Differential synchronization in default and task-specific networks of the human brain
Directory of Open Access Journals (Sweden)
Aaron Kirschner
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.
Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG
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).
Synchronization in active networks
Pereira da Silva, Tiago
2007-01-01
In nature one commonly finds interacting complex oscillators which by the coupling scheme form small and large networks, e.g. neural networks. Surprisingly, the oscillators can synchronize, still preserving the complex behavior. Synchronization is a fundamental phenomenon in coupled nonlinear oscillators. Synchronization can be enhanced at different levels, that is, the constraints on which the synchronization appears. Those can be in the trajectory amplitude, requiring the amplitudes of both...
Indian Academy of Sciences (India)
R E Amritkar
2008-08-01
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 multi-cluster synchronization. For networks with time-varying topology we compare the synchronization properties of these networks with the corresponding time-average network. We find that if the different coupling matrices corresponding to the time-varying networks commute with each other then the stability of the synchronized state for both the time-varying and the time-average topologies are approximately the same. On the other hand, for non-commuting coupling matrices the stability of the synchronized state for the time-varying topology is in general better than the time-average topology.
Fast sparsely synchronized brain rhythms in a scale-free neural network.
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>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For Dtopology (e.g., average path length and betweenness centralization) and sparse synchronization and contributions of individual dynamics to the sparse synchronization are discussed. PMID:26382442
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.
Synchronization in complex networks
Arenas, Alex; Díaz-Guilera, Albert; Kurths, Jurgen; Moreno, Yamir; Zhou, Changsong
2008-12-01
Synchronization processes in populations of locally interacting elements are the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understanding synchronization phenomena in natural systems now take 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 take an overview of the new emergent features coming out from the interplay between the structure and the function of the underlying patterns 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.
Development of Network Synchronization Predicts Language Abilities.
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. PMID:26401810
Remote Synchronization in Complex Networks
Gambuzza, Lucia Valentina; Fiasconaro, Alessandro; Fortuna, Luigi; Gómez-Gardeñes, Jesús; Frasca, Mattia
2013-01-01
We show the existence of a novel dynamical state called remote synchronization in general networks of coupled oscillators. This state is characterized by the synchronization of pairs of nodes that are not directly connected via a physical link or any sequence of synchronized nodes. This phenomenon cannot be observed in networks of phase oscillators as its underlying mechanism is the modulation of the amplitude of those intermediary nodes between the remotely synchronized units. Our findings thus show the ubiquity and robustness of these states and bridge the gap from their recent observation in simple toy graphs to complex networks.
Simulating synchronization in neuronal networks
Fink, Christian G.
2016-06-01
We discuss several techniques used in simulating neuronal networks by exploring how a network's connectivity structure affects its propensity for synchronous spiking. Network connectivity is generated using the Watts-Strogatz small-world algorithm, and two key measures of network structure are described. These measures quantify structural characteristics that influence collective neuronal spiking, which is simulated using the leaky integrate-and-fire model. Simulations show that adding a small number of random connections to an otherwise lattice-like connectivity structure leads to a dramatic increase in neuronal synchronization.
Pinning Synchronization of Switched Complex Dynamical Networks
Liming Du; Feng Qiao; Fengying Wang
2015-01-01
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 synch...
Remote synchronization in star networks
Bergner, A.; Frasca, M.; Sciuto, G.; Buscarino, A.; Ngamga, E. J.; Fortuna, L.; Kurths, J.
2012-02-01
We study phase synchronization in a network motif with a starlike structure in which the central node's (the hub's) frequency is strongly detuned against the other peripheral nodes. We find numerically and experimentally a regime of remote synchronization (RS), where the peripheral nodes form a phase synchronized cluster, while the hub remains free with its own dynamics and serves just as a transmitter for the other nodes. We explain the mechanism for this RS by the existence of a free amplitude and also show that systems with a fixed or constant amplitude, such as the classic Kuramoto phase oscillator, are not able to generate this phenomenon. Further, we derive an analytic expression which supports our explanation of the mechanism.
Breathing synchronization in interconnected networks
Louzada, V H P; Andrade, J S; Herrmann, H J
2013-01-01
The harmony of an orchestra emerges from the individual effort of musicians towards mutual synchronization of their tempi. When the orchestra is split between two concert halls communicating via Internet, a time delay is imposed which might hinder synchronization. We describe this type of system as two interconnected networks of oscillators with a time delay and analyze its dynamics as a function of the couplings and communication lag. We discover a breathing synchronization regime, namely, for a wide range of parameters, two groups emerge in the orchestra within the same concert hall playing at different tempi. Each group has a mirror in the other hall, one group is in phase and the other in anti-phase with their mirrors. For strong couplings, a phase shift between halls might occur. The implications of our findings on several socio-technical and biological systems are discussed.
