimulation-induced LFP oscillation power and functional connectivity were assessed at baseline, and after 30, 60, and 90 minutes of stimulation. HF EP DBS produced widespread alterations in spontaneous and stimulus-induced LFP oscillations, with some effects similar across regions and others occurring in a region- and frequency band-specific manner. Many of these changes evolved over time. HF EP DBS produced an initial transient reduction in power in the low beta band in M1 and STR; however, phase synchronization between these regions in the low beta band was markedly suppressed at all time points. DBS also enhanced low gamma synchronization throughout the circuit. With sustained stimulation, there were significant reductions in low beta synchronization between M1-VA and STR-VA, and increases in power within regions in the faster frequency bands. HF DBS also suppressed the ability of acute EP stimulation to induce beta oscillations in all regions along the circuit. This dynamic pattern of synchronizing and desynchronizing effects of EP DBS suggests a complex modulation of activity along cortico-BG-thalamic circuits underlying the therapeutic effects of

  1. Synchronization in material flow networks with biologically inspired self-organized control

    Energy Technology Data Exchange (ETDEWEB)

    Donner, Reik; Laemmer, Stefan [TU Dresden (Germany); Helbing, Dirk [ETH Zuerich (Switzerland)

    2009-07-01

    The efficient operation of material flows in traffic or production networks is a subject of broad economic interest. Traditional centralized as well as decentralized approaches to operating material flow networks are known to have severe disadvantages. As an alternative approach that may help to overcome these problems, we propose a simple self-organization mechanism of conflicting flows that is inspired by oscillatory phenomena of pedestrian or animal counter-flows at bottlenecks. As a result, one may observe a synchronization of the switching dynamics at different intersections in the network. For regular grid topologies, we find different synchronization regimes depending on the inertia of the switching from one service state to the next one. In order to test the robustness of our corresponding observations, we study how the detailed properties of the network as well as dynamic feedbacks between the relevant state variables affect the degree of achievable synchronization and the resulting performance of the network. Our results yield an improved understanding of the conditions that have to be present for efficiently operating material flow networks by a decentralized control, which is of paramount importance for future implementations in real-world traffic or production systems.

  2. Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

    Science.gov (United States)

    Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

    2017-01-01

    In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

  3. Clock Synchronization in Wireless Sensor Networks: A New Model and Analysis Approach Based on Networked Control Perspective

    Directory of Open Access Journals (Sweden)

    Wang Ting

    2014-01-01

    Full Text Available Motivated by the importance of the clock synchronization in wireless sensor networks (WSNs, this paper proposes a new research approach and model approach, which quantitatively analyzes clock synchronization from the perspective of modern control theory. Two kinds of control strategies are used as examples to analyze the effect of the control strategy on clock synchronization from different perspectives, namely, the single-step optimal control and the LQG global optimal control. The proposed method establishes a state space model for clock relationship, thus making dimension extension and parameter identification easier, and is robust to changes under the condition of node failures and new nodes. And through the design of different control strategies and performance index functions, the method can satisfy various requirements of the synchronization precision, convergence speed, energy consumption and the computational complexity, and so on. Finally, the simulations show that the synchronization accuracy of the proposed method is higher than that of the existing protocol, and the former convergence speed of the synchronization error is faster.

  4. Brain Networks of Explicit and Implicit Learning

    Science.gov (United States)

    Yang, Jing; Li, Ping

    2012-01-01

    Are explicit versus implicit learning mechanisms reflected in the brain as distinct neural structures, as previous research indicates, or are they distinguished by brain networks that involve overlapping systems with differential connectivity? In this functional MRI study we examined the neural correlates of explicit and implicit learning of artificial grammar sequences. Using effective connectivity analyses we found that brain networks of different connectivity underlie the two types of learning: while both processes involve activation in a set of cortical and subcortical structures, explicit learners engage a network that uses the insula as a key mediator whereas implicit learners evoke a direct frontal-striatal network. Individual differences in working memory also differentially impact the two types of sequence learning. PMID:22952624

  5. Anticipating, complete and lag synchronizations in RC phase-shift network based coupled Chua's circuits without delay.

    Science.gov (United States)

    Srinivasan, K; Senthilkumar, D V; Raja Mohamed, I; Murali, K; Lakshmanan, M; Kurths, J

    2012-06-01

    We construct a new RC phase shift network based Chua's circuit, which exhibits a period-doubling bifurcation route to chaos. Using coupled versions of such a phase-shift network based Chua's oscillators, we describe a new method for achieving complete synchronization (CS), approximate lag synchronization (LS), and approximate anticipating synchronization (AS) without delay or parameter mismatch. Employing the Pecora and Carroll approach, chaos synchronization is achieved in coupled chaotic oscillators, where the drive system variables control the response system. As a result, AS or LS or CS is demonstrated without using a variable delay line both experimentally and numerically.

  6. How Two Brains Make One Synchronized Mind in the Inferior Frontal Cortex: fNIRS-Based Hyperscanning During Cooperative Singing.

    Science.gov (United States)

    Osaka, Naoyuki; Minamoto, Takehiro; Yaoi, Ken; Azuma, Miyuki; Shimada, Yohko Minamoto; Osaka, Mariko

    2015-01-01

    One form of communication that is common in all cultures is people singing together. Singing together reflects an index of cognitive synchronization and cooperation of human brains. Little is known about the neural synchronization mechanism, however. Here, we examined how two brains make one synchronized behavior using cooperated singing/humming between two people and hyperscanning, a new brain scanning technique. Hyperscanning allowed us to observe dynamic cooperation between interacting participants. We used functional near-infrared spectroscopy (fNIRS) to simultaneously record the brain activity of two people while they cooperatively sang or hummed a song in face-to-face (FtF) or face-to-wall (FtW) conditions. By calculating the inter-brain wavelet transform coherence between two interacting brains, we found a significant increase in the neural synchronization of the left inferior frontal cortex (IFC) for cooperative singing or humming regardless of FtF or FtW compared with singing or humming alone. On the other hand, the right IFC showed an increase in neural synchronization for humming only, possibly due to more dependence on musical processing.

  7. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

    Science.gov (United States)

    Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

    2017-02-01

    We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Synchronization in a neural network of phase oscillators with time delayed coupling

    Science.gov (United States)

    Luzyanina, T. B.

    1994-08-01

    We investigate a neural network model designed as a system of the central oscillator and peripheral oscillators interacting with a time delay τ in a phase-locking loop. The delay corresponds to the finite velocity of signal propagation along nerve fibers. We study the synchronization under various values of τ. It is shown that under some conditions for a finite delay time there exist a multitude of synchronization frequencies in contrast to the case without delay where one has at most one solution. The criteria for the existence of multiple solutions and their stability are found. The asymptotic behavior under increasing connection strengths is analyzed.

  9. Pinning synchronization of two general complex networks with periodically intermittent control

    Directory of Open Access Journals (Sweden)

    Meng Fanyu

    2015-12-01

    Full Text Available In this paper, the method of periodically pinning intermittent control is introduced to solve the problem of outer synchronization between two complex networks. Based on the Lyapunov stability theory, differential inequality method and adaptive technique, some simple synchronous criteria have been derived analytically. At last, both the theoretical and numerical analysis illustrate the effectiveness of the proposed control methodology. This method not only reduces the conservatism of control gain but also saves the cost of production.These advantages make this method having a large application scope in the real production process.

  10. Time signal distribution in communication networks based on synchronous digital hierarchy

    Science.gov (United States)

    Imaoka, Atsushi; Kihara, Masami

    1993-01-01

    A new method that uses round-trip paths to accurately measure transmission delay for time synchronization is proposed. The performance of the method in Synchronous Digital Hierarchy networks is discussed. The feature of this method is that it separately measures the initial round trip path delay and the variations in round-trip path delay. The delay generated in SDH equipment is determined by measuring the initial round-trip path delay. In an experiment with actual SDH equipment, the error of initial delay measurement was suppressed to 30ns.

  11. Dynamic range in the C. elegans brain network

    Science.gov (United States)

    Antonopoulos, Chris G.

    2016-01-01

    We study external electrical perturbations and their responses in the brain dynamic network of the Caenorhabditis elegans soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a realistic experiment where one stimulates externally specific parts of the brain and studies the persistent neural activity triggered in other cortical regions. In this work, we perturb groups of neurons that form communities, identified by the walktrap community detection method, by trains of stereotypical electrical Poissonian impulses and study the propagation of neural activity to other communities by measuring the corresponding dynamic ranges and Steven law exponents. We show that when one perturbs specific communities, keeping the rest unperturbed, the external stimulations are able to propagate to some of them but not to all. There are also perturbations that do not trigger any response. We found that this depends on the initially perturbed community. Finally, we relate our findings for the former cases with low neural synchronization, self-criticality, and large information flow capacity, and interpret them as the ability of the brain network to respond to external perturbations when it works at criticality and its information flow capacity becomes maximal.

  12. On-chip constructive cell-network study (I): contribution of cardiac fibroblasts to cardiomyocyte beating synchronization and community effect.

    Science.gov (United States)

    Kaneko, Tomoyuki; Nomura, Fumimasa; Yasuda, Kenji

    2011-05-23

    To clarify the role of cardiac fibroblasts in beating synchronization, we have made simple lined-up cardiomyocyte-fibroblast network model in an on-chip single-cell-based cultivation system. The synchronization phenomenon of two cardiomyocyte networks connected by fibroblasts showed (1) propagation velocity of electrophysiological signals decreased a magnitude depending on the increasing number of fibroblasts, not the lengths of fibroblasts; (2) fluctuation of interbeat intervals of the synchronized two cardiomyocyte network connected by fibroblasts did not always decreased, and was opposite from homogeneous cardiomyocyte networks; and (3) the synchronized cardiomyocytes connected by fibroblasts sometimes loses their synchronized condition and recovered to synchronized condition, in which the length of asynchronized period was shorter less than 30 beats and was independent to their cultivation time, whereas the length of synchronized period increased according to cultivation time. The results indicated that fibroblasts can connect cardiomyocytes electrically but do not significantly enhance and contribute to beating interval stability and synchronization. This might also mean that an increase in the number of fibroblasts in heart tissue reduces the cardiomyocyte 'community effect', which enhances synchronization and stability of their beating rhythms.

  13. Stability and synchronization of memristor-based fractional-order delayed neural networks.

    Science.gov (United States)

    Chen, Liping; Wu, Ranchao; Cao, Jinde; Liu, Jia-Bao

    2015-11-01

    Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Dissipativity and Synchronization of Generalized BAM Neural Networks With Multivariate Discontinuous Activations.

    Science.gov (United States)

    Wang, Dongshu; Huang, Lihong; Tang, Longkun

    2017-09-14

    This paper is concerned with the dissipativity and synchronization problems of a class of delayed bidirectional associative memory (BAM) neural networks in which neuron activations are modeled by discontinuous bivariate functions. First, the concept of the Filippov solution is extended to functional differential equations with discontinuous right-hand sides and mixed delays via functional differential inclusions. The global dissipativity of the Filippov solution to the considered BAM neural networks is proven using generalized Halanay inequalities and matrix measure approaches. Second, to realize global exponential complete synchronization of BAM neural networks with multivariate discontinuous activations, discontinuous state feedback controllers are designed using functional differential inclusions theory and nonsmooth analysis theory with generalized Lyapunov functional method. Finally, several numerical examples are provided to demonstrate the applicability and effectiveness of our proposed results.

  15. Linear Approach for Synchronous State Stability in Fully Connected PLL Networks

    Directory of Open Access Journals (Sweden)

    Luiz H. A. Monteiro

    2008-03-01

    Full Text Available Synchronization is an essential feature for the use of digital systems in telecommunication networks, integrated circuits, and manufacturing automation. Formerly, master-slave (MS architectures, with precise master clock generators sending signals to phase-locked loops (PLLs working as slave oscillators, were considered the best solution. Nowadays, the development of wireless networks with dynamical connectivity and the increase of the size and the operation frequency of integrated circuits suggest that the distribution of clock signals could be more efficient if distributed solutions with fully connected oscillators are used. Here, fully connected networks with second-order PLLs as nodes are considered. In previous work, how the synchronous state frequency for this type of network depends on the node parameters and delays was studied and an expression for the long-term frequency was derived (Piqueira, 2006. Here, by taking the first term of the Taylor series expansion for the dynamical system description, it is shown that for a generic network with N nodes, the synchronous state is locally asymptotically stable.

  16. Physical layer one-time-pad data encryption through synchronized semiconductor laser networks

    Science.gov (United States)

    Argyris, Apostolos; Pikasis, Evangelos; Syvridis, Dimitris

    2016-02-01

    Semiconductor lasers (SL) have been proven to be a key device in the generation of ultrafast true random bit streams. Their potential to emit chaotic signals under conditions with desirable statistics, establish them as a low cost solution to cover various needs, from large volume key generation to real-time encrypted communications. Usually, only undemanding post-processing is needed to convert the acquired analog timeseries to digital sequences that pass all established tests of randomness. A novel architecture that can generate and exploit these true random sequences is through a fiber network in which the nodes are semiconductor lasers that are coupled and synchronized to central hub laser. In this work we show experimentally that laser nodes in such a star network topology can synchronize with each other through complex broadband signals that are the seed to true random bit sequences (TRBS) generated at several Gb/s. The potential for each node to access real-time generated and synchronized with the rest of the nodes random bit streams, through the fiber optic network, allows to implement an one-time-pad encryption protocol that mixes the synchronized true random bit sequence with real data at Gb/s rates. Forward-error correction methods are used to reduce the errors in the TRBS and the final error rate at the data decoding level. An appropriate selection in the sampling methodology and properties, as well as in the physical properties of the chaotic seed signal through which network locks in synchronization, allows an error free performance.

  17. Synchronization of a Class of Switched Neural Networks with Time-Varying Delays via Nonlinear Feedback Control.

    Science.gov (United States)

    Wang, Leimin; Shen, Yi; Zhang, Guodong

    2016-10-01

    This paper is concerned with the synchronization problem for a class of switched neural networks (SNNs) with time-varying delays. First, a new crucial lemma which includes and extends the classical exponential stability theorem is constructed. Then by using the lemma, new algebraic criteria of ψ -type synchronization (synchronization with general decay rate) for SNNs are established via the designed nonlinear feedback control. The ψ -type synchronization which is in a general framework is obtained by introducing a ψ -type function. It contains exponential synchronization, polynomial synchronization, and other synchronization as its special cases. The results of this paper are general, and they also complement and extend some previous results. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

  18. Fuzzy Approximation-Based Global Pinning Synchronization Control of Uncertain Complex Dynamical Networks.

    Science.gov (United States)

    Li, Xiao-Jian; Yang, Guang-Hong

    2017-04-01

    This paper is concerned with the global pinning synchronization problem of uncertain complex dynamical networks with communication constraints. First, an adaptive fuzzy controller is designed within a given compact set. In addition, a robust controller is introduced outside the compact set to pull back the system states. Then, a new pinning control scheme is given such that the global synchronization can be ensured. Moreover, via the Lyapunov theory and graph theory, the synchronization errors are proved to be asymptotically convergent. Especially, in an uncertainty-free environment, the proposed control scheme includes two easy-to-implement pinning control strategies as special cases, which improve the existing results from the view point of reducing the number of feedback controllers. Finally, two simulation examples are provided to validate the theoretical results.

  19. Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells

    Science.gov (United States)

    Johard, Helena; Mahdessian, Diana; Fedr, Radek; Marks, Carolyn; Medalová, Jiřina; Souček, Karel; Lundberg, Emma; Linnarsson, Sten; Bryja, Vítězslav; Sekyrova, Petra; Altun, Mikael; Andäng, Michael

    2017-01-01

    The cell cycle coordinates core functions such as replication and cell division. However, cell-cycle-regulated transcription in the control of non-core functions, such as cell identity maintenance through specific transcription factors (TFs) and signalling pathways remains unclear. Here, we provide a resource consisting of mapped transcriptomes in unsynchronized HeLa and U2OS cancer cells sorted for cell cycle phase by Fucci reporter expression. We developed a novel algorithm for data analysis that enables efficient visualization and data comparisons and identified cell cycle synchronization of Notch signalling and TFs associated with development. Furthermore, the cell cycle synchronizes with the circadian clock, providing a possible link between developmental transcriptional networks and the cell cycle. In conclusion we find that cell cycle synchronized transcriptional patterns are temporally compartmentalized and more complex than previously anticipated, involving genes, which control cell identity and development. PMID:29228002

  20. RBF neural network based H∞ H∞ H∞ synchronization for ...

    Indian Academy of Sciences (India)

    Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an H ∞ norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved ...

  1. Synchronization of general complex networks via adaptive control ...

    Indian Academy of Sciences (India)

    2014-03-07

    Mar 7, 2014 ... 3Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science &. Engineering, Zigong, Sichuan, 643000, People's Republic of China ...... inputs ui(t) (i = 1, 2, 3) and the values of control inputs are acceptable. From figures 1–5, it is easy to see that the controlled complex network ...

  2. Coordinate transformation and matrix measure approach for synchronization of complex networks.

    Science.gov (United States)

    Juang, Jonq; Liang, Yu-Hao

    2009-09-01

    Global synchronization in complex networks has attracted considerable interest in various fields. There are mainly two analytical approaches for studying such time-varying networks. The first approach is Lyapunov function-based methods. For such an approach, the connected-graph-stability (CGS) method arguably gives the best results. Nevertheless, CGS is limited to the networks with cooperative couplings. The matrix measure approach (MMA) proposed by Chen, although having a wider range of applications in the network topologies than that of CGS, works for smaller numbers of nodes in most network topologies. The approach also has a limitation with networks having partial-state coupling. Other than giving yet another MMA, we introduce a new and, in some cases, optimal coordinate transformation to study such networks. Our approach fixes all the drawbacks of CGS and MMA. In addition, by merely checking the structure of the vector field of the individual oscillator, we shall be able to determine if the system is globally synchronized. In summary, our results can be applied to rather general time-varying networks with a large number of nodes.

  3. Genetic control of functional brain network efficiency in children

    NARCIS (Netherlands)

    Heuvel, M.P.; van Soelen, I.L.C.; Stam, C.J.; Kahn, R.S.; Boomsma, D.I.; Hulshoff Pol, H.E.

    2013-01-01

    The human brain is a complex network of interconnected brain regions. In adulthood, the brain's network was recently found to be under genetic influence. However, the extent to which genes influence the functional brain network early in development is not yet known. We report on the heritability of

  4. Cluster Anti-Synchronization of Complex Networks with Nonidentical Dynamical Nodes

    Directory of Open Access Journals (Sweden)

    Shuguo Wang

    2012-01-01

    Full Text Available This paper investigates a new cluster antisynchronization scheme in the time-varying delays coupled complex dynamical networks with nonidentical nodes. Based on the community structure of the networks, the controllers are designed differently between the nodes in one community that have direct connections to the nodes in other communities and the nodes without direct connections with the nodes in other communities strategy; some sufficient criteria are derived to ensure cluster anti-synchronization of the network model. Particularly, the weight configuration matrix is not assumed to be irreducible. The numerical simulations are performed to verify the effectiveness of the theoretical results.

  5. Analysis and Design of Adaptive Synchronization of a Complex Dynamical Network with Time-Delayed Nodes and Coupling Delays

    Directory of Open Access Journals (Sweden)

    Yu Miao

    2017-01-01

    Full Text Available This paper is devoted to the study of synchronization problems in uncertain dynamical networks with time-delayed nodes and coupling delays. First, a complex dynamical network model with time-delayed nodes and coupling delays is given. Second, for a complex dynamical network with known or unknown but bounded nonlinear couplings, an adaptive controller is designed, which can ensure that the state of a dynamical network asymptotically synchronizes at the individual node state locally or globally in an arbitrary specified network. Then, the Lyapunov-Krasovskii stability theory is employed to estimate the network coupling parameters. The main results provide sufficient conditions for synchronization under local or global circumstances, respectively. Finally, two typical examples are given, using the M-G system as the nodes of the ring dynamical network and second-order nodes in the dynamical network with time-varying communication delays and switching communication topologies, which illustrate the effectiveness of the proposed controller design methods.

  6. Synchronization of hybrid-coupled delayed dynamical networks with noises by partial mixed impulsive control strategy

    Science.gov (United States)

    Zhao, Yi; Fu, Fangfang; Wang, Jingyi; Feng, Jianwen; Zhang, Haiyu

    2018-02-01

    In this paper, a new control method named partial mixed impulsive control strategy is proposed to investigate the problem of exponential synchronization in mean square for a class of general hybrid-coupled delayed dynamical networks with both internal delay and coupling delay. The partial mixed impulsive effects in this strategy can be taken as local and time-varying, which means that they are not only injected into a fraction of nodes in the whole networks but also contain synchronizing and desynchronizing impulses at the same time. In addition, to be more realistic, a delayed coupling term involving the transmission delay and self-feedback delay is taken into account. By means of the Lyapunov method and the comparison principle for impulsive systems, several sufficient criteria are obtained to guarantee the global exponential synchronization in mean square of the dynamical network. The obtained criteria are closely related to the proportion of the controlled nodes, the strengths of mixed impulses, the impulsive intervals, the time delays and the topology structure of the networks. Finally, a numerical example is given to demonstrate the effectiveness of our results.

  7. Emergence and scaling of synchronization in moving-agent networks with restrictive interactions

    Science.gov (United States)

    Kim, Beomseok; Do, Younghae; Lai, Ying-Cheng

    2013-10-01

    In fields such as robotics and sensor networks, synchronization among mobile and dynamic agents is a basic task. We articulate an effective strategy to achieve synchronization in dynamic networks of moving chaotic agents. Our counterintuitive idea is to restrict agents’ ability to interact with each other, which can be implemented by designating a finite number of fixed zones in the space, in which agents are allowed to interact with each other but agents outside the zones are deprived of the ability of mutual interaction. Our setting is thus different from the one used in existing works on synchronization of mobile agents where each agent is associated with an interacting zone that moves with the agent. We find, through a mathematical analysis, that an optimal interval exists in the interaction probability, where stable synchronization emerges. An inverse square-root scaling law is uncovered which relates the interval with the system size, i.e., the total number of moving agents. Extensive numerical support for physical spaces of one, two, and three dimensions is provided.

  8. Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance

    Science.gov (United States)

    Siebenhühner, Felix; Wang, Sheng H; Palva, J Matias; Palva, Satu

    2016-01-01

    Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions. DOI: http://dx.doi.org/10.7554/eLife.13451.001 PMID:27669146

  9. Movement decoding using neural synchronization and inter-hemispheric connectivity from deep brain local field potentials.

    Science.gov (United States)

    Mamun, K A; Mace, M; Lutman, M E; Stein, J; Liu, X; Aziz, T; Vaidyanathan, R; Wang, S

    2015-10-01

    Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain-machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs). LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process. This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination. These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a relatively stable and computationally inexpensive control

  10. Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

    Energy Technology Data Exchange (ETDEWEB)

    Li, Chun-Hsien, E-mail: chli@nknucc.nknu.edu.tw [Department of Mathematics, National Kaohsiung Normal University, Yanchao District, Kaohsiung City 82444, Taiwan (China); Yang, Suh-Yuh, E-mail: syyang@math.ncu.edu.tw [Department of Mathematics, National Central University, Jhongli City, Taoyuan County 32001, Taiwan (China)

    2014-03-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  11. Error and attack tolerance of synchronization in Hindmarsh-Rose neural networks with community structure

    Science.gov (United States)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-03-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh-Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  12. A Group Neighborhood Average Clock Synchronization Protocol for Wireless Sensor Networks

    Science.gov (United States)

    Lin, Lin; Ma, Shiwei; Ma, Maode

    2014-01-01

    Clock synchronization is a very important issue for the applications of wireless sensor networks. The sensors need to keep a strict clock so that users can know exactly what happens in the monitoring area at the same time. This paper proposes a novel internal distributed clock synchronization solution using group neighborhood average. Each sensor node collects the offset and skew rate of the neighbors. Group averaging of offset and skew rate value are calculated instead of conventional point-to-point averaging method. The sensor node then returns compensated value back to the neighbors. The propagation delay is considered and compensated. The analytical analysis of offset and skew compensation is presented. Simulation results validate the effectiveness of the protocol and reveal that the protocol allows sensor networks to quickly establish a consensus clock and maintain a small deviation from the consensus clock. PMID:25120163

  13. Synchronization of nonidentical chaotic neural networks with leakage delay and mixed time-varying delays

    Directory of Open Access Journals (Sweden)

    Cao Jinde

    2011-01-01

    Full Text Available Abstract In this paper, an integral sliding mode control approach is presented to investigate synchronization of nonidentical chaotic neural networks with discrete and distributed time-varying delays as well as leakage delay. By considering a proper sliding surface and constructing Lyapunov-Krasovskii functional, as well as employing a combination of the free-weighting matrix method, Newton-Leibniz formulation and inequality technique, a sliding mode controller is designed to achieve the asymptotical synchronization of the addressed nonidentical neural networks. Moreover, a sliding mode control law is also synthesized to guarantee the reachability of the specified sliding surface. The provided conditions are expressed in terms of linear matrix inequalities, and are dependent on the discrete and distributed time delays as well as leakage delay. A simulation example is given to verify the theoretical results.

  14. Robust Adaptive Exponential Synchronization of Stochastic Perturbed Chaotic Delayed Neural Networks with Parametric Uncertainties

    Directory of Open Access Journals (Sweden)

    Yang Fang

    2014-01-01

    Full Text Available This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs. The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.

  15. Modeling and simulation of permanent magnet synchronous motor based on neural network control strategy

    Science.gov (United States)

    Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao

    2017-03-01

    Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.

  16. Star network synchronization led by strong coupling-induced frequency squeezing

    Science.gov (United States)

    Militello, Benedetto; Chruściński, Dariusz; Napoli, Anna

    2018-02-01

    We consider a star network consisting of N oscillators coupled to a central one which in turn is coupled to an infinite set of oscillators (reservoir), which makes it leaking. Two of the N+1 normal modes are dissipating, while the remaining N-1 lie in a frequency range which is more and more squeezed as the coupling strengths increase, which realizes synchronization of the single parts of the system.

  17. Sub-Synchronous Interaction Analysis between DFIG Based Wind Farm and Series Compensated Network

    OpenAIRE

    Wang, Yun; Wu, Qiuwei; Kang, Shaoli

    2016-01-01

    This paper analyzes the sub-synchronous interaction (SSI) phenomenon between the doubly fed induction generator (DFIG) based wind farm (WF) and the series capacitor compensated network. The possible types of SSI in the DFIG based WF are studied. The factors influencing the SSI of DFIG based WF are investigated. The large signal stability and small signal stability of the DFIG based WF with different series compensation (SC) level and wind speed are simulated and compared.

  18. Computational simulation: astrocyte-induced depolarization of neighboring neurons mediates synchronous UP states in a neural network.

    Science.gov (United States)

    Kuriu, Takayuki; Kakimoto, Yuta; Araki, Osamu

    2015-09-01

    Although recent reports have suggested that synchronous neuronal UP states are mediated by astrocytic activity, the mechanism responsible for this remains unknown. Astrocytic glutamate release synchronously depolarizes adjacent neurons, while synaptic transmissions are blocked. The purpose of this study was to confirm that astrocytic depolarization, propagated through synaptic connections, can lead to synchronous neuronal UP states. We applied astrocytic currents to local neurons in a neural network consisting of model cortical neurons. Our results show that astrocytic depolarization may generate synchronous UP states for hundreds of milliseconds in neurons even if they do not directly receive glutamate release from the activated astrocyte.

  19. A SAT-based algorithm for finding attractors in synchronous Boolean networks.

    Science.gov (United States)

    Dubrova, Elena; Teslenko, Maxim

    2011-01-01

    This paper addresses the problem of finding attractors in synchronous Boolean networks. The existing Boolean decision diagram-based algorithms have limited capacity due to the excessive memory requirements of decision diagrams. The simulation-based algorithms can be applied to larger networks, however, they are incomplete. We present an algorithm, which uses a SAT-based bounded model checking to find all attractors in a Boolean network. The efficiency of the presented algorithm is evaluated by analyzing seven networks models of real biological processes, as well as 150,000 randomly generated Boolean networks of sizes between 100 and 7,000. The results show that our approach has a potential to handle an order of magnitude larger models than currently possible.

  20. An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

    Science.gov (United States)

    2012-01-01

    Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

  1. Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

    Science.gov (United States)

    Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

    2017-09-01

    This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Channel noise-induced temporal coherence transitions and synchronization transitions in adaptive neuronal networks with time delay

    Science.gov (United States)

    Gong, Yubing; Xie, Huijuan

    2017-09-01

    Using spike-timing-dependent plasticity (STDP), we study the effect of channel noise on temporal coherence and synchronization of adaptive scale-free Hodgkin-Huxley neuronal networks with time delay. It is found that the spiking regularity and spatial synchronization of the neurons intermittently increase and decrease as channel noise intensity is varied, exhibiting transitions of temporal coherence and synchronization. Moreover, this phenomenon depends on time delay, STDP, and network average degree. As time delay increases, the phenomenon is weakened, however, there are optimal STDP and network average degree by which the phenomenon becomes strongest. These results show that channel noise can intermittently enhance the temporal coherence and synchronization of the delayed adaptive neuronal networks. These findings provide a new insight into channel noise for the information processing and transmission in neural systems.

  3. Stress Impact on Resting State Brain Networks.

    Science.gov (United States)

    Soares, José Miguel; Sampaio, Adriana; Ferreira, Luís Miguel; Santos, Nadine Correia; Marques, Paulo; Marques, Fernanda; Palha, Joana Almeida; Cerqueira, João José; Sousa, Nuno

    2013-01-01

    Resting state brain networks (RSNs) are spatially distributed large-scale networks, evidenced by resting state functional magnetic resonance imaging (fMRI) studies. Importantly, RSNs are implicated in several relevant brain functions and present abnormal functional patterns in many neuropsychiatric disorders, for which stress exposure is an established risk factor. Yet, so far, little is known about the effect of stress in the architecture of RSNs, both in resting state conditions or during shift to task performance. Herein we assessed the architecture of the RSNs using functional magnetic resonance imaging (fMRI) in a cohort of participants exposed to prolonged stress (participants that had just finished their long period of preparation for the medical residence selection exam), and respective gender- and age-matched controls (medical students under normal academic activities). Analysis focused on the pattern of activity in resting state conditions and after deactivation. A volumetric estimation of the RSNs was also performed. Data shows that stressed participants displayed greater activation of the default mode (DMN), dorsal attention (DAN), ventral attention (VAN), sensorimotor (SMN), and primary visual (VN) networks than controls. Importantly, stressed participants also evidenced impairments in the deactivation of resting state-networks when compared to controls. These functional changes are paralleled by a constriction of the DMN that is in line with the pattern of brain atrophy observed after stress exposure. These results reveal that stress impacts on activation-deactivation pattern of RSNs, a finding that may underlie stress-induced changes in several dimensions of brain activity.

  4. High-performance parallel interface to synchronous optical network gateway

    Science.gov (United States)

    St. John, Wallace B.; DuBois, David H.

    1996-01-01

    A system of sending and receiving gateways interconnects high speed data interfaces, e.g., HIPPI interfaces, through fiber optic links, e.g., a SONET network. An electronic stripe distributor distributes bytes of data from a first interface at the sending gateway onto parallel fiber optics of the fiber optic link to form transmitted data. An electronic stripe collector receives the transmitted data on the parallel fiber optics and reforms the data into a format effective for input to a second interface at the receiving gateway. Preferably, an error correcting syndrome is constructed at the sending gateway and sent with a data frame so that transmission errors can be detected and corrected in a real-time basis. Since the high speed data interface operates faster than any of the fiber optic links the transmission rate must be adapted to match the available number of fiber optic links so the sending and receiving gateways monitor the availability of fiber links and adjust the data throughput accordingly. In another aspect, the receiving gateway must have sufficient available buffer capacity to accept an incoming data frame. A credit-based flow control system provides for continuously updating the sending gateway on the available buffer capacity at the receiving gateway.

  5. Generative adversarial networks for brain lesion detection

    Science.gov (United States)

    Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

    2017-02-01

    Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

  6. Changes in cognitive state alter human functional brain networks

    Directory of Open Access Journals (Sweden)

    Malaak Nasser Moussa

    2011-08-01

    Full Text Available The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network (DMN. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation. Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.

  7. Switching synchronization in one-dimensional memristive networks: An exact solution

    Science.gov (United States)

    Slipko, V. A.; Pershin, Y. V.

    2017-12-01

    We study a switching synchronization phenomenon taking place in one-dimensional memristive networks when the memristors switch from the high- to low-resistance state. It is assumed that the distributions of threshold voltages and switching rates of memristors are arbitrary. Using the Laplace transform, a set of nonlinear equations describing the memristors dynamics is solved exactly, without any approximations. The time dependencies of memristances are found, and it is shown that the voltage falls across memristors are proportional to their threshold voltages. A compact expression for the network switching time is derived.

  8. Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Tat-Bao-Thien Nguyen

    2014-01-01

    Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

  9. Stability Analysis and Synchronization for a Class of Fractional-Order Neural Networks

    Directory of Open Access Journals (Sweden)

    Guanjun Li

    2016-02-01

    Full Text Available Stability of a class of fractional-order neural networks (FONNs is analyzed in this paper. First, two sufficient conditions for convergence of the solution for such systems are obtained by utilizing Gronwall–Bellman lemma and Laplace transform technique. Then, according to the fractional-order Lyapunov second method and linear feedback control, the synchronization problem between two fractional-order chaotic neural networks is investigated. Finally, several numerical examples are presented to justify the feasibility of the proposed methods.

  10. Synchronization Algorithm for SDN-controlled All-Optical TDM Switching in a Random Length Ring Network

    DEFF Research Database (Denmark)

    Kamchevska, Valerija; Cristofori, Valentina; Da Ros, Francesco

    2016-01-01

    We propose and demonstrate an algorithm that allows for automatic synchronization of SDN-controlled all-optical TDM switching nodes connected in a ring network. We experimentally show successful WDM-SDM transmission of data bursts between all ring nodes.......We propose and demonstrate an algorithm that allows for automatic synchronization of SDN-controlled all-optical TDM switching nodes connected in a ring network. We experimentally show successful WDM-SDM transmission of data bursts between all ring nodes....

  11. Approach of Complex Networks for the Determination of Brain Death

    Science.gov (United States)

    Sun, Wei-Gang; Cao, Jian-Ting; Wang, Ru-Bin

    2011-06-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death.

  12. Robust outer synchronization between two nonlinear complex networks with parametric disturbances and mixed time-varying delays

    Science.gov (United States)

    Zhang, Chuan; Wang, Xingyuan; Luo, Chao; Li, Junqiu; Wang, Chunpeng

    2018-03-01

    In this paper, we focus on the robust outer synchronization problem between two nonlinear complex networks with parametric disturbances and mixed time-varying delays. Firstly, a general complex network model is proposed. Besides the nonlinear couplings, the network model in this paper can possess parametric disturbances, internal time-varying delay, discrete time-varying delay and distributed time-varying delay. Then, according to the robust control strategy, linear matrix inequality and Lyapunov stability theory, several outer synchronization protocols are strictly derived. Simple linear matrix controllers are designed to driver the response network synchronize to the drive network. Additionally, our results can be applied on the complex networks without parametric disturbances. Finally, by utilizing the delayed Lorenz chaotic system as the dynamics of all nodes, simulation examples are given to demonstrate the effectiveness of our theoretical results.

  13. Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2017-03-01

    Full Text Available In this study, an adaptive neural network synchronization (NNS approach, capable of guaranteeing prescribed performance (PP, is designed for non-identical fractional-order chaotic systems (FOCSs. For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

  14. Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

    Science.gov (United States)

    Li, Yuan; Lv, Hui; Jiao, Dongxiu

    2017-03-01

    In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

  15. Network Theory and Effects of Transcranial Brain Stimulation Methods on the Brain Networks

    Directory of Open Access Journals (Sweden)

    Sema Demirci

    2014-12-01

    Full Text Available In recent years, there has been a shift from classic localizational approaches to new approaches where the brain is considered as a complex system. Therefore, there has been an increase in the number of studies involving collaborations with other areas of neurology in order to develop methods to understand the complex systems. One of the new approaches is graphic theory that has principles based on mathematics and physics. According to this theory, the functional-anatomical connections of the brain are defined as a network. Moreover, transcranial brain stimulation techniques are amongst the recent research and treatment methods that have been commonly used in recent years. Changes that occur as a result of applying brain stimulation techniques on physiological and pathological networks help better understand the normal and abnormal functions of the brain, especially when combined with techniques such as neuroimaging and electroencephalography. This review aims to provide an overview of the applications of graphic theory and related parameters, studies conducted on brain functions in neurology and neuroscience, and applications of brain stimulation systems in the changing treatment of brain network models and treatment of pathological networks defined on the basis of this theory.

  16. Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2018-01-01

    We consider the Watts-Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity. Occurrence of a "Matthew" effect in synaptic plasticity is found due to a positive feedback process. As a result, good synchronization gets better via long-term potentiation of synaptic strengths, while bad synchronization gets worse via long-term depression. Emergences of long-term potentiation and long-term depression of synaptic strengths are intensively investigated via microscopic studies based on the pair-correlations between the pre- and the post-synaptic IISRs (instantaneous individual spike rates) as well as the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, the effects of multiplicative STDP (which depends on states) on the SSS are studied and discussed in comparison with the case of additive STDP (independent of states). These effects of STDP on the SSS in the SWN are also compared with those in the regular lattice and the random graph. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Network effects of deep brain stimulation.

    Science.gov (United States)

    Alhourani, Ahmad; McDowell, Michael M; Randazzo, Michael J; Wozny, Thomas A; Kondylis, Efstathios D; Lipski, Witold J; Beck, Sarah; Karp, Jordan F; Ghuman, Avniel S; Richardson, R Mark

    2015-10-01

    The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies. Copyright © 2015 the American Physiological Society.

  18. Cluster synchronization in networks of identical oscillators with α -function pulse coupling

    Science.gov (United States)

    Chen, Bolun; Engelbrecht, Jan R.; Mirollo, Renato

    2017-02-01

    We study a network of N identical leaky integrate-and-fire model neurons coupled by α -function pulses, weighted by a coupling parameter K . Studies of the dynamics of this system have mostly focused on the stability of the fully synchronized and the fully asynchronous splay states, which naturally depends on the sign of K , i.e., excitation vs inhibition. We find that there is also a rich set of attractors consisting of clusters of fully synchronized oscillators, such as fixed (N -1 ,1 ) states, which have synchronized clusters of sizes N -1 and 1, as well as splay states of clusters with equal sizes greater than 1. Additionally, we find limit cycles that clarify the stability of previously observed quasiperiodic behavior. Our framework exploits the neutrality of the dynamics for K =0 which allows us to implement a dimensional reduction strategy that simplifies the dynamics to a continuous flow on a codimension 3 subspace with the sign of K determining the flow direction. This reduction framework naturally incorporates a hierarchy of partially synchronized subspaces in which the new attracting states lie. Using high-precision numerical simulations, we describe completely the sequence of bifurcations and the stability of all fixed points and limit cycles for N =2 -4 . The set of possible attracting states can be used to distinguish different classes of neuron models. For instance from our previous work [Chaos 24, 013114 (2014), 10.1063/1.4858458] we know that of the types of partially synchronized states discussed here, only the (N -1 ,1 ) states can be stable in systems of identical coupled sinusoidal (i.e., Kuramoto type) oscillators, such as θ -neuron models. Upon introducing a small variation in individual neuron parameters, the attracting fixed points we discuss here generalize to equivalent fixed points in which neurons need not fire coincidently.

  19. Global Mittag-Leffler synchronization of fractional-order neural networks with discontinuous activations.

    Science.gov (United States)

    Ding, Zhixia; Shen, Yi; Wang, Leimin

    2016-01-01

    This paper is concerned with the global Mittag-Leffler synchronization for a class of fractional-order neural networks with discontinuous activations (FNNDAs). We give the concept of Filippov solution for FNNDAs in the sense of Caputo's fractional derivation. By using a singular Gronwall inequality and the properties of fractional calculus, the existence of global solution under the framework of Filippov for FNNDAs is proved. Based on the nonsmooth analysis and control theory, some sufficient criteria for the global Mittag-Leffler synchronization of FNNDAs are derived by designing a suitable controller. The proposed results enrich and enhance the previous reports. Finally, one numerical example is given to demonstrate the effectiveness of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Synchronized Multimedia Streaming on the iPhone Platform with Network Coding

    DEFF Research Database (Denmark)

    Vingelmann, Peter; Fitzek, Frank; Pedersen, Morten Videbæk

    2011-01-01

    This work presents the implementation of synchronized multimedia streaming for the Apple iPhone platform. The idea is to stream multimedia content from a single source to multiple receivers with direct or multihop connections to the source. First we look into existing solutions for video streaming...... on the iPhone that use point-to-point architectures. After acknowledging their limitations, we propose a solution based on network coding to efficiently and reliably deliver the multimedia content to many devices in a synchronized manner. Then we introduce an application that implements this technique...... on the iPhone. We also present our testbed, which consists of 16 iPod Touch devices to showcase the capabilities of our application....