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.
Synchronization in oscillatory networks
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.
Neural network plasticity in the human brain
Rizk, Sviatlana
2013-01-01
The human brain is highly organized within networks. Functionally related neural-assemblies communicate by oscillating synchronously. Intrinsic brain activity contains information on healthy and damaged brain functioning. This thesis investigated the relationship between functional networks and behavior. Furthermore, we assessed functional network plasticity after brain damage and as a result of brain stimulation. In different groups of patients we observed reduced functional connectivity bet...
Spontaneous synchronization driven by energy transport in interconnected networks
Nicosia, Vincenzo; Latora, Vito; Arenas, Alex
2014-01-01
The existence of intertwined dynamical processes in interconnected networks is a distinctive aspect of complex systems. For example, cerebral circulation delivers oxygenated blood to the brain through blood vessels, and in turn the neural system in the brain is responsible, through neural network connections, for mediating changes in cerebral blood flow. To understand the effects of the coupling between different processes we study the case of two interconnected networks, one accounting for energy transport and the other for a synchronization dynamics. We observe the emergence of switch-like spontaneous synchronization, similar to that seen in the transition between resting-state and cognitive activity in the human brain, for a wide range of control parameters and for different network topologies. We suggest that similarly interconnected dynamical processes could be responsible for spontaneous synchronization transitions observed in natural systems.
Disturbed temporal dynamics of brain synchronization in vision loss.
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. PMID:25956453
Inhomogeneity induces relay synchronization in complex networks
Gambuzza, Lucia Valentina; Frasca, Mattia; Fortuna, Luigi; Boccaletti, Stefano
2016-04-01
Relay synchronization is a collective state, originally found in chains of interacting oscillators, in which uncoupled dynamical units synchronize through the action of mismatched inner nodes that relay the information but do not synchronize with them. It is demonstrated herein that relay synchronization is not limited to such simple motifs, rather it can emerge in larger and arbitrary network topologies. In particular, we show how this phenomenon can be observed in networks of chaotic systems in the presence of some mismatched units, the relay nodes, and how it is actually responsible for an enhancement of synchronization in the network.
[Synchronized, oscillatory brain activity in visual perception].
Braunitzer, Gábor
2008-09-30
The present study investigates one of the most promising developments of the brain-mind question, namely the possible links between synchronized oscillatory brain activity and certain (visual) perceptual processes. Through a review of the relevant literature, the author introduces the reader to the most important theories of coherent perception ('binding'), and makes an attempt to show how synchronization of EEG-registrable oscillatory activities from various frequency bands might explain binding. Finally, a number of clinical problems are also mentioned, regarding which the presented theoretical framework might deserve further consideration. PMID:18841649
Synchronization of oscillators in complex networks
Indian Academy of Sciences (India)
Louis M Pecora
2008-06-01
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 and compare three types of semirandom networks for their efficacy in synchronizing oscillators. It is shown that the simplest -cycle augmented by a few random edges or links are the most efficient network that will guarantee good synchronization.
Global Synchronization of General Delayed Dynamical Networks
Institute of Scientific and Technical Information of China (English)
LI Zhi
2007-01-01
Global synchronization of general delayed dynamical networks with linear coupling are investigated. A sufficient condition for the global synchronization is obtained by using the linear matrix inequality and introducing a reference state. This condition is simply given based on the maximum nonzero eigenvalue of the network coupling matrix. Moreover, we show how to construct the coupling matrix to guarantee global synchronization of network,which is very convenient to use. A two-dimension system with delay as a dynamical node in network with global coupling is finally presented to verify the theoretical results of the proposed global synchronization scheme.
Synchronization performance of complex oscillator networks
Yan, Gang; Chen, Guanrong; Lü, Jinhu; Fu, Zhong-Qian
2009-11-01
Recently, synchronization of complex networks has attracted increasing attention from various research fields. However, most previous works focused on the stability of synchronization manifold. In this paper, we analyze the time-delay tolerance and converging speed of synchronization. Our theoretical analysis and extensive simulations show that the critical value of time delay for network synchronization is inversely proportional to the largest Laplacian eigenvalue, the converging speed without time delay is proportional to the second least Laplacian eigenvalue, and the time delay could increase the converging speed linearly for heterogeneous networks and significantly for homogeneous networks.