  1. Time Delay and Long-Range Connection Induced Synchronization Transitions in Newman-Watts Small-World Neuronal Networks

    Science.gov (United States)

    Qian, Yu

    2014-01-01

    The synchronization transitions in Newman-Watts small-world neuronal networks (SWNNs) induced by time delay and long-range connection (LRC) probability have been investigated by synchronization parameter and space-time plots. Four distinct parameter regions, that is, asynchronous region, transition region, synchronous region, and oscillatory region have been discovered at certain LRC probability as time delay is increased. Interestingly, desynchronization is observed in oscillatory region. More importantly, we consider the spatiotemporal patterns obtained in delayed Newman-Watts SWNNs are the competition results between long-range drivings (LRDs) and neighboring interactions. In addition, for moderate time delay, the synchronization of neuronal network can be enhanced remarkably by increasing LRC probability. Furthermore, lag synchronization has been found between weak synchronization and complete synchronization as LRC probability is a little less than 1.0. Finally, the two necessary conditions, moderate time delay and large numbers of LRCs, are exposed explicitly for synchronization in delayed Newman-Watts SWNNs. PMID:24810595

  2. Decoding Network Structure in On-Chip Integrated Flow Cells with Synchronization of Electrochemical Oscillators.

    Science.gov (United States)

    Jia, Yanxin; Kiss, István Z

    2017-04-07

    The analysis of network interactions among dynamical units and the impact of the coupling on self-organized structures is a challenging task with implications in many biological and engineered systems. We explore the coupling topology that arises through the potential drops in a flow channel in a lab-on-chip device that accommodates chemical reactions on electrode arrays. The networks are revealed by analysis of the synchronization patterns with the use of an oscillatory chemical reaction (nickel electrodissolution) and are further confirmed by direct decoding using phase model analysis. In dual electrode configuration, a variety coupling schemes, (uni- or bidirectional positive or negative) were identified depending on the relative placement of the reference and counter electrodes (e.g., placed at the same or the opposite ends of the flow channel). With three electrodes, the network consists of a superposition of a localized (upstream) and global (all-to-all) coupling. With six electrodes, the unique, position dependent coupling topology resulted spatially organized partial synchronization such that there was a synchrony gradient along the quasi-one-dimensional spatial coordinate. The networked, electrode potential (current) spike generating electrochemical reactions hold potential for construction of an in-situ information processing unit to be used in electrochemical devices in sensors and batteries.

  3. Decoding Network Structure in On-Chip Integrated Flow Cells with Synchronization of Electrochemical Oscillators

    Science.gov (United States)

    Jia, Yanxin; Kiss, István Z.

    2017-04-01

    The analysis of network interactions among dynamical units and the impact of the coupling on self-organized structures is a challenging task with implications in many biological and engineered systems. We explore the coupling topology that arises through the potential drops in a flow channel in a lab-on-chip device that accommodates chemical reactions on electrode arrays. The networks are revealed by analysis of the synchronization patterns with the use of an oscillatory chemical reaction (nickel electrodissolution) and are further confirmed by direct decoding using phase model analysis. In dual electrode configuration, a variety coupling schemes, (uni- or bidirectional positive or negative) were identified depending on the relative placement of the reference and counter electrodes (e.g., placed at the same or the opposite ends of the flow channel). With three electrodes, the network consists of a superposition of a localized (upstream) and global (all-to-all) coupling. With six electrodes, the unique, position dependent coupling topology resulted spatially organized partial synchronization such that there was a synchrony gradient along the quasi-one-dimensional spatial coordinate. The networked, electrode potential (current) spike generating electrochemical reactions hold potential for construction of an in-situ information processing unit to be used in electrochemical devices in sensors and batteries.

  4. Synchronization in scale-free networks: The role of finite-size effects

    Science.gov (United States)

    Torres, D.; Di Muro, M. A.; La Rocca, C. E.; Braunstein, L. A.

    2015-06-01

    Synchronization problems in complex networks are very often studied by researchers due to their many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, scale-free networks with degree distribution P(k)∼ k-λ , are widely used in research since they are ubiquitous in Nature and other real systems. In this paper we focus on the surface relaxation growth model in scale-free networks with 2.5< λ <3 , and study the scaling behavior of the fluctuations, in the steady state, with the system size N. We find a novel behavior of the fluctuations characterized by a crossover between two regimes at a value of N=N* that depends on λ: a logarithmic regime, found in previous research, and a constant regime. We propose a function that describes this crossover, which is in very good agreement with the simulations. We also find that, for a system size above N* , the fluctuations decrease with λ, which means that the synchronization of the system improves as λ increases. We explain this crossover analyzing the role of the network's heterogeneity produced by the system size N and the exponent of the degree distribution.

  5. Energy-efficient pulse-coupled synchronization strategy design for wireless sensor networks through reduced idle listening

    Science.gov (United States)

    Wang, Yongqiang; Núñez, Felipe; Doyle, Francis J.

    2013-01-01

    Synchronization is crucial to wireless sensor networks due to their decentralized structure. We propose an energy-efficient pulse-coupled synchronization strategy to achieve this goal. The basic idea is to reduce idle listening by intentionally introducing a large refractory period in the sensors’ cooperation. The large refractory period greatly reduces idle listening in each oscillation period, and is analytically proven to have no influence on the time to synchronization. Hence, it significantly reduces the total energy consumption in a synchronization process. A topology control approach tailored for pulse-coupled synchronization is given to guarantee a k-edge strongly connected interaction topology, which is tolerant to communication-link failures. The topology control approach is totally decentralized and needs no information exchange among sensors, and it is applicable to dynamic network topologies as well. This facilitates a completely decentralized implementation of the synchronization strategy. The strategy is applicable to mobile sensor networks, too. QualNet case studies confirm the effectiveness of the synchronization strategy. PMID:24307831

  6. Climate network analysis of regional precipitation extremes: The true story told by event synchronization

    Science.gov (United States)

    Odenweller, Adrian; Donner, Reik V.

    2017-04-01

    Over the last decade, complex network methods have been frequently used for characterizing spatio-temporal patterns of climate variability from a complex systems perspective, yielding new insights into time-dependent teleconnectivity patterns and couplings between different components of the Earth climate. Among the foremost results reported, network analyses of the synchronicity of extreme events as captured by the so-called event synchronization have been proposed to be powerful tools for disentangling the spatio-temporal organization of particularly extreme rainfall events and anticipating the timing of monsoon onsets or extreme floodings. Rooted in the analysis of spike train synchrony analysis in the neurosciences, event synchronization has the great advantage of automatically classifying pairs of events arising at two distinct spatial locations as temporally close (and, thus, possibly statistically - or even dynamically - interrelated) or not without the necessity of selecting an additional parameter in terms of a maximally tolerable delay between these events. This consideration is conceptually justified in case of the original application to spike trains in electroencephalogram (EEG) recordings, where the inter-spike intervals show relatively narrow distributions at high temporal sampling rates. However, in case of climate studies, precipitation extremes defined by daily precipitation sums exceeding a certain empirical percentile of their local distribution exhibit a distinctively different type of distribution of waiting times between subsequent events. This raises conceptual concerns if event synchronization is still appropriate for detecting interlinkages between spatially distributed precipitation extremes. In order to study this problem in more detail, we employ event synchronization together with an alternative similarity measure for event sequences, event coincidence rates, which requires a manual setting of the tolerable maximum delay between two

  7. Identifying modular relations in complex brain networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Mørup, Morten; Siebner, Hartwig

    2012-01-01

    and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure......We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility...... are measured within the data driven NPAIRS split-half framework. Using synthetic data drawn from each of the generative models we show that the IRM model outperforms the two competing models when data contain relational structure. For data drawn from the other two simpler models the IRM does not overfit...

  8. Power law scaling in synchronization of brain signals depends on cognitive load.

    Science.gov (United States)

    Tinker, Jesse; Velazquez, Jose Luis Perez

    2014-01-01

    As it has several features that optimize information processing, it has been proposed that criticality governs the dynamics of nervous system activity. Indications of such dynamics have been reported for a variety of in vitro and in vivo recordings, ranging from in vitro slice electrophysiology to human functional magnetic resonance imaging. However, there still remains considerable debate as to whether the brain actually operates close to criticality or in another governing state such as stochastic or oscillatory dynamics. A tool used to investigate the criticality of nervous system data is the inspection of power-law distributions. Although the findings are controversial, such power-law scaling has been found in different types of recordings. Here, we studied whether there is a power law scaling in the distribution of the phase synchronization derived from magnetoencephalographic recordings during executive function tasks performed by children with and without autism. Characterizing the brain dynamics that is different between autistic and non-autistic individuals is important in order to find differences that could either aid diagnosis or provide insights as to possible therapeutic interventions in autism. We report in this study that power law scaling in the distributions of a phase synchrony index is not very common and its frequency of occurrence is similar in the control and the autism group. In addition, power law scaling tends to diminish with increased cognitive load (difficulty or engagement in the task). There were indications of changes in the probability distribution functions for the phase synchrony that were associated with a transition from power law scaling to lack of power law (or vice versa), which suggests the presence of phenomenological bifurcations in brain dynamics associated with cognitive load. Hence, brain dynamics may fluctuate between criticality and other regimes depending upon context and behaviors.

  9. Power law scaling in synchronization of brain signals depends on cognitive load

    Directory of Open Access Journals (Sweden)

    Jose Luis ePerez Velazquez

    2014-05-01

    Full Text Available As it has several features that optimize information processing, it has been proposed that criticality governs the dynamics of nervous system activity. Indications of such dynamics have been reported for a variety of in vitro and in vivo recordings, ranging from in vitro slice electrophysiology to human functional magnetic resonance imaging. However, there still remains considerable debate as to whether the brain actually operates close to criticality or in another governing state such as stochastic or oscillatory dynamics. A tool used to investigate the criticality of nervous system data is the inspection of power-law distributions. Although the findings are controversial, such power-law scaling has been found in different types of recordings. Here, we studied whether there is a power law scaling in the distribution of the phase synchronization derived from magnetoencephalographic recordings during executive function tasks performed by children with and without autism. Characterizing the brain dynamics that is different between autistic and non-autistic individuals is important in order to find differences that could either aid diagnosis or provide insights as to possible therapeutic interventions in autism. We report in this study that power law scaling in the distributions of a phase synchrony index is not very common and its frequency of occurrence is similar in the control and the autism group. In addition, power law scaling tends to diminish with increased cognitive load (difficulty or engagement in the task. There were indications of changes in the probability distribution functions for the phase synchrony that were associated with a transition from power law scaling to lack of power law (or vice versa, which suggests the presence of phenomenological bifurcations in brain dynamics associated with cognitive load. Hence, brain dynamics may fluctuate between criticality and other regimes depending upon context and behaviours.

  10. Stress Impact on Resting State Brain Networks.

    Directory of Open Access Journals (Sweden)

    José Miguel Soares

    Full Text Available Resting state brain networks (RSNs are spatially distributed large-scale networks, evidenced by resting state functional magnetic resonance imaging (fMRI studies. Importantly, RSNs are implicated in several relevant brain functions and present abnormal functional patterns in many neuropsychiatric disorders, for which stress exposure is an established risk factor. Yet, so far, little is known about the effect of stress in the architecture of RSNs, both in resting state conditions or during shift to task performance. Herein we assessed the architecture of the RSNs using functional magnetic resonance imaging (fMRI in a cohort of participants exposed to prolonged stress (participants that had just finished their long period of preparation for the medical residence selection exam, and respective gender- and age-matched controls (medical students under normal academic activities. Analysis focused on the pattern of activity in resting state conditions and after deactivation. A volumetric estimation of the RSNs was also performed. Data shows that stressed participants displayed greater activation of the default mode (DMN, dorsal attention (DAN, ventral attention (VAN, sensorimotor (SMN, and primary visual (VN networks than controls. Importantly, stressed participants also evidenced impairments in the deactivation of resting state-networks when compared to controls. These functional changes are paralleled by a constriction of the DMN that is in line with the pattern of brain atrophy observed after stress exposure. These results reveal that stress impacts on activation-deactivation pattern of RSNs, a finding that may underlie stress-induced changes in several dimensions of brain activity.

  11. Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays.

    Science.gov (United States)

    Cao, Jinde; Wan, Ying

    2014-05-01

    A single inertial BAM neural network with time-varying delays and external inputs is concerned in this paper. First, by choosing suitable variable substitution, the original system can be transformed into first-order differential equations. Then, we present several sufficient conditions for the global exponential stability of the equilibrium by using matrix measure and Halanay inequality, these criteria are simple in form and easy to verify in practice. Furthermore, when employing an error-feedback control term to the response neural network, parallel criteria regarding to the exponential synchronization of the drive-response neural network are also generated. Finally, some examples are given to illustrate our theoretical results. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. NMDA-dependent phase synchronization between septal and temporal CA3 hippocampal networks.

    Science.gov (United States)

    Gu, Ning; Jackson, Jesse; Goutagny, Romain; Lowe, Germaine; Manseau, Frédéric; Williams, Sylvain

    2013-05-08

    Increasing evidence suggests that synchronization between brain regions is essential for information exchange and memory processes. However, it remains incompletely known which synaptic mechanisms contribute to the process of synchronization. Here, we investigated whether NMDA receptor-mediated synaptic plasticity was an important player in synchronization between septal and temporal CA3 areas of the rat hippocampus. We found that both the septal and temporal CA3 regions intrinsically generate weakly synchronized δ frequency oscillations in the complete hippocampus in vitro. Septal and temporal oscillators differed in frequency, power, and rhythmicity, but both required GABAA and AMPA receptors. NMDA receptor activation, and most particularly the NR2B subunit, contributed considerably more to rhythm generation at the temporal than the septal region. Brief activation of NMDA receptors by application of extracellular calcium dramatically potentiated the septal-temporal coherence for long durations (>40 min), an effect blocked by the NMDA antagonist AP-5. This long-lasting NMDA-receptor-dependent increase in coherence was also associated with an elevated phase locking of spikes locally and across regions. Changes in coherence between oscillators were associated with increases in phase locking between oscillators independent of oscillator amplitude. Finally, although the septal CA3 rhythm preceded the oscillations in temporal regions in control conditions, this was reversed during the NMDA-dependent enhancement in coherence, suggesting that NMDA receptor activation can change the direction of information flow along the septotemporal CA3 axis. These data demonstrate that plastic changes in communication between septal and temporal hippocampal regions can arise from the NMDA-dependent phase locking of neural oscillators.

  13. Method of derivation and differentiation of mouse embryonic stem cells generating synchronous neuronal networks.

    Science.gov (United States)

    Gazina, Elena V; Morrisroe, Emma; Mendis, Gunarathna D C; Michalska, Anna E; Chen, Joseph; Nefzger, Christian M; Rollo, Benjamin N; Reid, Christopher A; Pera, Martin F; Petrou, Steven

    2018-01-01

    Stem cells-derived neuronal cultures hold great promise for in vitro disease modelling and drug screening. However, currently stem cells-derived neuronal cultures do not recapitulate the functional properties of primary neurons, such as network properties. Cultured primary murine neurons develop networks which are synchronised over large fractions of the culture, whereas neurons derived from mouse embryonic stem cells (ESCs) display only partly synchronised network activity and human pluripotent stem cells-derived neurons have mostly asynchronous network properties. Therefore, strategies to improve correspondence of derived neuronal cultures with primary neurons need to be developed to validate the use of stem cell-derived neuronal cultures as in vitro models. By combining serum-free derivation of ESCs from mouse blastocysts with neuronal differentiation of ESCs in morphogen-free adherent culture we generated neuronal networks with properties recapitulating those of mature primary cortical cultures. After 35days of differentiation ESC-derived neurons developed network activity very similar to that of mature primary cortical neurons. Importantly, ESC plating density was critical for network development. Compared to the previously published methods this protocol generated more synchronous neuronal networks, with high similarity to the networks formed in mature primary cortical culture. We have demonstrated that ESC-derived neuronal networks recapitulating key properties of mature primary cortical networks can be generated by optimising both stem cell derivation and differentiation. This validates the approach of using ESC-derived neuronal cultures for disease modelling and in vitro drug screening. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Resilience of developing brain networks to interictal epileptiform discharges is associated with cognitive outcome.

    Science.gov (United States)

    Ibrahim, George M; Cassel, Daniel; Morgan, Benjamin R; Smith, Mary Lou; Otsubo, Hiroshi; Ochi, Ayako; Taylor, Margot; Rutka, James T; Snead, O Carter; Doesburg, Sam

    2014-10-01

    The effects of interictal epileptiform discharges on neurocognitive development in children with medically-intractable epilepsy are poorly understood. Such discharges may have a deleterious effect on the brain's intrinsic connectivity networks, which reflect the organization of functional networks at rest, and in turn on neurocognitive development. Using a combined functional magnetic resonance imaging-magnetoencephalography approach, we examine the effects of interictal epileptiform discharges on intrinsic connectivity networks and neurocognitive outcome. Functional magnetic resonance imaging was used to determine the location of regions comprising various intrinsic connectivity networks in 26 children (7-17 years), and magnetoencephalography data were reconstructed from these locations. Inter-regional phase synchronization was then calculated across interictal epileptiform discharges and graph theoretical analysis was applied to measure event-related changes in network topology in the peri-discharge period. The magnitude of change in network topology (network resilience/vulnerability) to interictal epileptiform discharges was associated with neurocognitive outcomes and functional magnetic resonance imaging networks using dual regression. Three main findings are reported: (i) large-scale network changes precede and follow interictal epileptiform discharges; (ii) the resilience of network topologies to interictal discharges is associated with stronger resting-state network connectivity; and (iii) vulnerability to interictal discharges is associated with worse neurocognitive outcomes. By combining the spatial resolution of functional magnetic resonance imaging with the temporal resolution of magnetoencephalography, we describe the effects of interictal epileptiform discharges on neurophysiological synchrony in intrinsic connectivity networks and establish the impact of interictal disruption of functional networks on cognitive outcome in children with epilepsy. The

  15. Leader-follower consensus and synchronization in numerosity-constrained networks with dynamic leadership

    Science.gov (United States)

    Roy, Subhradeep; Abaid, Nicole

    2016-11-01

    In this work, we study leader-follower consensus and synchronization protocols over a stochastically switching network. The agents representing the followers can communicate with any other agent, whereas the agents serving as leaders are restricted to interact only with the other leaders. The model incorporates the phenomenon of numerosity, which limits the perceptual capacity of the agents while allowing for shuffling with whom each individual interacts at each time step. We derive closed form expressions for necessary and sufficient conditions for consensus, the rate of convergence to consensus, and conditions for stochastic synchronization in terms of the asymptotic convergence factor. We provide simulation results to validate the theoretical findings and to illustrate the dependence of this factor on system parameters. The closed form results enable us to study the factors affecting the feasibility of consensus. We show that agents' traits can be chosen for an engineered system to maximize the convergence speed and that protocol speed is enhanced as the proportion of the leaders increases in certain cases. These results may find application in the design and control of an engineered leader-follower system, where consensus or synchronization at the fastest possible rate is desired.

  16. A Synchronous Multi-Body Sensor Platform in a Wireless Body Sensor Network: Design and Implementation

    Directory of Open Access Journals (Sweden)

    Jungtae Lee

    2012-07-01

    Full Text Available Background: Human life can be further improved if diseases and disorders can be predicted before they become dangerous, by correctly recognizing signals from the human body, so in order to make disease detection more precise, various body-signals need to be measured simultaneously in a synchronized manner. Object: This research aims at developing an integrated system for measuring four signals (EEG, ECG, respiration, and PPG and simultaneously producing synchronous signals on a Wireless Body Sensor Network. Design: We designed and implemented a platform for multiple bio-signals using Bluetooth communication. Results: First, we developed a prototype board and verified the signals from the sensor platform using frequency responses and quantities. Next, we designed and implemented a lightweight, ultra-compact, low cost, low power-consumption Printed Circuit Board. Conclusion: A synchronous multi-body sensor platform is expected to be very useful in telemedicine and emergency rescue scenarios. Furthermore, this system is expected to be able to analyze the mutual effects among body signals.

  17. Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks.

    Science.gov (United States)

    Shi, Fanrong; Tuo, Xianguo; Yang, Simon X; Li, Huailiang; Shi, Rui

    2017-05-04

    Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.