Synchronization in complex networks with switching topology
International Nuclear Information System (INIS)
This Letter investigates synchronization issues of complex dynamical networks with switching topology. By constructing a common Lyapunov function, we show that local and global synchronization for a linearly coupled network with switching topology can be evaluated by the time average of second smallest eigenvalues corresponding to the Laplacians of switching topology. This result is quite powerful and can be further used to explore various switching cases for complex dynamical networks. Numerical simulations illustrate the effectiveness of the obtained results in the end. -- Highlights: → Synchronization of complex networks with switching topology is investigated. → A common Lyapunov function is established for synchronization of switching network. → The common Lyapunov function is not necessary to monotonically decrease with time. → Synchronization is determined by the second smallest eigenvalue of its Laplacian. → Synchronization criterion can be used to investigate various switching cases.
Wireless Networks Effective Time Power Synchronization
Directory of Open Access Journals (Sweden)
Kamalakannan
2012-06-01
Full Text Available Time synchronization is a critical piece of infrastructure for any distributed system. Distributed, wireless sensor net-works make extensive use of synchronized time, but often have unique requirements in the scope, lifetime and precision of the synchronization achieved, as well as the time and energy required to achieve it. Existing time synchronization methods need to be extended to meet these new needs. We outline the synchronization requirements of future sensor networks and present an implementation of our own low-power synchronization scheme, post-facto synchronization. We also describe an experiment that characterizes its performance for creating short-lived and localized but high-precision synchronization using very little energy.
Analysis of remote synchronization in complex networks
Gambuzza, Lucia Valentina; Cardillo, Alessio; Fiasconaro, Alessandro; Fortuna, Luigi; Gómez-Gardeñes, Jesus; Frasca, Mattia
2013-12-01
A novel regime of synchronization, called remote synchronization, where the peripheral nodes form a phase synchronized cluster not including the hub, was recently observed in star motifs [Bergner et al., Phys. Rev. E 85, 026208 (2012)]. We show the existence of a more general dynamical state of remote synchronization in arbitrary networks of coupled oscillators. This state is characterized by the synchronization of pairs of nodes that are not directly connected via a physical link or any sequence of synchronized nodes. This phenomenon is almost negligible in networks of phase oscillators as its underlying mechanism is the modulation of the amplitude of those intermediary nodes between the remotely synchronized units. Our findings thus show the ubiquity and robustness of these states and bridge the gap from their recent observation in simple toy graphs to complex networks.
Controlling synchronous patterns in complex networks
Lin, Weijie; Fan, Huawei; Wang, Ying; Ying, Heping; Wang, Xingang
2016-04-01
Although the set of permutation symmetries of a complex network could be very large, few of them give rise to stable synchronous patterns. Here we present a general framework and develop techniques for controlling synchronization patterns in complex network of coupled chaotic oscillators. Specifically, according to the network permutation symmetry, we design a small-size and weighted network, namely the control network, and use it to control the large-size complex network by means of pinning coupling. We argue mathematically that for any of the network symmetries, there always exists a critical pinning strength beyond which the unstable synchronous pattern associated to this symmetry can be stabilized. The feasibility of the control method is verified by numerical simulations of both artificial and real-world networks and demonstrated experimentally in systems of coupled chaotic circuits. Our studies show the controllability of synchronous patterns in complex networks of coupled chaotic oscillators.
Adaptive cluster synchronization in complex dynamical networks
International Nuclear Information System (INIS)
Cluster synchronization is investigated in different complex dynamical networks. In this Letter, a novel adaptive strategy is proposed to make a complex dynamical network achieve cluster synchronization, where the adaptive strategy of one edge is adjusted only according to its local information. A sufficient condition about the global stability arbitrarily grouped of cluster synchronization is derived. Several numerical simulations show the effectiveness of the adaptive strategy.
Detecting synchronization in coupled stochastic ecosystem networks
Energy Technology Data Exchange (ETDEWEB)
Kouvaris, N. [Institute of Physical Chemistry, National Center for Scientific Research ' Demokritos' , 15310 Athens (Greece); Department of Mathematical, Physical and Computational Science, Faculty of Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki (Greece); Provata, A. [Institute of Physical Chemistry, National Center for Scientific Research ' Demokritos' , 15310 Athens (Greece); Kugiumtzis, D., E-mail: dkugiu@gen.auth.g [Department of Mathematical, Physical and Computational Science, Faculty of Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki (Greece)
2010-01-11
Instantaneous phase difference, synchronization index and mutual information are considered in order to detect phase transitions, collective behaviours and synchronization phenomena that emerge for different levels of diffusive and reactive activity in stochastic networks. The network under investigation is a spatial 2D lattice which serves as a substrate for Lotka-Volterra dynamics with 3rd order nonlinearities. Kinetic Monte Carlo simulations demonstrate that the system spontaneously organizes into a number of asynchronous local oscillators, when only nearest neighbour interactions are considered. In contrast, the oscillators can be correlated, phase synchronized and completely synchronized when introducing different interactivity rules (diffusive or reactive) for nearby and distant species. The quantitative measures of synchronization show that long distance diffusion coupling induces phase synchronization after a well defined transition point, while long distance reaction coupling induces smeared phase synchronization.
Synchronization of impulsively coupled complex networks
International Nuclear Information System (INIS)
We investigate the synchronization of complex networks, which are impulsively coupled only at discrete instants. Based on the comparison theory of impulsive differential systems, a distributed impulsive control scheme is proposed for complex dynamical networks to achieve synchronization. The proposed scheme not only takes into account the influence of all nodes to network synchronization, which depends on the weight of each node in the network, but also provides us with a flexible method to select the synchronized state of the network. In addition, it is unnecessary for the impulsive coupling matrix to be symmetrical. Finally, the proposed control scheme is applied to a chaotic Lorenz network and Chua's circuit network. Numerical simulations are used to illustrate the validity of this control scheme. (general)
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.
International Nuclear Information System (INIS)
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
Fitness for synchronization of network motifs
DEFF Research Database (Denmark)
Vega, Y.M.; Vázquez-Prada, M.; Pacheco, A.F.; Vazquez-Prada Baillet, Miguel
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. © 2004 Elsevier B.V. All rights reserved....
Brain activities during synchronized tapping task.
Hiroyasu, Tomoyuki; Murakami, Akiho; Mao Gto; Yokouchi, Hisatake
2015-08-01
This study aims to investigate how people process information about other people to determine a response during human-to-human cooperative work. As a preliminary study, the mechanism of cooperative work was examined using interaction between a machine and a human. This machine was designed to have an "other person" model that simulates an emotional model of another person. The task performed in the experiment was a synchronized tapping task. Two models were prepared for this experiment, a simple model that does not employ the other person model and a synchronized model that employs the other person model. Subjects performed cooperative work with these machines. During the experiment, brain activities were measured using functional near-infrared spectroscopy. It was observed that the left inferior frontal gyrus was activated more with the synchronized model than the simple model. PMID:26737670
Chaos synchronization in networks of semiconductor superlattices
Li, Wen; Aviad, Yaara; Reidler, Igor; Song, Helun; Huang, Yuyang; Biermann, Klaus; Rosenbluh, Michael; Zhang, Yaohui; Grahn, Holger T.; Kanter, Ido
2015-11-01
Chaos synchronization has been demonstrated as a useful building block for various tasks in secure communications, including a source of all-electronic ultrafast physical random number generators based on room temperature spontaneous chaotic oscillations in a DC-biased weakly coupled GaAs/Al0.45Ga0.55As semiconductor superlattice (SSL). Here, we experimentally demonstrate the emergence of several types of chaos synchronization, e.g. leader-laggard, face-to-face and zero-lag synchronization in network motifs of coupled SSLs consisting of unidirectional and mutual coupling as well as self-feedback coupling. Each type of synchronization clearly reflects the symmetry of the topology of its network motif. The emergence of a chaotic SSL without external feedback and synchronization among different structured SSLs open up the possibility for advanced secure multi-user communication methods based on large networks of coupled SSLs.
Collapse of Synchronization in a Memristive Network
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
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...
Synchronization in heterogeneous FitzHugh-Nagumo networks with hierarchical architecture.
Plotnikov, S A; Lehnert, J; Fradkov, A L; Schöll, E
2016-07-01
We study synchronization in heterogeneous FitzHugh-Nagumo networks. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. Here we develop a controller to counteract the impact of these heterogeneities. We first analyze the stability of the equilibrium point in a ring network of heterogeneous nodes. We then derive a sufficient condition for synchronization in the absence of control. Based on these results we derive the controller providing synchronization for parameter values where synchronization without control is absent. We demonstrate our results in networks with different topologies. Particular attention is given to hierarchical (fractal) topologies, which are relevant for the architecture of the brain. PMID:27575119
Driven synchronization in random networks of oscillators
Hindes, Jason
2015-01-01
Synchronization is a universal phenomenon found in many non-equilibrium systems. Much recent interest in this area has overlapped with the study of complex networks, where a major focus is determining how a system's connectivity patterns affect the types of behavior that it can produce. Thus far, modeling efforts have focused on the tendency of networks of oscillators to mutually synchronize themselves, with less emphasis on the effects of external driving. In this work we discuss the interplay between mutual and driven synchronization in networks of phase oscillators of the Kuramoto type, and resolve how the structure and emergence of such states depends on the underlying network topology for simple random networks with a given degree distribution. We provide a partial bifurcation analysis, centering on the appearance of a Takens-Bogdanov-Cusp singularity, which broadly separates homogeneous and heterogeneous network behavior in a weak coupling limit, and from which the number, stability and appearance of dr...