  18. Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

    Science.gov (United States)

    Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

    2016-10-01

    Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

  19. Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

    Science.gov (United States)

    Hu, Jin; Zeng, Chunna

    2017-02-01

    The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

    Science.gov (United States)

    2018-01-01

    This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results. PMID:29370248

  1. Semi-global regulation of output synchronization for heterogeneous networks of non-introspective, invertible agents subject to actuator saturation

    NARCIS (Netherlands)

    Yang, Tao; Stoorvogel, Antonie Arij; Grip, H°avard Fjær; Saberi, Ali

    2014-01-01

    In this paper, we consider the semi-global regulation of output synchronization problem for heterogeneous networks of invertible linear agents subject to actuator saturation. That is, we regulate the output of each agent according to an a priori specified reference model. The network communication

  2. Consciousness, cognition and brain networks: New perspectives.

    Science.gov (United States)

    Aldana, E M; Valverde, J L; Fábregas, N

    2016-10-01

    A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. Copyright © 2014 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

  3. Synchronous Databus Network in ITER: Open source real-time network for the next nuclear fusion experiment

    Energy Technology Data Exchange (ETDEWEB)

    Boncagni, L.; Centioli, C. [Associazione EURATOM-ENEA sulla Fusione, C.R.ENEA Frascati, Rome (Italy); Iannone, F. [Associazione EURATOM-ENEA sulla Fusione, C.R.ENEA Frascati, Rome (Italy)], E-mail: francesco.iannone@frascati.enea.it; Neri, C.; Panella, M.; Pangione, L.; Riva, M. [Associazione EURATOM-ENEA sulla Fusione, C.R.ENEA Frascati, Rome (Italy); Scappaticci, M. [Dipartimento di Informatica, Sistemi e Produzione, Universita di Tor Vergata, Rome (Italy); Vitale, V. [Associazione EURATOM-ENEA sulla Fusione, C.R.ENEA Frascati, Rome (Italy); Zaccarian, L. [Dipartimento di Informatica, Sistemi e Produzione, Universita di Tor Vergata, Rome (Italy)

    2008-04-15

    The next nuclear fusion experiment, ITER, is providing the infrastructure for the optimal operation of a burning plasma, requiring feedback control of discharge parameters and on-line evaluation of computationally intensive models running in a cluster of controller nodes. Thus, the synchronization of the available information on the plasma and plant state variables among the controller nodes is a key issue for ITER. The ITER conceptual design aims to perform feedback control on a cluster of distributed controllers connected by a Synchronous Databus Network (SDN). Therefore it is mandatory to achieve a deterministic data exchange among the controller nodes with a refresh rate of at least 1 kHz and a jitter of at least 50 {mu}s. Thus, a conservative estimate of the data flow within the controller network can be 3 kSample/ms. In this paper the open source RTnet project is evaluated to meet the requirements of the SDN of ITER. A testbed involving a cluster of eight nodes connected over a standard ethernet network has been set up to simulate a distributed real-time control system. The main goal of the test is to verify the compliance of the performance with the ITER SDN requirements.

  4. Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

    Science.gov (United States)

    Shan, Qingxiao; Jun, Yang; Le Floch, Jean-Michel; Fan, Yaohui; Ivanov, Eugene N; Tobar, Michael E

    2014-07-01

    Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.

  5. A Flexible Terminal Approach to Sampled-Data Exponentially Synchronization of Markovian Neural Networks With Time-Varying Delayed Signals.

    Science.gov (United States)

    Cheng, Jun; Park, Ju H; Karimi, Hamid Reza; Shen, Hao

    2017-08-02

    This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov-Krasovskii functional and suitable integral inequality. Finally, numerical examples are provided to show the usefulness and advantages of the proposed design procedure.

  6. The Virtual Brain: a simulator of primate brain network dynamics.

    Science.gov (United States)

    Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

    2013-01-01

    We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

  7. Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

    Science.gov (United States)

    Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

    2015-10-01

    This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Effects of transient unilateral functional brain disruption on global neural network status in rats

    Directory of Open Access Journals (Sweden)

    Willem M Otte

    2014-03-01

    Full Text Available Permanent focal brain damage can have critical effects on the function of nearby as well as remote brain regions. However, the effects of transient disturbances on global brain function are largely unknown. Our goal was to develop an experimental in vivo model to map the impact of transient functional brain impairment on large-scale neural networks in the absence of structural damage.We describe a new rat model of transient functional hemispheric disruption using unilateral focal anesthesia by intracarotid pentobarbital injection. The brain’s functional status was assessed with resting-state fMRI (rs-fMRI and EEG. We performed network analysis to identify and quantify highly connected network hubs, i.e. ‘rich-club organization’, in pre- and postbarbital functional networks.Perfusion MRI data demonstrated that the catheterized carotid artery predominantly supplied the ipsilateral hemisphere, allowing for selective hemispheric brain silencing. The prebarbital baseline network displayed strong functional connectivity within and between hemispheres. Following pentobarbital injection, the disrupted hemisphere revealed increased intrahemispheric functional connectivity with concomitant decrease of interhemispheric connectivity. The bilateral functional network was characterized by a strong positive rich-club effect, which was not affected by ipsilateral disruption. Nevertheless, the rich-club value was significantly decreased in the ipsilateral hemisphere and to a lesser extent contralaterally. Loss of interhemispheric EEG synchronization supported the rs-fMRI findings.Our data support the concept that densely connected rich-club regions play a central role in global brain communication, and show that network hub configurations can be significantly affected by focal temporary functional hemispheric disruption without structural neuronal damage. Further studies with this rat model will provide essential additional insights into network

  9. Spectral properties of the temporal evolution of brain network structure

    Science.gov (United States)

    Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

    2015-12-01

    The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

  10. Exponential H(infinity) synchronization of general discrete-time chaotic neural networks with or without time delays.

    Science.gov (United States)

    Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin

    2010-08-01

    This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.

  11. Further Development of Synchronous Array Method for Ad Hoc Wireless Networks

    Directory of Open Access Journals (Sweden)

    Yingbo Hua

    2008-09-01

    Full Text Available A further development of the synchronous array method (SAM as a medium access control scheme for large-scale ad hoc wireless networks is presented. Under SAM, all transmissions of data packets between adjacent nodes are synchronized on a frame-by-frame basis, and the spacing between concurrent cochannel transmissions of data packets is properly controlled. An opportunistic SAM (O-SAM is presented which allows concurrent cochannel transmissions to be locally adaptive to channel gain variations. A distributed SAM (D-SAM is discussed that schedules all concurrent cochannel transmissions in a distributed fashion. For networks of low mobility, the control overhead required by SAM can be made much smaller than the payload. By analysis and simulation, the intranetwork throughput of O-SAM and D-SAM is evaluated. The effects of traffic load and multiple antennas on the intranetwork throughput are studied. The throughput of ALOHA is also analyzed and compared with that of O-SAM and D-SAM. By a distance-weighted throughput, a comparison of long distance transmission versus short distance transmission is also presented. The study of D-SAM reveals an important insight into the MSH-DSCH protocol adopted in IEEE 802.16 standards.

  12. The Relation Between Structure and Function in Brain Networks : A network science perspective

    NARCIS (Netherlands)

    Meier, J.M.

    2017-01-01

    Over the last two decades the field of network science has been evolving fast. Many useful applications in a wide variety of disciplines have been found. The application of network science to the brain initiated the interdisciplinary field of complex brain networks. On a macroscopic level, brain

  13. Estimating functional brain networks by incorporating a modularity prior.

    Science.gov (United States)

    Qiao, Lishan; Zhang, Han; Kim, Minjeong; Teng, Shenghua; Zhang, Limei; Shen, Dinggang

    2016-11-01

    Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct "ideal" brain networks given, for example, a set of functional magnetic resonance imaging (fMRI) time series associated with different brain regions. Although many methods have been developed, it is currently still an open field to estimate biologically meaningful and statistically robust brain networks due to our limited understanding of the human brain as well as complex noises in the observed data. Motivated by the fact that the brain is organized with modular structures, in this paper, we propose a novel functional brain network modeling scheme by encoding a modularity prior under a matrix-regularized network learning framework, and further formulate it as a sparse low-rank graph learning problem, which can be solved by an efficient optimization algorithm. Then, we apply the learned brain networks to identify patients with mild cognitive impairment (MCI) from normal controls. We achieved 89.01% classification accuracy even with a simple feature selection and classification pipeline, which significantly outperforms the conventional brain network construction methods. Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Detection of nonstationary transition to synchronized states of a neural network using recurrence analyses

    Science.gov (United States)

    Budzinski, R. C.; Boaretto, B. R. R.; Prado, T. L.; Lopes, S. R.

    2017-07-01

    We study the stability of asymptotic states displayed by a complex neural network. We focus on the loss of stability of a stationary state of networks using recurrence quantifiers as tools to diagnose local and global stabilities as well as the multistability of a coupled neural network. Numerical simulations of a neural network composed of 1024 neurons in a small-world connection scheme are performed using the model of Braun et al. [Int. J. Bifurcation Chaos 08, 881 (1998), 10.1142/S0218127498000681], which is a modified model from the Hodgkin-Huxley model [J. Phys. 117, 500 (1952)]. To validate the analyses, the results are compared with those produced by Kuramoto's order parameter [Chemical Oscillations, Waves, and Turbulence (Springer-Verlag, Berlin Heidelberg, 1984)]. We show that recurrence tools making use of just integrated signals provided by the networks, such as local field potential (LFP) (LFP signals) or mean field values bring new results on the understanding of neural behavior occurring before the synchronization states. In particular we show the occurrence of different stationary and nonstationarity asymptotic states.

  15. Brain networks that track musical structure.

    Science.gov (United States)

    Janata, Petr

    2005-12-01

    As the functional neuroimaging literature grows, it becomes increasingly apparent that music and musical activities engage diverse regions of the brain. In this paper I discuss two studies to illustrate that exactly which brain areas are observed to be responsive to musical stimuli and tasks depends on the tasks and the methods used to describe the tasks and the stimuli. In one study, subjects listened to polyphonic music and were asked to either orient their attention selectively to individual instruments or in a divided or holistic manner across multiple instruments. The network of brain areas that was recruited changed subtly with changes in the task instructions. The focus of the second study was to identify brain regions that follow the pattern of movement of a continuous melody through the tonal space defined by the major and minor keys of Western tonal music. Such an area was identified in the rostral medial prefrontal cortex. This observation is discussed in the context of other neuroimaging studies that implicate this region in inwardly directed mental states involving decisions about the self, autobiographical memory, the cognitive regulation of emotion, affective responses to musical stimuli, and familiarity judgments about musical stimuli. Together with observations that these regions are among the last to atrophy in Alzheimer disease, and that these patients appear to remain responsive to autobiographically salient musical stimuli, very early evidence is emerging from the literature for the hypothesis that the rostral medial prefrontal cortex is a node that is important for binding music with memories within a broader music-responsive network.

  16. Task-Based Cohesive Evolution of Dynamic Brain Networks

    Science.gov (United States)

    Davison, Elizabeth

    2014-03-01

    Applications of graph theory to neuroscience have resulted in significant progress towards a mechanistic understanding of the brain. Functional network representation of the brain has linked efficient network structure to psychometric intelligence and altered configurations with disease. Dynamic graphs provide us with tools to further study integral properties of the brain; specifically, the mathematical convention of hyperedges has allowed us to study the brain's cross-linked structure. Hyperedges capture the changes in network structure by identifying groups of brain regions with correlation patterns that change cohesively through time. We performed a hyperedge analysis on functional MRI data from 86 subjects and explored the cohesive evolution properties of their functional brain networks as they performed a series of tasks. Our results establish the hypergraph as a useful measure in understanding functional brain dynamics over tasks and reveal characteristic differences in the co-evolution structure of task-specific networks.

  17. Brain Networks Implicated in Seasonal Affective Disorder

    DEFF Research Database (Denmark)

    Nørgaard, Martin; Ganz, Melanie; Svarer, Claus

    2017-01-01

    Background: Seasonal Affective Disorder (SAD) is a subtype of Major Depressive Disorder characterized by seasonally occurring depression that often presents with atypical vegetative symptoms such as hypersomnia and carbohydrate craving. It has recently been shown that unlike healthy people......, patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption...

  18. Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing

    Directory of Open Access Journals (Sweden)

    Benjamin Wittevrongel

    2017-11-01

    Full Text Available Brain-Computer Interfaces (BCIs decode brain activity with the aim to establish a direct communication channel with an external device. Albeit they have been hailed to (re-establish communication in persons suffering from severe motor- and/or communication disabilities, only recently BCI applications have been challenging other assistive technologies. Owing to their considerably increased performance and the advent of affordable technological solutions, BCI technology is expected to trigger a paradigm shift not only in assistive technology but also in the way we will interface with technology. However, the flipside of the quest for accuracy and speed is most evident in EEG-based visual BCI where it has led to a gamut of increasingly complex classifiers, tailored to the needs of specific stimulation paradigms and use contexts. In this contribution, we argue that spatiotemporal beamforming can serve several synchronous visual BCI paradigms. We demonstrate this for three popular visual paradigms even without attempting to optimizing their electrode sets. For each selectable target, a spatiotemporal beamformer is applied to assess whether the corresponding signal-of-interest is present in the preprocessed multichannel EEG signals. The target with the highest beamformer output is then selected by the decoder (maximum selection. In addition to this simple selection rule, we also investigated whether interactions between beamformer outputs could be employed to increase accuracy by combining the outputs for all targets into a feature vector and applying three common classification algorithms. The results show that the accuracy of spatiotemporal beamforming with maximum selection is at par with that of the classification algorithms and interactions between beamformer outputs do not further improve that accuracy.

  19. Brain tumor segmentation with Deep Neural Networks.

    Science.gov (United States)

    Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

    2017-01-01

    In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. Global synchronization in finite time for fractional-order neural networks with discontinuous activations and time delays.

    Science.gov (United States)

    Peng, Xiao; Wu, Huaiqin; Song, Ka; Shi, Jiaxin

    2017-10-01

    This paper is concerned with the global Mittag-Leffler synchronization and the synchronization in finite time for fractional-order neural networks (FNNs) with discontinuous activations and time delays. Firstly, the properties with respect to Mittag-Leffler convergence and convergence in finite time, which play a critical role in the investigation of the global synchronization of FNNs, are developed, respectively. Secondly, the novel state-feedback controller, which includes time delays and discontinuous factors, is designed to realize the synchronization goal. By applying the fractional differential inclusion theory, inequality analysis technique and the proposed convergence properties, the sufficient conditions to achieve the global Mittag-Leffler synchronization and the synchronization in finite time are addressed in terms of linear matrix inequalities (LMIs). In addition, the upper bound of the setting time of the global synchronization in finite time is explicitly evaluated. Finally, two examples are given to demonstrate the validity of the proposed design method and theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. New Methods of Finite-Time Synchronization for a Class of Fractional-Order Delayed Neural Networks

    Directory of Open Access Journals (Sweden)

    Weiwei Zhang

    2017-01-01

    Full Text Available Finite-time synchronization for a class of fractional-order delayed neural networks with fractional order α, 0<α≤1/2 and 1/2<α<1, is investigated in this paper. Through the use of Hölder inequality, generalized Bernoulli inequality, and inequality skills, two sufficient conditions are considered to ensure synchronization of fractional-order delayed neural networks in a finite-time interval. Numerical example is given to verify the feasibility of the theoretical results.

  2. Cluster Synchronization of Stochastic Complex Networks with Markovian Switching and Time-Varying Delay via Impulsive Pinning Control

    Directory of Open Access Journals (Sweden)

    Xuan Zhou

    2014-01-01

    Full Text Available This paper studies the cluster synchronization of a kind of complex networks by means of impulsive pinning control scheme. These networks are subject to stochastic noise perturbations and Markovian switching, as well as internal and outer time-varying delays. Using the Lyapunov-Krasovskii functional, Itö’s formula, and some linear matrix inequalities (LMI, several novel sufficient conditions are obtained to guarantee the desired cluster synchronization. At the end of this writing, a numerical simulation is given to demonstrate the effectiveness of those theoretical results.

  3. Implementation of time synchronized cryogenics control system network architecture for SST-1

    Energy Technology Data Exchange (ETDEWEB)

    Patel, Rakesh J., E-mail: rpatel@ipr.res.in; Mahesuria, Gaurang; Panchal, Pradip; Panchal, Rohit; Sonara, Dasarath; Tanna, Vipul; Pradhan, Subrata

    2016-11-15

    Highlights: • SST-1 cryogenics sub-systems are 1.3 kW HRL, LN2 distribution system, current feeders system and 80 K booster system. • GUI developed in SCADA and control program developed in PLC for automation of the above sub-systems. • Implemented the cryogenics control system network to communicate all systems to InSQL server. • InSQL server configured for real time centralized process data acquisition from all connected sub-systems control nodes. • Acquired the process parameters coming from different systems at same time stamp. - Abstract: Under the SST-1 mission mandate, the several cryogenic sub-systems have been developed, upgraded and procured in prior to the SST-1 operation. New developments include 80 K Bubble type thermal shields, LN2 distribution system, LN2 booster system and current feeders system (CFS).Graphical User Interface (GUI) program developed in Wonderware SCADA and control logic program developed in Schneider make PLC for the above sub-systems. Industrial SQL server (InSQL) configured for centralized storage of real time process data coming from various control nodes of cryogenics sub-systems. The cryogenics control system network for communicating all cryogenics sub-system control nodes to InSQL server for centralized data storage and time synchronization among cryogenic sub-systems with centralized InSQL server is successfully implemented. Due to implemented time synchronization among sub-systems control nodes, it is possible to analyze the process parameters coming from different sub-systems at same time stamp. This paper describes the overview of implemented cryogenics control system network architecture for real time cryogenic process data monitor, storage and retrieval.

  4. Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay.

    Science.gov (United States)

    Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

    2017-12-01

    This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Alpha synchronization as a brain model for unconscious defense: An overview of the work of Howard Shevrin and his team.

    Science.gov (United States)

    Bazan, Ariane

    2017-10-01

    Howard Shevrin and his team have developed a stringent subliminal priming methodology, which experimentally approximates a situation of an internal, mental triggering of unconscious defense. Through a series of four studies they thus are able to bring evidence for this type of unconscious defense. With event-related potentials, three clinical studies show how synchronization of a specific brain wave, the alpha wave, known for its inhibitory function, is also induced by subliminally presented conflictual subject-specific stimuli. Therefore, alpha synchronization could serve as the brain mechanism of unconscious defense. The results only make sense if we suppose the existence of a dynamic unconscious, which has inherited childhood conflicts, and with privileged connections to neurotic symptom characteristics. Moreover, by showing that the unconscious conflict phrases, inferred by clinicians from clinical interviews, have a similar brain behavior, Shevrin and his team provide evidence that these inferences are not simply clinician-dependent subjective interpretations but also imply some form of independent mental reality. Finally, interpretation of the results has led us to propose two distinct physiological mechanisms for defense: one, unconscious defense, by alpha synchronization in connection with the drive derivatives, and another, repression, based on the indications of reality in connection with the ego. Copyright © 2017 Institute of Psychoanalysis.

  6. Synchronization unveils the organization of ecological networks with positive and negative interactions.

    Science.gov (United States)

    Girón, Andrea; Saiz, Hugo; Bacelar, Flora S; Andrade, Roberto F S; Gómez-Gardeñes, Jesús

    2016-06-01

    Network science has helped to understand the organization principles of the interactions among the constituents of large complex systems. However, recently, the high resolution of the data sets collected has allowed to capture the different types of interactions coexisting within the same system. A particularly important example is that of systems with positive and negative interactions, a usual feature appearing in social, neural, and ecological systems. The interplay of links of opposite sign presents natural difficulties for generalizing typical concepts and tools applied to unsigned networks and, moreover, poses some questions intrinsic to the signed nature of the network, such as how are negative interactions balanced by positive ones so to allow the coexistence and survival of competitors/foes within the same system? Here, we show that synchronization phenomenon is an ideal benchmark for uncovering such balance and, as a byproduct, to assess which nodes play a critical role in the overall organization of the system. We illustrate our findings with the analysis of synthetic and real ecological networks in which facilitation and competitive interactions coexist.

  7. Real-time synchronization of wireless sensor network by 1-PPS signal

    Science.gov (United States)

    Giammarini, Marco; Pieralisi, Marco; Isidori, Daniela; Concettoni, Enrico; Cristalli, Cristina; Fioravanti, Matteo

    2015-05-01

    The use of wireless sensor networks with different nodes is desirable in a smart environment, because the network setting up and installation on preexisting structures can be done without a fixed cabled infrastructure. The flexibility of the monitoring system is fundamental where the use of a considerable quantity of cables could compromise the normal exercise, could affect the quality of acquired signal and finally increase the cost of the materials and installation. The network is composed of several intelligent "nodes", which acquires data from different kind of sensors, and then store or transmit them to a central elaboration unit. The synchronization of data acquisition is the core of the real-time wireless sensor network (WSN). In this paper, we present a comparison between different methods proposed by literature for the real-time acquisition in a WSN and finally we present our solution based on 1-Pulse-Per-Second (1-PPS) signal generated by GPS systems. The sensor node developed is a small-embedded system based on ARM microcontroller that manages the acquisition, the timing and the post-processing of the data. The communications between the sensors and the master based on IEEE 802.15.4 protocol and managed by dedicated software. Finally, we present the preliminary results obtained on a 3 floor building simulator with the wireless sensors system developed.

  8. The elusive concept of brain network. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    Science.gov (United States)

    Horwitz, Barry

    2014-09-01

    As the poet John Donne said of man - "No man is an island entire of itself; every man is a piece of the continent, a part of the main." - so the neuroscience research community now says of brain areas. This is the topic that Luiz Pessoa expands upon in his thorough review of the paradigm shift that has occurred in much of brain research, especially in cognitive neuroscience [1]. His key point is made explicitly in the Abstract: "I argue that a network perspective should supplement the common strategy of understanding the brain in terms of individual regions." In his review, Pessoa covers a large range of topics, including how the network perspective changes the way in which one views the structure-function relationship between brain and behavior, the importance of context in ascertaining how a brain region functions, and the notion of emergent properties as a network feature. Also discussed is graph theory, one of the important mathematical methods used to analyze and describe network structure and function.

  9. Joint transfer of time and frequency signals and multi-point synchronization via fiber network

    Science.gov (United States)

    Nan, Cheng; Wei, Chen; Qin, Liu; Dan, Xu; Fei, Yang; You-Zhen, Gui; Hai-Wen, Cai

    2016-01-01

    A system of jointly transferring time signals with a rate of 1 pulse per second (PPS) and frequency signals of 10 MHz via a dense wavelength division multiplex-based (DWDM) fiber is demonstrated in this paper. The noises of the fiber links are suppressed and compensated for by a controlled fiber delay line. A method of calibrating and characterizing time is described. The 1PPS is synchronized by feed-forward calibrating the fiber delays precisely. The system is experimentally examined via a 110 km spooled fiber in laboratory. The frequency stabilities of the user end with compensation are 1.8×10-14 at 1 s and 2.0×10-17 at 104 s average time. The calculated uncertainty of time synchronization is 13.1 ps, whereas the direct measurement of the uncertainty is 12 ps. Next, the frequency and 1PPS are transferred via a metropolitan area optical fiber network from one central site to two remote sites with distances of 14 km and 110 km. The frequency stabilities of 14 km link reach 3.0×10-14 averaged in 1 s and 1.4×10-17 in 104 s respectively; and the stabilities of 110 km link are 8.3×10-14 and 1.7×10-17, respectively. The accuracies of synchronization are estimated to be 12.3 ps for the 14 km link and 13.1 ps for the 110 km link, respectively. Project supported by the National Natural Science Foundation of China (Grant No. 61405227).

  10. Blind Synchronization in Asynchronous UWB Networks Based on the Transmit-Reference Scheme

    Directory of Open Access Journals (Sweden)

    Leus Geert

    2006-01-01

    Full Text Available Ultra-wideband (UWB wireless communication systems are based on the transmission of extremely narrow pulses, with a duration inferior to a nanosecond. The application of transmit reference (TR to UWB systems allows to side-step channel estimation at the receiver, with a tradeoff of the effective transmission bandwidth, which is reduced by the usage of a reference pulse. Similar to CDMA systems, different users can share the same available bandwidth by means of different spreading codes. This allows the receiver to separate users, and to recover the timing information of the transmitted data packets. The nature of UWB transmissions—short, burst-like packets—requires a fast synchronization algorithm, that can accommodate several asynchronous users. Exploiting the fact that a shift in time corresponds to a phase rotation in the frequency domain, a blind and computationally effcient synchronization algorithm that takes advantage of the shift invariance structure in the frequency domain is proposed in this paper. Integer and fractional delay estimations are considered, along with a subsequent symbol estimation step. This results in a collision-avoiding multiuser algorithm, readily applicable to a fast acquisition procedure in a UWB ad hoc network.

  11. Resting-state brain organization revealed by functional covariance networks.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Zhang

    Full Text Available BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN and structural covariance network (SCN have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. METHODOLOGY AND PRINCIPAL FINDINGS: We proposed a functional covariance network (FCN method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. CONCLUSION: The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.

  12. Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays

    Directory of Open Access Journals (Sweden)

    Jin-E Zhang

    2017-01-01

    Full Text Available In this paper, the global O(t-α synchronization problem is investigated for a class of fractional-order neural networks with time delays. Taking into account both better control performance and energy saving, we make the first attempt to introduce centralized data-sampling approach to characterize the O(t-α synchronization design strategy. A sufficient criterion is given under which the drive-response-based coupled neural networks can achieve global O(t-α synchronization. It is worth noting that, by using centralized data-sampling principle, fractional-order Lyapunov-like technique, and fractional-order Leibniz rule, the designed controller performs very well. Two numerical examples are presented to illustrate the efficiency of the proposed centralized data-sampling scheme.

  13. Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

    Directory of Open Access Journals (Sweden)

    Dawei Gong

    2017-01-01

    Full Text Available The pinning synchronous problem for complex networks with interval delays is studied in this paper. First, by using an inequality which is introduced from Newton-Leibniz formula, a new synchronization criterion is derived. Second, combining Finsler’s Lemma with homogenous matrix, convergent linear matrix inequality (LMI relaxations for synchronization analysis are proposed with matrix-valued coefficients. Third, a new variable subintervals method is applied to expand the obtained results. Different from previous results, the interval delays are divided into some subdelays, which can introduce more free weighting matrices. Fourth, the results are shown as LMI, which can be easily analyzed or tested. Finally, the stability of the networks is proved via Lyapunov’s stability theorem, and the simulation of the trajectory claims the practicality of the proposed pinning control.

  14. Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching

    Directory of Open Access Journals (Sweden)

    Wuneng Zhou

    2014-01-01

    Full Text Available The problem of almost sure (a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper.

  15. High resolution wireless body area network with statistically synchronized sensor data for tracking pulse wave velocity.

    Science.gov (United States)

    Li, Kejia; Warren, Steve

    2012-01-01

    Wireless body area networks (WBANs) will take on more diverse forms in terms of their sensor combinations and communication protocols as their presence is extended to a greater number of monitoring scenarios. This paper presents an application layer protocol that solves issues caused by sensor nodes that must compete for high speed, real-time communication with the receiver. Such applications emphasize the delivery of large amounts of raw data from different sensor nodes in a time-synchronized manner, rather than channels that experience intermittent operation. An example of tracking pulse wave velocity (PWV) is introduced in this paper, where high-precision PWVs are estimated with the help of timeline recovery and feature extraction processes in MATLAB.

  16. Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

    Science.gov (United States)

    Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

    2015-03-01

    This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

  17. Sensorless Speed Control of Permanent Magnet Synchronous Motors by Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2014-01-01

    Full Text Available The sliding mode control has the merits with respect to the variation of the disturbance and robustness. In this paper, the sensorless sliding-mode observer with least mean squared error approach for permanent magnet synchronous motor (PMSM to detect the rotor position by counter electromotive force and then compute motor speed is designed and implemented. In addition, the neural network control is also used to compensate the PI gain tuning to increase the speed accuracy without regarding the errors of the current measurement and motor noise. In this paper, a digital signal processor TMS320F2812 utilizes its high-speed ADC module to get current feedback information and thus to estimate the rotor position and takes advantage of the built-in modules to achieve SVPWM current control so that the senseless speed control will be accomplished. The correctness and effectiveness of the proposed control system will be verified from the experimental results.

  18. Network Analysis of Functional Brain Connectivity Driven by Gamma-Band Auditory Steady-State Response in Auditory Hallucinations.

    Science.gov (United States)

    Ying, Jun; Zhou, Dan; Lin, Ke; Gao, Xiaorong

    The auditory steady-state response (ASSR) may reflect activity from different regions of the brain. Particularly, it was reported that the gamma-band ASSR plays an important role in working memory, speech understanding, and recognition. Traditionally, the ASSR has been determined by power spectral density analysis, which cannot detect the exact overall distributed properties of the ASSR. Functional network analysis has recently been applied in electroencephalography studies. Previous studies on resting or working state found a small-world organization of the brain network. Some researchers have studied dysfunctional networks caused by diseases. The present study investigates the brain connection networks of schizophrenia patients with auditory hallucinations during an ASSR task. A directed transfer function is utilized to estimate the brain connectivity patterns. Moreover, the structures of brain networks are analyzed by converting the connectivity matrices into graphs. It is found that for normal subjects, network connections are mainly distributed at the central and frontal-temporal regions. This indicates that the central regions act as transmission hubs of information under ASSR stimulation. For patients, network connections seem unordered. The finding that the path length was larger in patients compared to that in normal subjects under most thresholds provides insight into the structures of connectivity patterns. The results suggest that there are more synchronous oscillations that cover a long distance on the cortex but a less efficient network for patients with auditory hallucinations.

  19. Synchronization in Scale Free networks: The role of finite size effects

    CERN Document Server

    Torres, Débora; La Rocca, Cristian E; Braunstein, Lidia A

    2015-01-01

    Synchronization problems in complex networks are very often studied by researchers due to its many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, Scale Free networks with degree distribution $P(k)\\sim k^{-\\lambda}$, are widely used in research since they are ubiquitous in nature and other real systems. In this paper we focus on the surface relaxation growth model in Scale Free networks with $2.5< \\lambda <3$, and study the scaling behavior of the fluctuations, in the steady state, with the system size $N$. We find a novel behavior of the fluctuations characterized by a crossover between two regimes at a value of $N=N^*$ that depends on $\\lambda$: a logarithmic regime, found in previous research, and a constant regime. We propose a function that describes this crossover, which is in very good agreement with the simulations. We also find that, for a system size above $N^{*}$, the fluctuations decrease with $\\lambda$, which means that the synchroniza...

  20. Synchronous Wearable Wireless Body Sensor Network Composed of Autonomous Textile Nodes

    Science.gov (United States)

    Vanveerdeghem, Peter; Van Torre, Patrick; Stevens, Christiaan; Knockaert, Jos; Rogier, Hendrik

    2014-01-01

    A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system. PMID:25302808

  1. Synchronous Wearable Wireless Body Sensor Network Composed of Autonomous Textile Nodes

    Directory of Open Access Journals (Sweden)

    Peter Vanveerdeghem

    2014-10-01

    Full Text Available A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system.

  2. Survival prognostic factors for patients with synchronous brain oligometastatic non-small-cell lung carcinoma receiving local therapy

    Directory of Open Access Journals (Sweden)

    Bai H

    2016-07-01

    Full Text Available Hao Bai,1,* Jianlin Xu,1,* Haitang Yang,2,* Bo Jin,1 Yuqing Lou,1 Dan Wu,3 Baohui Han1 1Department of Pulmonary, 2Department of Pathology, 3Central Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, People’s Republic of China *These authors contributed equally to this work Introduction: Clinical evidence for patients with synchronous brain oligometastatic non-small-cell lung carcinoma is limited. We aimed to summarize the clinical data of these patients to explore the survival prognostic factors for this population. Methods: From September 1995 to July 2011, patients with 1–3 synchronous brain oligometastases, who were treated with stereotactic radiosurgery (SRS or surgical resection as the primary treatment, were identified at Shanghai Chest Hospital.Results: A total of 76 patients (22 patients underwent brain surgery as primary treatment and 54 patients received SRS were available for survival analysis. The overall survival (OS for patients treated with SRS and brain surgery as the primary treatment were 12.6 months (95% confidence interval [CI] 10.3–14.9 and 16.4 months (95% CI 8.8–24.1, respectively (adjusted hazard ratio =0.59, 95% CI 0.33–1.07, P=0.08. Among 76 patients treated with SRS or brain surgery, 21 patients who underwent primary tumor resection did not experience a significantly improved OS (16.4 months, 95% CI 9.6–23.2, compared with those who did not undergo resection (11.9 months, 95% CI 9.7–14.0; adjusted hazard ratio =0.81, 95% CI 0.46–1.44, P=0.46. Factors associated with survival benefits included stage I–II of primary lung tumor and solitary brain metastasis. Conclusion: There was no significant difference in OS for patients with synchronous brain oligometastasis receiving SRS or surgical resection. Among this population, the number of brain metastases and stage of primary lung disease were the factors associated with a survival benefit. Keywords: non-small-cell lung carcinoma

  3. Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

    Science.gov (United States)

    Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

    In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

  4. To Enhance Collaborative Learning and Practice Network Knowledge with a Virtualization Laboratory and Online Synchronous Discussion

    Directory of Open Access Journals (Sweden)

    Wu-Yuin Hwang

    2014-09-01

    Full Text Available Recently, various computer networking courses have included additional laboratory classes in order to enhance students’ learning achievement. However, these classes need to establish a suitable laboratory where each student can connect network devices to configure and test functions within different network topologies. In this case, the Linux operating system can be used to operate network devices and the virtualization technique can include multiple OSs for supporting a significant number of students. In previous research, the virtualization application was successfully applied in a laboratory, but focused only on individual assignments. The present study extends previous research by designing the Networking Virtualization-Based Laboratory (NVBLab, which requires collaborative learning among the experimental students. The students were divided into an experimental group and a control group for the experiment. The experimental group performed their laboratory assignments using NVBLab, whereas the control group completed them on virtual machines (VMs that were installed on their personal computers. Moreover, students using NVBLab were provided with an online synchronous discussion (OSD feature that enabled them to communicate with others. The laboratory assignments were divided into two parts: Basic Labs and Advanced Labs. The results show that the experimental group significantly outperformed the control group in two Advanced Labs and the post-test after Advanced Labs. Furthermore, the experimental group’s activities were better than those of the control group based on the total average of the command count per laboratory. Finally, the findings of the interviews and questionnaires with the experimental group reveal that NVBLab was helpful during and after laboratory class.

  5. Complex brain networks: From topological communities to clustered ...

    Indian Academy of Sciences (India)

    Abstract. Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. A challenging task is to understand the implications of such network structures on the functional organisation of the brain activ- ities. We investigate synchronisation dynamics on the ...

  6. Mapping human whole-brain structural networks with diffusion MRI.

    Directory of Open Access Journals (Sweden)

    Patric Hagmann

    Full Text Available Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

  7. A Network Model of the Periodic Synchronization Process in the Dynamics of Calcium Concentration in GnRH Neurons

    Science.gov (United States)

    2013-01-01

    Mathematical neuroendocrinology is a branch of mathematical neurosciences that is specifically interested in endocrine neurons, which have the uncommon ability of secreting neurohormones into the blood. One of the most striking features of neuroendocrine networks is their ability to exhibit very slow rhythms of neurosecretion, on the order of one or several hours. A prototypical instance is that of the pulsatile secretion pattern of GnRH (gonadotropin releasing hormone), the master hormone controlling the reproductive function, whose origin remains a puzzle issue since its discovery in the seventies. In this paper, we investigate the question of GnRH neuron synchronization on a mesoscopic scale, and study how synchronized events in calcium dynamics can arise from the average electric activity of individual neurons. We use as reference seminal experiments performed on embryonic GnRH neurons from rhesus monkeys, where calcium imaging series were recorded simultaneously in tens of neurons, and which have clearly shown the occurrence of synchronized calcium peaks associated with GnRH pulses, superposed on asynchronous, yet oscillatory individual background dynamics. We design a network model by coupling 3D individual dynamics of FitzHugh–Nagumo type. Using phase-plane analysis, we constrain the model behavior so that it meets qualitative and quantitative specifications derived from the experiments, including the precise control of the frequency of the synchronization episodes. In particular, we show how the time scales of the model can be tuned to fit the individual and synchronized time scales of the experiments. Finally, we illustrate the ability of the model to reproduce additional experimental observations, such as partial recruitment of cells within the synchronization process or the occurrence of doublets of synchronization. PMID:23574739

  8. The Brain Network Underpinning Novel Melody Creation.

    Science.gov (United States)

    Adhikari, Bhim M; Norgaard, Martin; Quinn, Kristen M; Ampudia, Jenine; Squirek, Justin; Dhamala, Mukesh

    2016-12-01

    Musical improvisation offers an excellent experimental paradigm for the study of real-time human creativity. It involves moment-to-moment decision-making, monitoring of one's performance, and utilizing external feedback to spontaneously create new melodies or variations on a melody. Recent neuroimaging studies have begun to study the brain activity during musical improvisation, aiming to unlock the mystery of human creativity. What brain resources come together and how these are utilized during musical improvisation are not well understood. To help answer these questions, we recorded electroencephalography (EEG) signals from 19 experienced musicians while they played or imagined short isochronous learned melodies and improvised on those learned melodies. These four conditions (Play-Prelearned, Play-Improvised, Imagine-Prelearned, Imagine-Improvised) were randomly interspersed in a total of 300 trials per participant. From the sensor-level EEG, we found that there were power differences in the alpha (8-12 Hz) and beta (13-30 Hz) bands in separate clusters of frontal, parietal, temporal, and occipital electrodes. Using EEG source localization and dipole modeling methods for task-related signals, we identified the locations and network activities of five sources: the left superior frontal gyrus (L SFG), supplementary motor area (SMA), left inferior parietal lobule (L IPL), right dorsolateral prefrontal cortex, and right superior temporal gyrus. During improvisation, the network activity between L SFG, SMA, and L IPL was significantly less than during the prelearned conditions. Our results support the general idea that attenuated cognitive control facilitates the production of creative output.

  9. Brain-on-a-chip integrated neuronal networks

    NARCIS (Netherlands)

    Xie, Sijia

    2016-01-01

    The brain-on-a-chip technology aims to provide an efficient and economic in vitro platform for brain disease study. In the well-known literature on brain-on-a-chip systems, nonstructured surfaces were conventionally used for the cell attachment in a culture chamber, therefore the neuronal networks

  10. Resting-state EEG oscillatory dynamics in fragile X syndrome: abnormal functional connectivity and brain network organization.

    Directory of Open Access Journals (Sweden)

    Melle J W van der Molen

    Full Text Available Disruptions in functional connectivity and dysfunctional brain networks are considered to be a neurological hallmark of neurodevelopmental disorders. Despite the vast literature on functional brain connectivity in typical brain development, surprisingly few attempts have been made to characterize brain network integrity in neurodevelopmental disorders. Here we used resting-state EEG to characterize functional brain connectivity and brain network organization in eight males with fragile X syndrome (FXS and 12 healthy male controls. Functional connectivity was calculated based on the phase lag index (PLI, a non-linear synchronization index that is less sensitive to the effects of volume conduction. Brain network organization was assessed with graph theoretical analysis. A decrease in global functional connectivity was observed in FXS males for upper alpha and beta frequency bands. For theta oscillations, we found increased connectivity in long-range (fronto-posterior and short-range (frontal-frontal and posterior-posterior clusters. Graph theoretical analysis yielded evidence of increased path length in the theta band, suggesting that information transfer between brain regions is particularly impaired for theta oscillations in FXS. These findings are discussed in terms of aberrant maturation of neuronal oscillatory dynamics, resulting in an imbalance in excitatory and inhibitory neuronal circuit activity.

  11. Brain and cognitive reserve: Translation via network control theory.

    Science.gov (United States)

    Medaglia, John Dominic; Pasqualetti, Fabio; Hamilton, Roy H; Thompson-Schill, Sharon L; Bassett, Danielle S

    2017-04-01

    Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  12. Scholastic performance and functional connectivity of brain networks in children.

    Directory of Open Access Journals (Sweden)

    Laura Chaddock-Heyman

    Full Text Available One of the keys to understanding scholastic success is to determine the neural processes involved in school performance. The present study is the first to use a whole-brain connectivity approach to explore whether functional connectivity of resting state brain networks is associated with scholastic performance in seventy-four 7- to 9-year-old children. We demonstrate that children with higher scholastic performance across reading, math and language have more integrated and interconnected resting state networks, specifically the default mode network, salience network, and frontoparietal network. To add specificity, core regions of the dorsal attention and visual networks did not relate to scholastic performance. The results extend the cognitive role of brain networks in children as well as suggest the importance of network connectivity in scholastic success.

  13. Deep brain stimulation modulates synchrony within spatially and spectrally distinct resting state networks in Parkinson's disease.

    Science.gov (United States)

    Oswal, Ashwini; Beudel, Martijn; Zrinzo, Ludvic; Limousin, Patricia; Hariz, Marwan; Foltynie, Tom; Litvak, Vladimir; Brown, Peter

    2016-05-01

    Chronic dopamine depletion in Parkinson's disease leads to progressive motor and cognitive impairment, which is associated with the emergence of characteristic patterns of synchronous oscillatory activity within cortico-basal-ganglia circuits. Deep brain stimulation of the subthalamic nucleus is an effective treatment for Parkinson's disease, but its influence on synchronous activity in cortico-basal-ganglia loops remains to be fully characterized. Here, we demonstrate that deep brain stimulation selectively suppresses certain spatially and spectrally segregated resting state subthalamic nucleus-cortical networks. To this end we used a validated and novel approach for performing simultaneous recordings of the subthalamic nucleus and cortex using magnetoencephalography (during concurrent subthalamic nucleus deep brain stimulation). Our results highlight that clinically effective subthalamic nucleus deep brain stimulation suppresses synchrony locally within the subthalamic nucleus in the low beta oscillatory range and furthermore that the degree of this suppression correlates with clinical motor improvement. Moreover, deep brain stimulation relatively selectively suppressed synchronization of activity between the subthalamic nucleus and mesial premotor regions, including the supplementary motor areas. These mesial premotor regions were predominantly coupled to the subthalamic nucleus in the high beta frequency range, but the degree of deep brain stimulation-associated suppression in their coupling to the subthalamic nucleus was not found to correlate with motor improvement. Beta band coupling between the subthalamic nucleus and lateral motor areas was not influenced by deep brain stimulation. Motor cortical coupling with subthalamic nucleus predominantly involved driving of the subthalamic nucleus, with those drives in the higher beta frequency band having much shorter net delays to subthalamic nucleus than those in the lower beta band. These observations raise the

  14. Changes of the directional brain networks related with brain plasticity in patients with long-term unilateral sensorineural hearing loss.

    Science.gov (United States)

    Zhang, G-Y; Yang, M; Liu, B; Huang, Z-C; Li, J; Chen, J-Y; Chen, H; Zhang, P-P; Liu, L-J; Wang, J; Teng, G-J

    2016-01-28

    Previous studies often report that early auditory deprivation or congenital deafness contributes to cross-modal reorganization in the auditory-deprived cortex, and this cross-modal reorganization limits clinical benefit from cochlear prosthetics. However, there are inconsistencies among study results on cortical reorganization in those subjects with long-term unilateral sensorineural hearing loss (USNHL). It is also unclear whether there exists a similar cross-modal plasticity of the auditory cortex for acquired monaural deafness and early or congenital deafness. To address this issue, we constructed the directional brain functional networks based on entropy connectivity of resting-state functional MRI and researched changes of the networks. Thirty-four long-term USNHL individuals and seventeen normally hearing individuals participated in the test, and all USNHL patients had acquired deafness. We found that certain brain regions of the sensorimotor and visual networks presented enhanced synchronous output entropy connectivity with the left primary auditory cortex in the left long-term USNHL individuals as compared with normally hearing individuals. Especially, the left USNHL showed more significant changes of entropy connectivity than the right USNHL. No significant plastic changes were observed in the right USNHL. Our results indicate that the left primary auditory cortex (non-auditory-deprived cortex) in patients with left USNHL has been reorganized by visual and sensorimotor modalities through cross-modal plasticity. Furthermore, the cross-modal reorganization also alters the directional brain functional networks. The auditory deprivation from the left or right side generates different influences on the human brain. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

  15. Disrupted functional brain networks in autistic toddlers

    NARCIS (Netherlands)

    Boersma, M.; Kemner, C.; Reus, M.A. de; Collin, G; Snijders, T.M.; Hofman, D.; Buitelaar, J.K.; Stam, C.J.; Heuvel, M.P. van den

    2013-01-01

    Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions

  16. Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.

    Science.gov (United States)

    Latteri, Alberta; Arena, Paolo; Mazzone, Paolo

    2011-04-15

    Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized. To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort

  17. Traffic Adaptive Synchronized Cluster Based MAC Protocol for Cognitive Radio Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Sultana Sahelee

    2017-01-01

    Full Text Available In wireless communication, Cognitive Radio Network (CRN is the contemporary research area to improve efficiency and spectrum utilization. It is structured with both licensed users and unlicensed users. In CRN, unlicensed users also called Cognitive Radio (CR users are permitted to utilize the free/idle of licensed channels without harmful interference to licensed users. However, accessing idle channels is the big challenging issue due to licensed users’ activities. A large number of cluster based MAC protocol have been proposed to solve this issue. In this paper, we have come up with a Traffic Adaptive Synchronized Cluster Based MAC Protocol for Cognitive Radio Ad Hoc Network, with the target of creating cluster structure more vigorous to the licensed users’ channel re-occupancy actions, maximize throughput, and minimize switching delay, so that CR users be able to use the idle spectrum more efficiently. In our protocol, clusters are formed according to Cluster Identification Channel (CIC and inter-communication is completed without gateway nodes. Finally, we have analysed and implemented our protocol through simulation and it provides better performance in terms of different performance metrics.

  18. Source-synchronous networks-on-chip circuit and architectural interconnect modeling

    CERN Document Server

    Mandal, Ayan; Mahapatra, Rabi

    2014-01-01

    This book describes novel methods for network-on-chip (NoC) design, using source-synchronous high-speed resonant clocks.  The authors discuss NoCs from the bottom up, providing circuit level details, before providing architectural simulations. As a result, readers will get a complete picture of how a NoC can be designed and optimized.  Using the methods described in this book, readers are enabled to design NoCs that are 5X better than existing approaches in terms of latency and throughput and can also sustain a significantly greater amount of traffic.   • Describes novel methods for high-speed network-on-chip (NoC) design; • Enables readers to understand NoC design from both circuit and architectural levels; • Provides circuit-level details of the NoC (including clocking, router design), along with a high-speed, resonant clocking style which is used in the NoC; • Includes architectural simulations of the NoC, demonstrating significantly superior performance over the state-of-the-art.

  19. Emergence of modular structure in a large-scale brain network with interactions between dynamics and connectivity

    Directory of Open Access Journals (Sweden)

    Cornelis Jan Stam

    2010-09-01

    Full Text Available A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an EEG / MEG like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i synchronization dependent plasticity (SDP and (ii growth dependent plasticity (GDP. In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions.

  20. Emergence of Modular Structure in a Large-Scale Brain Network with Interactions between Dynamics and Connectivity.

    Science.gov (United States)

    Stam, Cornelis J; Hillebrand, Arjan; Wang, Huijuan; Van Mieghem, Piet

    2010-01-01

    A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an electroencephalography/magnetoencephalography like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i) synchronization dependent plasticity (SDP) and (ii) growth dependent plasticity (GDP). In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions.

  1. Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method.

    Science.gov (United States)

    Li, Xuanying; Li, Xiaotong; Hu, Cheng

    2017-12-01

    In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Connectivity, excitability and activity patterns in neuronal networks

    NARCIS (Netherlands)

    le Feber, Jakob; Stoyanova, Irina; Chiappalone, Michela

    2014-01-01

    Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods

  3. Finite-time stability and synchronization for memristor-based fractional-order Cohen-Grossberg neural network

    Science.gov (United States)

    Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhao, Hui

    2016-09-01

    In this paper, we study the finite-time stability and synchronization problem of a class of memristor-based fractional-order Cohen-Grossberg neural network (MFCGNN) with the fractional order α ∈ (0,1 ]. We utilize the set-valued map and Filippov differential inclusion to treat MFCGNN because it has discontinuous right-hand sides. By using the definition of Caputo fractional-order derivative, the definitions of finite-time stability and synchronization, Gronwall's inequality and linear feedback controller, two new sufficient conditions are derived to ensure the finite-time stability of our proposed MFCGNN and achieve the finite-time synchronization of drive-response systems which are constituted by MFCGNNs. Finally, two numerical simulations are presented to verify the rightness of our proposed theorems.

  4. Human brain EEG indices of emotions: delineating responses to affective vocalizations by measuring frontal theta event-related synchronization.

    Science.gov (United States)

    Bekkedal, Marni Y V; Rossi, John; Panksepp, Jaak

    2011-10-01

    At present there is no direct brain measure of basic emotional dynamics from the human brain. EEG provides non-invasive approaches for monitoring brain electrical activity to emotional stimuli. Event-related desynchronization/synchronization (ERD/ERS) analysis, based on power shifts in specific frequency bands, has some potential as a method for differentiating responses to basic emotions as measured during brief presentations of affective stimuli. Although there appears to be fairly consistent theta ERS in frontal regions of the brain during the earliest phases of processing affective auditory stimuli, the patterns do not readily distinguish between specific emotions. To date it has not been possible to consistently differentiate brain responses to emotion-specific affective states or stimuli, and some evidence to suggests the theta ERS more likely measures general arousal processes rather than yielding veridical indices of specific emotional states. Perhaps cortical EEG patterns will never be able to be used to distinguish discrete emotional states from the surface of the brain. The implications and limitations of such approaches for understanding human emotions are discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. Resting state brain networks and their implications in neurodegenerative disease

    Science.gov (United States)

    Sohn, William S.; Yoo, Kwangsun; Kim, Jinho; Jeong, Yong

    2012-10-01

    Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer's disease, frontotemporal lobar degeneration and Parkinson's disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.

  6. Macroscopic networks in the human brain: mapping connectivity in healthy and damaged brains

    NARCIS (Netherlands)

    Nijhuis, E.H.J.

    2013-01-01

    The human brain contains a network of interconnected neurons. Recent advances in functional and structural in-vivo magnetic resonance neuroimaging (MRI) techniques have provided opportunities to model the networks of the human brain on a macroscopic scale. This dissertation investigates the

  7. EEG-based research on brain functional networks in cognition.

    Science.gov (United States)

    Wang, Niannian; Zhang, Li; Liu, Guozhong

    2015-01-01

    Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders.

  8. Brain and Social Networks: Fundamental Building Blocks of Human Experience.

    Science.gov (United States)

    Falk, Emily B; Bassett, Danielle S

    2017-09-01

    How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Role of physical and mental training in brain network configuration

    Directory of Open Access Journals (Sweden)

    Philip P. Foster

    2015-06-01

    Full Text Available Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of energy cost-driven small-world network disorder as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement produces a reconfiguration of brain networks into greater small-worldness. Creation of synaptic connections in a macro-network, and, at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF. The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brainbrain in such trainings? What is the respective role of independent mental, physical or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.. However, mental training, meditation or virtual reality (films, games require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brainbrain com. molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g. amyotrophic lateral sclerosis, traumatism also achieve successful cognitive enhancement albeit they may only elicit mental practice

  10. An efficient algorithm for computing fixed length attractors based on bounded model checking in synchronous Boolean networks with biochemical applications.

    Science.gov (United States)

    Li, X Y; Yang, G W; Zheng, D S; Guo, W S; Hung, W N N

    2015-04-28

    Genetic regulatory networks are the key to understanding biochemical systems. One condition of the genetic regulatory network under different living environments can be modeled as a synchronous Boolean network. The attractors of these Boolean networks will help biologists to identify determinant and stable factors. Existing methods identify attractors based on a random initial state or the entire state simultaneously. They cannot identify the fixed length attractors directly. The complexity of including time increases exponentially with respect to the attractor number and length of attractors. This study used the bounded model checking to quickly locate fixed length attractors. Based on the SAT solver, we propose a new algorithm for efficiently computing the fixed length attractors, which is more suitable for large Boolean networks and numerous attractors' networks. After comparison using the tool BooleNet, empirical experiments involving biochemical systems demonstrated the feasibility and efficiency of our approach.

  11. Scalable brain network construction on white matter fibers

    Science.gov (United States)

    Chung, Moo K.; Adluru, Nagesh; Dalton, Kim M.; Alexander, Andrew L.; Davidson, Richard J.

    2011-03-01

    DTI offers a unique opportunity to characterize the structural connectivity of the human brain non-invasively by tracing white matter fiber tracts. Whole brain tractography studies routinely generate up to half million tracts per brain, which serves as edges in an extremely large 3D graph with up to half million edges. Currently there is no agreed-upon method for constructing the brain structural network graphs out of large number of white matter tracts. In this paper, we present a scalable iterative framework called the ɛ-neighbor method for building a network graph and apply it to testing abnormal connectivity in autism.

  12. Mesoscopic segregation of excitation and inhibition in a brain network model.

    Directory of Open Access Journals (Sweden)

    Daniel Malagarriga

    2015-02-01

    Full Text Available Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.

  13. Sampled-Data Synchronization of Markovian Coupled Neural Networks With Mode Delays Based on Mode-Dependent LKF.

    Science.gov (United States)

    Wang, Junyi; Zhang, Huaguang; Wang, Zhanshan; Liu, Zhenwei

    This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.

  14. Development of large-scale functional brain networks in children.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2009-07-01

    Full Text Available The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y and 22 young-adults (ages 19-22 y. Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

  15. Role of physical and mental training in brain network configuration.

    Science.gov (United States)

    Foster, Philip P

    2015-01-01

    It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brainbrain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  16. Nutritional status, brain network organization, and general intelligence.

    Science.gov (United States)

    Zamroziewicz, Marta K; Talukdar, M Tanveer; Zwilling, Chris E; Barbey, Aron K

    2017-11-01

    The high energy demands of the brain underscore the importance of nutrition in maintaining brain health and further indicate that aspects of nutrition may optimize brain health, in turn enhancing cognitive performance. General intelligence represents a critical cognitive ability that has been well characterized by cognitive neuroscientists and psychologists alike, but the extent to which a driver of brain health, namely nutritional status, impacts the neural mechanisms that underlie general intelligence is not understood. This study therefore examined the relationship between the intrinsic connectivity networks supporting general intelligence and nutritional status, focusing on nutrients known to impact the metabolic processes that drive brain function. We measured general intelligence, favorable connective architecture of seven intrinsic connectivity networks, and seventeen plasma phospholipid monounsaturated and saturated fatty acids in a sample of 99 healthy, older adults. A mediation analysis was implemented to investigate the relationship between empirically derived patterns of fatty acids, general intelligence, and underlying intrinsic connectivity networks. The mediation analysis revealed that small world propensity within one intrinsic connectivity network supporting general intelligence, the dorsal attention network, was promoted by a pattern of monounsaturated fatty acids. These results suggest that the efficiency of functional organization within a core network underlying general intelligence is influenced by nutritional status. This report provides a novel connection between nutritional status and functional network efficiency, and further supports the promise and utility of functional connectivity metrics in studying the impact of nutrition on cognitive and brain health. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Episodic memory in aspects of large-scale brain networks

    Directory of Open Access Journals (Sweden)

    Woorim eJeong

    2015-08-01

    Full Text Available Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network. Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network. Altered patterns of functional connectivity among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment.

  18. Episodic memory in aspects of large-scale brain networks

    Science.gov (United States)

    Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

    2015-01-01

    Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

  19. The role of local field potential coupling in epileptic synchronization.

    Science.gov (United States)

    Wu, Jiongxing; Yang, Heng; Peng, Yufeng; Fang, Liangjuan; Zheng, Wen; Song, Zhi

    2013-03-15

    (1) Neuronal synchronization underlies brain functioning, and it seems possible that blocking excessive synchronization in an epileptic neural network could reduce or even control seizures. (2) Local field potential coupling is a very common phenomenon during synchronization in networks. Removal of neurons or neuronal networks that are coupled can significantly alter the extracellular field potential. Interventions of coupling mediated by local field potentials could result in desynchronization of epileptic seizures. (3) The synchronized electrical activity generated by neurons is sensitive to changes in the size of the extracellular space, which affects the efficiency of field potential transmission and the threshold of cell excitability. (4) Manipulations of the field potential fluctuations could help block synchronization at seizure onset.

  20. New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network

    Directory of Open Access Journals (Sweden)

    Meng-Hui Wang

    2014-10-01

    Full Text Available A hybrid method comprising a chaos synchronization (CS-based detection scheme and an Extension Neural Network (ENN classification algorithm is proposed for power quality monitoring and analysis. The new method can detect minor changes in signals of the power systems. Likewise, prominent characteristics of system signal disturbance can be extracted by this technique. In the proposed approach, the CS-based detection method is used to extract three fundamental characteristics of the power system signal and an ENN-based clustering scheme is then applied to detect the state of the signal, i.e., normal, voltage sag, voltage swell, interruption or harmonics. The validity of the proposed method is demonstrated by means of simulations given the use of three different chaotic systems, namely Lorenz, New Lorenz and Sprott. The simulation results show that the proposed method achieves a high detection accuracy irrespective of the chaotic system used or the presence of noise. The proposed method not only achieves higher detection accuracy than existing methods, but also has low computational cost, an improved robustness toward noise, and improved scalability. As a result, it provides an ideal solution for the future development of hand-held power quality analyzers and real-time detection devices.

  1. Disrupted functional brain networks in autistic toddlers

    OpenAIRE

    Boersma, M.; Kemner, C.; M. de Reus; Collin, G; Snijders, T.; Hofman, D.; Buitelaar, J.; Stam, C.; van den Heuvel, M

    2013-01-01

    Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions and aberrant basic information processing. Aberrant brain connectivity patterns have indeed been hypothesized to be a key neural underpinning of autism. In this study, graph analytical tools are used...

  2. WHOLE BRAIN GROUP NETWORK ANALYSIS USING NETWORK BIAS AND VARIANCE PARAMETERS.

    Science.gov (United States)

    Akhondi-Asl, Alireza; Hans, Arne; Scherrer, Benoit; Peters, Jurriaan M; Warfield, Simon K

    2012-05-01

    The disruption of normal function and connectivity of neural circuits is common across many diseases and disorders of the brain. This disruptive effect can be studied and analyzed using the brain's complex functional and structural connectivity network. Complex network measures from the field of graph theory have been used for this purpose in the literature. In this paper we have introduced a new approach for analyzing the brain connectivity network. In our approach the true connectivity network and each subject's bias and variance are estimated using a population of patients and healthy controls. These parameters can then be used to compare two groups of brain networks. We have used this approach for the comparison of the resting state functional MRI network of pediatric Tuberous Sclerosis Complex (TSC) patients and healthy subjects. We have shown that a significant difference between the two groups can be found. For validation, we have compared our findings with three well known complex network measures.

  3. Smart Brain Hemorrhage Diagnosis Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Santosh H. Suryawanshi

    2015-08-01

    Full Text Available Abstract The fundamental motivation behind this study is to identify the brain hemorrhage and to give accurate treatment so that death rate because of brain hemorrhage can be reduced. This project investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed method and feeding of the appropriate inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages can be used to verify expert diagnosis and also as learning tool for trainee radiologists to minimize errors in current methods.

  4. Three-dimensional network of Drosophila brain hemisphere

    CERN Document Server

    Mizutani, Ryuta; Takeuchi, Akihisa; Uesugi, Kentaro; Suzuki, Yoshio

    2016-01-01

    The first step to understanding brain function is to determine the brain's network structure. We report a three-dimensional analysis of the brain network of the fruit fly Drosophila melanogaster by synchrotron-radiation tomographic microscopy. A skeletonized wire model of the left half of the brain network was built by tracing the three-dimensional distribution of X-ray absorption coefficients. The obtained models of neuronal processes were classified into groups on the basis of their three-dimensional structures. These classified groups correspond to neuronal tracts that send long-range projections or repeated structures of the optic lobe. The skeletonized model is also composed of neuronal processes that could not be classified into the groups. The distribution of these unclassified structures correlates with the distribution of contacts between neuronal processes. This suggests that neurons that cannot be classified into typical structures should play important roles in brain functions. The quantitative de...

  5. Brain connectivity dynamics during social interaction reflect social network structure.

    Science.gov (United States)

    Schmälzle, Ralf; Brook O'Donnell, Matthew; Garcia, Javier O; Cascio, Christopher N; Bayer, Joseph; Bassett, Danielle S; Vettel, Jean M; Falk, Emily B

    2017-05-16

    Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants' friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics.

  6. Breakdown of the brain's functional network modularity with awareness

    National Research Council Canada - National Science Library

    Godwin, Douglass; Barry, Robert L; Marois, René

    2015-01-01

    ... performed a simple masked target detection task. We found that awareness of a visual target is associated with a degradation of the modularity of the brain's functional networks brought about by an increase in intermodular functional connectivity...

  7. Human brain networks function in connectome-specific harmonic waves.

    Science.gov (United States)

    Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

    2016-01-21

    A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.

  8. Synchronous digital implementation of the AER communication scheme for emulating large-scale spiking neural networks models

    OpenAIRE

    Moreno Aróstegui, Juan Manuel; Madrenas Boadas, Jordi; Kotynia, L.

    2009-01-01

    In this paper we shall present a fully synchronous digital implementation of the Address Event Representation (AER) communication scheme that has been used in the PERPLEXUS chip in order to permit the emulation of large-scale biologically inspired spiking neural networks models. By introducing specific commands in the AER protocol it is possible to distribute the AER bus among a large number of chips where the functionality of the spiking neurons is being emulated. A c...

  9. Dynamical responses to external stimuli for both cases of excitatory and inhibitory synchronization in a complex neuronal network.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2017-10-01

    For studying how dynamical responses to external stimuli depend on the synaptic-coupling type, we consider two types of excitatory and inhibitory synchronization (i.e., synchronization via synaptic excitation and inhibition) in complex small-world networks of excitatory regular spiking (RS) pyramidal neurons and inhibitory fast spiking (FS) interneurons. For both cases of excitatory and inhibitory synchronization, effects of synaptic couplings on dynamical responses to external time-periodic stimuli S(t) (applied to a fraction of neurons) are investigated by varying the driving amplitude A of S(t). Stimulated neurons are phase-locked to external stimuli for both cases of excitatory and inhibitory couplings. On the other hand, the stimulation effect on non-stimulated neurons depends on the type of synaptic coupling. The external stimulus S(t) makes a constructive effect on excitatory non-stimulated RS neurons (i.e., it causes external phase lockings in the non-stimulated sub-population), while S(t) makes a destructive effect on inhibitory non-stimulated FS interneurons (i.e., it breaks up original inhibitory synchronization in the non-stimulated sub-population). As results of these different effects of S(t), the type and degree of dynamical response (e.g., synchronization enhancement or suppression), characterized by the dynamical response factor [Formula: see text] (given by the ratio of synchronization degree in the presence and absence of stimulus), are found to vary in a distinctly different way, depending on the synaptic-coupling type. Furthermore, we also measure the matching degree between the dynamics of the two sub-populations of stimulated and non-stimulated neurons in terms of a "cross-correlation" measure [Formula: see text]. With increasing A, based on [Formula: see text], we discuss the cross-correlations between the two sub-populations, affecting the dynamical responses to S(t).

  10. Boolean network approach to negative feedback loops of the p53 pathways: synchronized dynamics and stochastic limit cycles.

    Science.gov (United States)

    Ge, Hao; Qian, Min

    2009-01-01

    Deterministic and stochastic Boolean network models are built for the dynamics of negative feedback loops of the p53 pathways. It is shown that the main function of the negative feedback in the p53 pathways is to keep p53 at a low steady state level, and each sequence of protein states in the negative feedback loops, is globally attracted to a closed cycle of the p53 dynamics after being perturbed by outside signal (e.g., DNA damage). Our theoretical and numerical studies show that both the biological stationary state and the biological oscillation after being perturbed are stable for a wide range of noise level. Applying the mathematical circulation theory of Markov chains, we investigate their stochastic synchronized dynamics and by comparing the network dynamics of the stochastic model with its corresponding deterministic network counterpart, a dominant circulation in the stochastic model is the natural generalization of the deterministic limit cycle in the deterministic system. Moreover, the period of the main peak in the power spectrum, which is in common use to characterize the synchronized dynamics, perfectly corresponds to the number of states in the main cycle with dominant circulation. Such a large separation in the magnitude of the circulations--between a dominant, main cycle and the rest--gives rise to the stochastic synchronization phenomenon.

  11. Mnemonic Training Reshapes Brain Networks to Support Superior Memory

    NARCIS (Netherlands)

    Dresler, M.; Shirer, W.R.; Konrad, B.N.; Muller, N.C.J.; Wagner, I.; Fernandez, G.S.E.; Czisch, M.; Greicius, M.D.

    2017-01-01

    Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world's most successful memory athletes and matched controls with fMRI

  12. Gender differences in brain networks supporting empathy.

    Science.gov (United States)

    Schulte-Rüther, Martin; Markowitsch, Hans J; Shah, N Jon; Fink, Gereon R; Piefke, Martina

    2008-08-01

    Females frequently score higher on standard tests of empathy, social sensitivity, and emotion recognition than do males. It remains to be clarified, however, whether these gender differences are associated with gender specific neural mechanisms of emotional social cognition. We investigated gender differences in an emotion attribution task using functional magnetic resonance imaging. Subjects either focused on their own emotional response to emotion expressing faces (SELF-task) or evaluated the emotional state expressed by the faces (OTHER-task). Behaviorally, females rated SELF-related emotions significantly stronger than males. Across the sexes, SELF- and OTHER-related processing of facial expressions activated a network of medial and lateral prefrontal, temporal, and parietal brain regions involved in emotional perspective taking. During SELF-related processing, females recruited the right inferior frontal cortex and superior temporal sulcus stronger than males. In contrast, there was increased neural activity in the left temporoparietal junction in males (relative to females). When performing the OTHER-task, females showed increased activation of the right inferior frontal cortex while there were no differential activations in males. The data suggest that females recruit areas containing mirror neurons to a higher degree than males during both SELF- and OTHER-related processing in empathic face-to-face interactions. This may underlie facilitated emotional "contagion" in females. Together with the observation that males differentially rely on the left temporoparietal junction (an area mediating the distinction between the SELF and OTHERS) the data suggest that females and males rely on different strategies when assessing their own emotions in response to other people.

  13. Brain activity during bladder filling and pelvic floor muscle contractions: a study using functional magnetic resonance imaging and synchronous urodynamics.

    Science.gov (United States)

    Krhut, Jan; Holy, Petr; Tintera, Jaroslav; Zachoval, Roman; Zvara, Peter

    2014-02-01

    To map the brain activity during bladder filling by functional magnetic resonance imaging using a refined scanning protocol including synchronous urodynamics and pelvic floor muscle contractions. A total of 23 healthy female volunteers (age 20-68 years) were enrolled. Participants were asked to contract their pelvic floor muscles. This was followed by a urodynamic examination consisting of repeated filling cycles. Brain activity was measured by functional magnetic resonance imaging using a 3T magnetic resonance system. Measurements of brain activity consisted of 120 functional scans during pelvic floor contractions and 210 scans during bladder filling. Each functional magnetic resonance imaging scan covered the brain with 35 slices. Statistical analyses used the general linear model and independent component analysis. Areas of activation were visualized using group statistics. The following main clusters of activation were observed during pelvic floor muscle contractions: medial surface of the frontal lobe (primary motor area), bilaterally; supplementary motor area, bilaterally; and left gyrus precentralis. During bladder filling, activation was detected in the inferior frontal lobe bordering the frontal cingulum, left gyrus parietalis superior, left central area, right insula, brainstem and thalamus with subcortical gray matter nuclei. Our work extends an existing functional magnetic resonance imaging protocol for researching the neural control of the lower urinary tract. The present results are consistent with the available literature and agree with the present hypothetical functional model of lower urinary tract neural control. © 2013 The Japanese Urological Association.

  14. Biological Computation Indexes of Brain Oscillations in Unattended Facial Expression Processing Based on Event-Related Synchronization/Desynchronization

    Directory of Open Access Journals (Sweden)

    Bo Yu

    2016-01-01

    Full Text Available Estimation of human emotions from Electroencephalogram (EEG signals plays a vital role in affective Brain Computer Interface (BCI. The present study investigated the different event-related synchronization (ERS and event-related desynchronization (ERD of typical brain oscillations in processing Facial Expressions under nonattentional condition. The results show that the lower-frequency bands are mainly used to update Facial Expressions and distinguish the deviant stimuli from the standard ones, whereas the higher-frequency bands are relevant to automatically processing different Facial Expressions. Accordingly, we set up the relations between each brain oscillation and processing unattended Facial Expressions by the measures of ERD and ERS. This research first reveals the contributions of each frequency band for comprehension of Facial Expressions in preattentive stage. It also evidences that participants have emotional experience under nonattentional condition. Therefore, the user’s emotional state under nonattentional condition can be recognized in real time by the ERD/ERS computation indexes of different frequency bands of brain oscillations, which can be used in affective BCI to provide the user with more natural and friendly ways.

  15. Synchronous slowing down in coupled logistic maps via random network topology

    Science.gov (United States)

    Wang, Sheng-Jun; Du, Ru-Hai; Jin, Tao; Wu, Xing-Sen; Qu, Shi-Xian

    2016-03-01

    The speed and paths of synchronization play a key role in the function of a system, which has not received enough attention up to now. In this work, we study the synchronization process of coupled logistic maps that reveals the common features of low-dimensional dissipative systems. A slowing down of synchronization process is observed, which is a novel phenomenon. The result shows that there are two typical kinds of transient process before the system reaches complete synchronization, which is demonstrated by both the coupled multiple-period maps and the coupled multiple-band chaotic maps. When the coupling is weak, the evolution of the system is governed mainly by the local dynamic, i.e., the node states are attracted by the stable orbits or chaotic attractors of the single map and evolve toward the synchronized orbit in a less coherent way. When the coupling is strong, the node states evolve in a high coherent way toward the stable orbit on the synchronized manifold, where the collective dynamics dominates the evolution. In a mediate coupling strength, the interplay between the two paths is responsible for the slowing down. The existence of different synchronization paths is also proven by the finite-time Lyapunov exponent and its distribution.

  16. Object segmentation and reconstruction via an oscillatory neural network: interaction among learning, memory, topological organization and gamma-band synchronization.

    Science.gov (United States)

    Magosso, E; Cuppini, C; Ursino, M

    2006-01-01

    Synchronization of neuronal activity in the gamma-band has been shown to play an important role in higher cognitive functions, by grouping together the necessary information in different cortical areas to achieve a coherent perception. In the present work, we used a neural network of Wilson-Cowan oscillators to analyze the problem of binding and segmentation of high-level objects. Binding is achieved by implementing in the network the similarity and prior knowledge Gestalt rules. Similarity law is realized via topological maps within the network. Prior knowledge originates by means of a Hebbian rule of synaptic change; objects are memorized in the network with different strengths. Segmentation is realized via a global inhibitor which allows desynchronisation among multiple objects avoiding interference. Simulation results performed with a 40x40 neural grid, using three simultaneous input objects, show that the network is able to recognize and segment objects in several different conditions (different degrees of incompleteness or distortion of input patterns), exhibiting the higher reconstruction performances the higher the strength of object memory. The presented model represents an integrated approach for investigating the relationships among learning, memory, topological organization and gamma-band synchronization.

  17. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

  18. Brain network activity in monolingual and bilingual older adults.

    Science.gov (United States)

    Grady, Cheryl L; Luk, Gigi; Craik, Fergus I M; Bialystok, Ellen

    2015-01-01

    Bilingual older adults typically have better performance on tasks of executive control (EC) than do their monolingual peers, but differences in brain activity due to language experience are not well understood. Based on studies showing a relation between the dynamic range of brain network activity and performance on EC tasks, we hypothesized that life-long bilingual older adults would show increased functional connectivity relative to monolinguals in networks related to EC. We assessed intrinsic functional connectivity and modulation of activity in task vs. fixation periods in two brain networks that are active when EC is engaged, the frontoparietal control network (FPC) and the salience network (SLN). We also examined the default mode network (DMN), which influences behavior through reduced activity during tasks. We found stronger intrinsic functional connectivity in the FPC and DMN in bilinguals than in monolinguals. Although there were no group differences in the modulation of activity across tasks and fixation, bilinguals showed stronger correlations than monolinguals between intrinsic connectivity in the FPC and task-related increases of activity in prefrontal and parietal regions. This bilingual difference in network connectivity suggests that language experience begun in childhood and continued throughout adulthood influences brain networks in ways that may provide benefits in later life. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Functional brain network modularity predicts response to cognitive training after brain injury.

    Science.gov (United States)

    Arnemann, Katelyn L; Chen, Anthony J-W; Novakovic-Agopian, Tatjana; Gratton, Caterina; Nomura, Emi M; D'Esposito, Mark

    2015-04-14

    We tested the value of measuring modularity, a graph theory metric indexing the relative extent of integration and segregation of distributed functional brain networks, for predicting individual differences in response to cognitive training in patients with brain injury. Patients with acquired brain injury (n = 11) participated in 5 weeks of cognitive training and a comparison condition (brief education) in a crossover intervention study design. We quantified the measure of functional brain network organization, modularity, from functional connectivity networks during a state of tonic attention regulation measured during fMRI scanning before the intervention conditions. We examined the relationship of baseline modularity with pre- to posttraining changes in neuropsychological measures of attention and executive control. The modularity of brain network organization at baseline predicted improvement in attention and executive function after cognitive training, but not after the comparison intervention. Individuals with higher baseline modularity exhibited greater improvements with cognitive training, suggesting that a more modular baseline network state may contribute to greater adaptation in response to cognitive training. Brain network properties such as modularity provide valuable information for understanding mechanisms that influence rehabilitation of cognitive function after brain injury, and may contribute to the discovery of clinically relevant biomarkers that could guide rehabilitation efforts. © 2015 American Academy of Neurology.

  20. Synchronizing an aging brain: can entraining circadian clocks by food slow Alzheimer's disease?

    Science.gov (United States)

    Kent, Brianne A

    2014-01-01

    Alzheimer's disease (AD) is a global epidemic. Unfortunately, we are still without effective treatments or a cure for this disease, which is having devastating consequences for patients, their families, and societies around the world. Until effective treatments are developed, promoting overall health may hold potential for delaying the onset or preventing neurodegenerative diseases such as AD. In particular, chronobiological concepts may provide a useful framework for identifying the earliest signs of age-related disease as well as inexpensive and noninvasive methods for promoting health. It is well reported that AD is associated with disrupted circadian functioning to a greater extent than normal aging. However, it is unclear if the central circadian clock (i.e., the suprachiasmatic nucleus) is dysfunctioning, or whether the synchrony between the central and peripheral clocks that control behavior and metabolic processes are becoming uncoupled. Desynchrony of rhythms can negatively affect health, increasing morbidity and mortality in both animal models and humans. If the uncoupling of rhythms is contributing to AD progression or exacerbating symptoms, then it may be possible to draw from the food-entrainment literature to identify mechanisms for re-synchronizing rhythms to improve overall health and reduce the severity of symptoms. The following review will briefly summarize the circadian system, its potential role in AD, and propose using a feeding-related neuropeptide, such as ghrelin, to synchronize uncoupled rhythms. Synchronizing rhythms may be an inexpensive way to promote healthy aging and delay the onset of neurodegenerative disease such as AD.

  1. Altered resting state brain networks in Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Martin Göttlich

    Full Text Available Parkinson's disease (PD is a neurodegenerative disorder affecting dopaminergic neurons in the substantia nigra leading to dysfunctional cortico-striato-thalamic-cortical loops. In addition to the characteristic motor symptoms, PD patients often show cognitive impairments, affective changes and other non-motor symptoms, suggesting system-wide effects on brain function. Here, we used functional magnetic resonance imaging and graph-theory based analysis methods to investigate altered whole-brain intrinsic functional connectivity in PD patients (n = 37 compared to healthy controls (n = 20. Global network properties indicated less efficient processing in PD. Analysis of brain network modules pointed to increased connectivity within the sensorimotor network, but decreased interaction of the visual network with other brain modules. We found lower connectivity mainly between the cuneus and the ventral caudate, medial orbitofrontal cortex and the temporal lobe. To identify regions of altered connectivity, we mapped the degree of intrinsic functional connectivity both on ROI- and on voxel-level across the brain. Compared to healthy controls, PD patients showed lower connectedness in the medial and middle orbitofrontal cortex. The degree of connectivity was also decreased in the occipital lobe (cuneus and calcarine, but increased in the superior parietal cortex, posterior cingulate gyrus, supramarginal gyrus and supplementary motor area. Our results on global network and module properties indicated that PD manifests as a disconnection syndrome. This was most apparent in the visual network module. The higher connectedness within the sensorimotor module in PD patients may be related to compensation mechanism in order to overcome the functional deficit of the striato-cortical motor loops or to loss of mutual inhibition between brain networks. Abnormal connectivity in the visual network may be related to adaptation and compensation processes as a consequence

  2. Network attributes underlying intellectual giftedness in the developing brain.

    Science.gov (United States)

    Ma, Jiyoung; Kang, Hee Jin; Kim, Jung Yoon; Jeong, Hyeonseok S; Im, Jooyeon Jamie; Namgung, Eun; Kim, Myeong Ju; Lee, Suji; Kim, Tammy D; Oh, Jin Kyoung; Chung, Yong-An; Lyoo, In Kyoon; Lim, Soo Mee; Yoon, Sujung

    2017-09-12

    Brain network is organized to maximize the efficiency of both segregated and integrated information processing that may be related to human intelligence. However, there have been surprisingly few studies that focus on the topological characteristics of brain network underlying extremely high intelligence that is intellectual giftedness, particularly in adolescents. Here, we examined the network topology in 25 adolescents with superior intelligence (SI-Adol), 25 adolescents with average intelligence (AI-Adol), and 27 young adults with AI (AI-Adult). We found that SI-Adol had network topological properties of high global efficiency as well as high clustering with a low wiring cost, relative to AI-Adol. However, contrary to the suggested role that brain hub regions play in general intelligence, the network efficiency of rich club connection matrix, which represents connections among brain hubs, was low in SI-Adol in comparison to AI-Adol. Rather, a higher level of local connection density was observed in SI-Adol than in AI-Adol. The highly intelligent brain may not follow this efficient yet somewhat stereotypical process of information integration entirely. Taken together, our results suggest that a highly intelligent brain may communicate more extensively, while being less dependent on rich club communications during adolescence.

  3. Information dynamics of brain-heart physiological networks during sleep

    Science.gov (United States)

    Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

    2014-10-01

    This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

  4. THE IMPACT OF POVERTY ON THE DEVELOPMENT OF BRAIN NETWORKS

    Directory of Open Access Journals (Sweden)

    Sebastian J Lipina

    2012-08-01

    Full Text Available Although the study of brain development in non-human animals is an old one, recent imaging methods have allowed non-invasive studies of the grey and white matter of the human brain over the lifespan. Classic animal studies show clearly that impoverished environments reduce cortical grey matter in relation to complex environments and cognitive and imaging studies in humans suggest which networks may be most influenced by poverty. Studies have been clear in showing the plasticity of many brain systems, but whether sensitivity to learning differs over the lifespan and for which networks is still unclear. A major task for current research is a successful integration of these methods to understand how development and learning shape the neural networks underlying achievements in literacy, numeracy, and attention. This paper seeks to foster further integration by reviewing the currents state of knowledge relating brain changes to behavior and indicating possible future directions.

  5. Phase synchronization for classification of spontaneous EEG signals in brain-computer interfaces

    OpenAIRE

    Gysels, Elly; Kunt, Murat; Celka, Patrick

    2007-01-01

    By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs different mental tasks, that correspond to different output commands as recognized by the system. From the recorded brain signals (Electroencephalogram, EEG), features that characterize the mental tasks and allow their discrimination by a cla...

  6. Phase synchronization for classification of spontaneous EEG signals in brain-computer interfaces

    OpenAIRE

    Gysels, Elly

    2005-01-01

    By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs different mental tasks, that correspond to different output commands as recognized by the system. From the recorded brain signals (Electroencephalogram, EEG), features that characterize the mental tasks and allow their discrimination by a cla...

  7. Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

    Directory of Open Access Journals (Sweden)

    Douw Linda

    2010-08-01

    Full Text Available Abstract Background Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1, and six months later (t2. At these time points, magnetoencephalography (MEG was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length. Results At the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology. Conclusions These results indicate that (pathologically increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to

  8. An algebraic topological method for multimodal brain networks comparison

    Directory of Open Access Journals (Sweden)

    Tiago eSimas

    2015-07-01

    Full Text Available Understanding brain connectivity is one of the most important issues in neuroscience. Nonetheless, connectivity data can reflect either functional relationships of brain activities or anatomical connections between brain areas. Although both representations should be related, this relationship is not straightforward. We have devised a powerful method that allows different operations between networks that share the same set of nodes, by embedding them in a common metric space, enforcing transitivity to the graph topology. Here, we apply this method to construct an aggregated network from a set of functional graphs, each one from a different subject. Once this aggregated functional network is constructed, we use again our method to compare it with the structural connectivity to identify particular brain regions that differ in both modalities (anatomical and functional. Remarkably, these brain regions include functional areas that form part of the classical resting state networks. We conclude that our method -based on the comparison of the aggregated functional network- reveals some emerging features that could not be observed when the comparison is performed with the classical averaged functional network.

  9. Mapping distributed brain function and networks with diffuse optical tomography

    Science.gov (United States)

    Eggebrecht, Adam T.; Ferradal, Silvina L.; Robichaux-Viehoever, Amy; Hassanpour, Mahlega S.; Dehghani, Hamid; Snyder, Abraham Z.; Hershey, Tamara; Culver, Joseph P.

    2014-06-01

    Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

  10. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2008-06-01

    Full Text Available Functional brain networks detected in task-free ("resting-state" functional magnetic resonance imaging (fMRI have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD. Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01, indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01 in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.

  11. Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays.

    Science.gov (United States)

    Senan, Sibel; Syed Ali, M; Vadivel, R; Arik, Sabri

    2017-02-01

    In this study, we present an approach for the decentralized event-triggered synchronization of Markovian jumping neutral-type neural networks with mixed delays. We present a method for designing decentralized event-triggered synchronization, which only utilizes locally available information, in order to determine the time instants for transmission from sensors to a central controller. By applying a novel Lyapunov-Krasovskii functional, as well as using the reciprocal convex combination method and some inequality techniques such as Jensen's inequality, we obtain several sufficient conditions in terms of a set of linear matrix inequalities (LMIs) under which the delayed neural networks are stochastically stable in terms of the error systems. Finally, we conclude that the drive systems synchronize stochastically with the response systems. We show that the proposed stability criteria can be verified easily using the numerically efficient Matlab LMI toolbox. The effectiveness and feasibility of the results obtained are verified by numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Assortative mixing in functional brain networks during epileptic seizures

    Science.gov (United States)

    Bialonski, Stephan; Lehnertz, Klaus

    2013-09-01

    We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients; and from time-resolved estimates of the assortativity coefficient, we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.

  13. On mind wandering, attention, brain networks, and meditation.

    Science.gov (United States)

    Sood, Amit; Jones, David T

    2013-01-01

    Human attention selectively focuses on aspects of experience that are threatening, pleasant, or novel. The physical threats of the ancient times have largely been replaced by chronic psychological worries and hurts. The mind gets drawn to these worries and hurts, mostly in the domain of the past and future, leading to mind wandering. In the brain, a network of neurons called the default mode network has been associated with mind wandering. Abnormal activity in the default mode network may predispose to depression, anxiety, attention deficit, and posttraumatic stress disorder. Several studies show that meditation can reverse some of these abnormalities, producing salutary functional and structural changes in the brain. This narrative review presents a mechanistic understanding of meditation in the context of recent advances in neurosciences about mind wandering, attention, and the brain networks. Copyright © 2013 Elsevier Inc. All rights reserved.

  14. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten

    Functional and structural magnetic resonance imaging have become the most important noninvasive windows to the human brain. A major challenge in the analysis of brain networks is to establish the similarities and dissimilarities between functional and structural connectivity. We formulate a non......-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...... significant structures that are consistently shared across subjects and data splits. This provides an unsupervised approach for modeling of structure-function relations in the brain and provides a general framework for multimodal integration....

  15. Synchronization in a Random Length Ring Network for SDN-Controlled Optical TDM Switching

    DEFF Research Database (Denmark)

    Kamchevska, Valerija; Cristofori, Valentina; Da Ros, Francesco

    2016-01-01

    In this paper we focus on optical time division multiplexed (TDM) switching and its main distinguishing characteristics compared with other optical subwavelength switching technologies. We review and discuss in detail the synchronization requirements that allow for proper switching operation. In ...

  16. Brain networks modulated by subthalamic nucleus deep brain stimulation.

    Science.gov (United States)

    Accolla, Ettore A; Herrojo Ruiz, Maria; Horn, Andreas; Schneider, Gerd-Helge; Schmitz-Hübsch, Tanja; Draganski, Bogdan; Kühn, Andrea A

    2016-09-01

    Deep brain stimulation of the subthalamic nucleus is an established treatment for the motor symptoms of Parkinson's disease. Given the frequent occurrence of stimulation-induced affective and cognitive adverse effects, a better understanding about the role of the subthalamic nucleus in non-motor functions is needed. The main goal of this study is to characterize anatomical circuits modulated by subthalamic deep brain stimulation, and infer about the inner organization of the nucleus in terms of motor and non-motor areas. Given its small size and anatomical intersubject variability, functional organization of the subthalamic nucleus is difficult to investigate in vivo with current methods. Here, we used local field potential recordings obtained from 10 patients with Parkinson's disease to identify a subthalamic area with an analogous electrophysiological signature, namely a predominant beta oscillatory activity. The spatial accuracy was improved by identifying a single contact per macroelectrode for its vicinity to the electrophysiological source of the beta oscillation. We then conducted whole brain probabilistic tractography seeding from the previously identified contacts, and further described connectivity modifications along the macroelectrode's main axis. The designated subthalamic 'beta' area projected predominantly to motor and premotor cortical regions additional to connections to limbic and associative areas. More ventral subthalamic areas showed predominant connectivity to medial temporal regions including amygdala and hippocampus. We interpret our findings as evidence for the convergence of different functional circuits within subthalamic nucleus' portions deemed to be appropriate as deep brain stimulation target to treat motor symptoms in Parkinson's disease. Potential clinical implications of our study are illustrated by an index case where deep brain stimulation of estimated predominant non-motor subthalamic nucleus induced hypomanic behaviour. © The

  17. Directed progression brain networks in Alzheimer's disease: properties and classification.

    Science.gov (United States)

    Friedman, Eric J; Young, Karl; Asif, Danial; Jutla, Inderjit; Liang, Michael; Wilson, Scott; Landsberg, Adam S; Schuff, Norbert

    2014-06-01

    This article introduces a new approach in brain connectomics aimed at characterizing the temporal spread in the brain of pathologies like Alzheimer's disease (AD). The main instrument is the development of "directed progression networks" (DPNets), wherein one constructs directed edges between nodes based on (weakly) inferred directions of the temporal spreading of the pathology. This stands in contrast to many previously studied brain networks where edges represent correlations, physical connections, or functional progressions. In addition, this is one of a few studies showing the value of using directed networks in the study of AD. This article focuses on the construction of DPNets for AD using longitudinal cortical thickness measurements from magnetic resonance imaging data. The network properties are then characterized, providing new insights into AD progression, as well as novel markers for differentiating normal cognition (NC) and AD at the group level. It also demonstrates the important role of nodal variations for network classification (i.e., the significance of standard deviations, not just mean values of nodal properties). Finally, the DPNets are utilized to classify subjects based on their global network measures using a variety of data-mining methodologies. In contrast to most brain networks, these DPNets do not show high clustering and small-world properties.

  18. Spreading dynamics on spatially constrained complex brain networks.

    Science.gov (United States)

    O'Dea, Reuben; Crofts, Jonathan J; Kaiser, Marcus

    2013-04-06

    The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way.

  19. Network-level structural covariance in the developing brain.

    Science.gov (United States)

    Zielinski, Brandon A; Gennatas, Efstathios D; Zhou, Juan; Seeley, William W

    2010-10-19

    Intrinsic or resting state functional connectivity MRI and structural covariance MRI have begun to reveal the adult human brain's multiple network architectures. How and when these networks emerge during development remains unclear, but understanding ontogeny could shed light on network function and dysfunction. In this study, we applied structural covariance MRI techniques to 300 children in four age categories (early childhood, 5-8 y; late childhood, 8.5-11 y; early adolescence, 12-14 y; late adolescence, 16-18 y) to characterize gray matter structural relationships between cortical nodes that make up large-scale functional networks. Network nodes identified from eight widely replicated functional intrinsic connectivity networks served as seed regions to map whole-brain structural covariance patterns in each age group. In general, structural covariance in the youngest age group was limited to seed and contralateral homologous regions. Networks derived using primary sensory and motor cortex seeds were already well-developed in early childhood but expanded in early adolescence before pruning to a more restricted topology resembling adult intrinsic connectivity network patterns. In contrast, language, social-emotional, and other cognitive networks were relatively undeveloped in younger age groups and showed increasingly distributed topology in older children. The so-called default-mode network provided a notable exception, following a developmental trajectory more similar to the primary sensorimotor systems. Relationships between functional maturation and structural covariance networks topology warrant future exploration.

  20. Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen-Grossberg BAM neural networks with impulses.

    Science.gov (United States)

    Yang, Wengui; Yu, Wenwu; Cao, Jinde; Alsaadi, Fuad E; Hayat, Tasawar

    2018-02-01

    This paper investigates the stability and lag synchronization for memristor-based fuzzy Cohen-Grossberg bidirectional associative memory (BAM) neural networks with mixed delays (asynchronous time delays and continuously distributed delays) and impulses. By applying the inequality analysis technique, homeomorphism theory and some suitable Lyapunov-Krasovskii functionals, some new sufficient conditions for the uniqueness and global exponential stability of equilibrium point are established. Furthermore, we obtain several sufficient criteria concerning globally exponential lag synchronization for the proposed system based on the framework of Filippov solution, differential inclusion theory and control theory. In addition, some examples with numerical simulations are given to illustrate the feasibility and validity of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Guaranteed Cost Control for Exponential Synchronization of Cellular Neural Networks with Mixed Time-Varying Delays via Hybrid Feedback Control

    Directory of Open Access Journals (Sweden)

    T. Botmart

    2013-01-01

    Full Text Available The problem of guaranteed cost control for exponential synchronization of cellular neural networks with interval nondifferentiable and distributed time-varying delays via hybrid feedback control is considered. The interval time-varying delay function is not necessary to be differentiable. Based on the construction of improved Lyapunov-Krasovskii functionals is combined with Leibniz-Newton's formula and the technique of dealing with some integral terms. New delay-dependent sufficient conditions for the exponential synchronization of the error systems with memoryless hybrid feedback control are first established in terms of LMIs without introducing any free-weighting matrices. The optimal guaranteed cost control with linear error hybrid feedback is turned into the solvable problem of a set of LMIs. A numerical example is also given to illustrate the effectiveness of the proposed method.

  2. The Virtual Brain: a simulator of primate brain network dynamics

    Directory of Open Access Journals (Sweden)

    Paula eSanz Leon

    2013-06-01

    Full Text Available We present TheVirtualBrain (TVB, a neuroinformatics platform for full brainnetwork simulations using biologically realistic connectivity. This simulationenvironment enables the model-based inference of neurophysiological mechanismsacross different brain scales that underlie the generation of macroscopicneuroimaging signals including functional MRI (fMRI, EEG and MEG. Researchersfrom different backgrounds can benefit from an integrative software platformincluding a supporting framework for data management (generation,organization, storage, integration and sharing and a simulation core writtenin Python. TVB allows the reproduction and evaluation of personalizedconfigurations of the brain by using individual subject data. Thispersonalization facilitates an exploration of the consequences of pathologicalchanges in the system, permitting to investigate potential ways to counteractsuch unfavorable processes. The architecture of TVB supports interaction withMATLAB packages, for example, the well known Brain Connectivity Toolbox. TVBcan be used in a client-server configuration, such that it can be remotelyaccessed through the Internet thanks to its web-basedHTML5, JS and WebGL graphical user interface. TVB is alsoaccessible as a standalone cross-platform Python library and application, andusers can interact with the scientific core through the scripting interfaceIDLE, enabling easy modeling, development and debugging of the scientifickernel. This second interface makes TVB extensible by combining it with otherlibraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to thedevelopment of TVB, the architecture and features of its major softwarecomponents as well as potential neuroscience applications.

  3. Abnormal whole-brain functional networks in homogeneous acute mild traumatic brain injury.

    NARCIS (Netherlands)

    Shumskaya, E.; Andriessen, T.; Norris, David Gordon; Vos, P.E.

    2012-01-01

    Objectives: To evaluate the whole-brain resting-state networks in a homogeneous group of patients with acute mild traumatic brain injury (MTBI) and to identify alterations in functional connectivity induced by MTBI. Methods: Thirty-five patients with acute MTBI and 35 healthy control subjects,

  4. Brain network dysregulation, emotion, and complaints after mild traumatic brain injury

    NARCIS (Netherlands)

    van der Horn, Harm J.; Liemburg, Edith J.; Scheenen, Myrthe E.; de Koning, Myrthe E.; Marsman, Jan-Bernard C.; Spikman, Jacoba M.; van der Naalt, Joukje

    ObjectivesTo assess the role of brain networks in emotion regulation and post-traumatic complaints in the sub-acute phase after non-complicated mild traumatic brain injury (mTBI). Experimental designFifty-four patients with mTBI (34 with and 20 without complaints) and 20 healthy controls

  5. Beyond localized and distributed accounts of brain functions. Comment on “Understanding brain networks and brain organization” by Pessoa

    Science.gov (United States)

    Cauda, Franco; Costa, Tommaso; Tamietto, Marco

    2014-09-01

    Recent evidence in cognitive neuroscience lends support to the idea that network models of brain architecture provide a privileged access to the understanding of the relation between brain organization and cognitive processes [1]. The core perspective holds that cognitive processes depend on the interactions among distributed neuronal populations and brain structures, and that the impact of a given region on behavior largely depends on its pattern of anatomical and functional connectivity [2,3].

  6. Identifying topological motif patterns of human brain functional networks.

    Science.gov (United States)

    Wei, Yongbin; Liao, Xuhong; Yan, Chaogan; He, Yong; Xia, Mingrui

    2017-05-01

    Recent imaging connectome studies demonstrated that the human functional brain network follows an efficient small-world topology with cohesive functional modules and highly connected hubs. However, the functional motif patterns that represent the underlying information flow remain largely unknown. Here, we investigated motif patterns within directed human functional brain networks, which were derived from resting-state functional magnetic resonance imaging data with controlled confounding hemodynamic latencies. We found several significantly recurring motifs within the network, including the two-node reciprocal motif and five classes of three-node motifs. These recurring motifs were distributed in distinct patterns to support intra- and inter-module functional connectivity, which also promoted integration and segregation in network organization. Moreover, the significant participation of several functional hubs in the recurring motifs exhibited their critical role in global integration. Collectively, our findings highlight the basic architecture governing brain network organization and provide insight into the information flow mechanism underlying intrinsic brain activities. Hum Brain Mapp 38:2734-2750, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  7. Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia.

    Science.gov (United States)

    Huang, Zirui; Zhang, Jun; Wu, Jinsong; Qin, Pengmin; Wu, Xuehai; Wang, Zhiyao; Dai, Rui; Li, Yuan; Liang, Weimin; Mao, Ying; Yang, Zhong; Zhang, Jianfeng; Wolff, Annemarie; Northoff, Georg

    2016-01-01

    Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which "couple" the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. BrainNet Viewer: a network visualization tool for human brain connectomics.

    Science.gov (United States)

    Xia, Mingrui; Wang, Jinhui; He, Yong

    2013-01-01

    The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

  9. BrainNet Viewer: a network visualization tool for human brain connectomics.

    Directory of Open Access Journals (Sweden)

    Mingrui Xia

    Full Text Available The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI, we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/.

  10. Why Do We Fall into Sync with Others? Interpersonal Synchronization and the Brain's Optimization Principle

    DEFF Research Database (Denmark)

    Koban, Leonie; Ramamoorthy, Anand; Konvalinka, Ivana

    2017-01-01

    in interpersonal coupling, the underlying brain mechanisms are not well understood. Here we argue that more general theories of neural computations, namely predictive coding and the Free Energy Principle, could explain interpersonal coordination dynamics. Each brain minimizes coding costs by reducing the mismatch...... between the representations of observed and own motor behavior. Continuous mutual prediction and alignment result in an overall minimization of free energy, thus forming a stable attractor state....

  11. Simple Algorithms for Distributed Leader Election in Anonymous Synchronous Rings and Complete Networks Inspired by Neural Development in Fruit Flies.

    Science.gov (United States)

    Xu, Lei; Jeavons, Peter

    2015-11-01

    Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.

  12. Neural murmurations. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    Science.gov (United States)

    Laurienti, Paul J.

    2014-09-01

    If not the last frontier, understanding the human brain is certainly one of the last. Over the past decade there has been a shift in the focus of neuroscience. The concept of the brain as a network is gaining traction and is rapidly becoming a dominant perspective [1]. In the target article [2], Luiz Pessoa discusses major conceptual shifts that must accompany the methodological changes associated with network science applications to the brain. The software, algorithms, and computational power needed to perform network analyses are now at the fingertips of all neuroscientists. But, this places us at a fork in the road. Will these tools be used to substantiate what has already been discovered, or will we seek a totally new and improved understanding of the brain?

  13. Synchronization of stochastic systems: from paddlefish electroreceptors to human epileptic glial cell cultures

    Science.gov (United States)

    Neiman, Alexander

    2000-03-01

    Synchronization is one of the fundamental nonlinear phenomena observed in nature. We have studied stochastic synchronization in the electrosensitive system of the paddlefish, Polyodon spathula and have also applied synchronization analysis to networks of glial cells cultured from brain tissue of patients with severe epilepsy. We also present theoretical and numerical models for stochastic synchronization. The electrosensitive system of the paddlefish consists of tens of thousands of electroreceptors located mainly on the "rostrum", which serves as an antenna to locate plankton. Each electroreceptor is a noisy oscillator with natural frequencies in the range of 30-90 Hz. We study synchronization in vivo due to 3-20 Hz external periodic electric fields, which correspond to natural signals produced by Daphnia, the usual prey of paddlefish. We find that for signals whose strengths are in the range that paddlefish customarily encounter in the wild, synchronization coding offers a plausible alternative to the more usual rate coding. We also have studied mutual synchronization between different electroreceptors. Although the spontaneous firing of distant electroreceptors is not synchronized, synchronization is observed when external periodic or even noisy electric fields are applied. We have applied the same analysis techniques to examine synchronization between groups of glial cells. In contrast to cultures of healthy astrocytes, which demonstrate calcium waves, the networks from epileptic tissue are characterized by spatially disordered hyper activity. Nevertheless, we have found that, in many cases, synchronized activity is a rather typical for tissue taken from the uncus region of the brain.

  14. Complex network analysis of brain functional connectivity under a multi-step cognitive task

    OpenAIRE

    Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

    2017-01-01

    Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a \\emph{multi-step} cognitive task involving with consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed base on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based ...

  15. Finite-Time Nonfragile Synchronization of Stochastic Complex Dynamical Networks with Semi-Markov Switching Outer Coupling

    Directory of Open Access Journals (Sweden)

    Rathinasamy Sakthivel

    2018-01-01

    Full Text Available The problem of robust nonfragile synchronization is investigated in this paper for a class of complex dynamical networks subject to semi-Markov jumping outer coupling, time-varying coupling delay, randomly occurring gain variation, and stochastic noise over a desired finite-time interval. In particular, the network topology is assumed to follow a semi-Markov process such that it may switch from one to another at different instants. In this paper, the random gain variation is represented by a stochastic variable that is assumed to satisfy the Bernoulli distribution with white sequences. Based on these hypotheses and the Lyapunov-Krasovskii stability theory, a new finite-time stochastic synchronization criterion is established for the considered network in terms of linear matrix inequalities. Moreover, the control design parameters that guarantee the required criterion are computed by solving a set of linear matrix inequality constraints. An illustrative example is finally given to show the effectiveness and advantages of the developed analytical results.

  16. Sleeping of a Complex Brain Networks with Hierarchical Organization

    Science.gov (United States)

    Zhang, Ying-Yue; Yang, Qiu-Ying; Chen, Tian-Lun

    2009-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

  17. Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks.

    Science.gov (United States)

    Hellyer, Peter J; Jachs, Barbara; Clopath, Claudia; Leech, Robert

    2016-01-01

    Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important for efficient neural functioning. A range of experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to maintain rich dynamics across a broad range of situations. Several mechanisms based around homeostatic plasticity have been proposed to explain how these dynamics emerge from networks of neurons at the microscopic scale. Here we explore how a homeostatic mechanism may operate at the macroscopic scale: in particular, focusing on how it interacts with the underlying structural network topology and how it gives rise to well-described functional connectivity networks. We use a simple mean-field model of the brain, constrained by empirical white matter structural connectivity where each region of the brain is simulated using a pool of excitatory and inhibitory neurons. We show, as with the microscopic work, that homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations, which otherwise show a very limited range of dynamic regimes. In addition, the simulated functional connectivity of the homeostatic model better resembles empirical functional connectivity network. To accomplish this, we show how the inhibitory weights adapt over time to capture important graph theoretic properties of the underlying structural network. Therefore, this work presents suggests how inhibitory homeostatic mechanisms facilitate stable macroscopic dynamics to emerge in the brain, aiding the formation of functional connectivity networks. Copyright © 2015 Elsevier Inc. All rights

  18. SYNCHRONIZATION IN NETWORKS OF COUPLED HARMONIC OSCILLATORS WITH STOCHASTIC PERTURBATION AND TIME DELAYS

    Directory of Open Access Journals (Sweden)

    Yilun Shang

    2012-07-01

    Full Text Available In this paper, we investigate the leader-follower synchronization ofcoupled second-order linear harmonic oscillators with the presence ofrandom noises and time delays. The interaction topology is modeledby a weighted directed graph and the weights are perturbed by whitenoise. On the basis of stability theory of stochastic differential delayequations, algebraic graph theory and matrix theory, we show that thecoupled harmonic oscillators can be synchronized almost surely withrandom perturbation and time delays. Numerical examples are presentedto illustrate our theoretical results.

  19. Application of «Sensor signal analysis network» complex for distributed, time synchronized analysis of electromagnetic radiation

    Science.gov (United States)

    Mochalov, Vladimir; Mochalova, Anastasia

    2017-10-01

    The paper considers a developing software-hardware complex «Sensor signal analysis network» for distributed and time synchronized analysis of electromagnetic radiations. The areas of application and the main features of the complex are described. An example of application of the complex to monitor natural electromagnetic radiation sources is considered based on the data recorded in VLF range. A generalized functional scheme of stream analysis of signals by a complex functional node is suggested and its application for stream detection of atmospherics, whistlers and tweaks is considered.

  20. Application of «Sensor signal analysis network» complex for distributed, time synchronized analysis of electromagnetic radiation

    Directory of Open Access Journals (Sweden)

    Mochalov Vladimir

    2017-01-01

    Full Text Available The paper considers a developing software-hardware complex «Sensor signal analysis network» for distributed and time synchronized analysis of electromagnetic radiations. The areas of application and the main features of the complex are described. An example of application of the complex to monitor natural electromagnetic radiation sources is considered based on the data recorded in VLF range. A generalized functional scheme of stream analysis of signals by a complex functional node is suggested and its application for stream detection of atmospherics, whistlers and tweaks is considered.