Impulsive synchronization of networked nonlinear dynamical systems
Energy Technology Data Exchange (ETDEWEB)
Jiang Haibo, E-mail: yctcjhb@gmail.co [Faculty of Science, Jiangsu University, Zhenjiang 212013 (China); School of Mathematics, Yancheng Teachers University, Yancheng 224051 (China); Bi Qinsheng [Faculty of Science, Jiangsu University, Zhenjiang 212013 (China)
2010-06-14
In this Letter, we investigate the problem of impulsive synchronization of networked multi-agent systems, where each agent can be modeled as an identical nonlinear dynamical system. Firstly, an impulsive control protocol is designed for network with fixed topology based on the local information of agents. Then sufficient conditions are given to guarantee the synchronization of the networked nonlinear dynamical system by using algebraic graph theory and impulsive control theory. Furthermore, how to select the discrete instants and impulsive constants is discussed. The case that the topologies of the networks are switching is also considered. Numerical simulations show the effectiveness of our theoretical results.
Impulsive synchronization of networked nonlinear dynamical systems
Jiang, Haibo; Bi, Qinsheng
2010-06-01
In this Letter, we investigate the problem of impulsive synchronization of networked multi-agent systems, where each agent can be modeled as an identical nonlinear dynamical system. Firstly, an impulsive control protocol is designed for network with fixed topology based on the local information of agents. Then sufficient conditions are given to guarantee the synchronization of the networked nonlinear dynamical system by using algebraic graph theory and impulsive control theory. Furthermore, how to select the discrete instants and impulsive constants is discussed. The case that the topologies of the networks are switching is also considered. Numerical simulations show the effectiveness of our theoretical results.
Carrying Synchronous Voice Data On Asynchronous Networks
Bergman, Larry A.
1990-01-01
Buffers restore synchronism for internal use and permit asynchronism in external transmission. Proposed asynchronous local-area digital communication network (LAN) carries synchronous voice, data, or video signals, or non-real-time asynchronous data signals. Network uses double buffering scheme that reestablishes phase and frequency references at each node in network. Concept demonstrated in token-ring network operating at 80 Mb/s, pending development of equipment operating at planned data rate of 200 Mb/s. Technique generic and used with any LAN as long as protocol offers deterministic (or bonded) access delays and sufficient capacity.
The synchronization of FitzHugh-Nagumo neuron network coupled by gap junction
Institute of Scientific and Technical Information of China (English)
Zhan Yong; Zhang Su-Hua; Zhao Tong-Jun; An Hai-Long; Zhang Zhen-Dong; Han Ying-Rong; Liu Hui; Zhang Yu-Hong
2008-01-01
It is well known that the strong coupling can synchronize a network of nonlinear oscillators. Synchronization provides the basis of the remarkable computational performance of the brain. In this paper the FitzHugh-Nagumo neuron network is constructed. The dependence of the synchronization on the coupling strength, the noise intensity and the size of the neuron network has been discussed. The results indicate that the coupling among neurons works to improve the synchronization, and noise increases the neuron random dynamics and the local fluctuations; the larger the size of network, the worse the synchronization. The dependence of the synchronization on the strength of the electric synapse coupling and chemical synapse coupling has also been discussed, which proves that electric synapse coupling can enhance the synchronization of the neuron network largely.
Forced synchronization of autonomous dynamical Boolean networks
International Nuclear Information System (INIS)
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
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.
Stability of Synchronized Motion in Complex Networks
Pereira, Tiago
2011-01-01
We give a succinct and self-contained description of the synchronized motion on networks of mutually coupled oscillators. Usually, the stability criterion for the stability of synchronized motion is obtained in terms of Lyapunov exponents. We consider the fully diffusive case which is amenable to treatment in terms of uniform contractions. This approach provides a rigorous, yet clear and concise, way to the important results.
Synchronization of coupled chaotic dynamics on networks
Indian Academy of Sciences (India)
R E Amritkar; Sarika Jalan
2005-03-01
We review some recent work on the synchronization of coupled dynamical systems on a variety of networks. When nodes show synchronized behaviour, two interesting phenomena can be observed. First, there are some nodes of the floating type that show intermittent behaviour between getting attached to some clusters and evolving independently. Secondly, two different ways of cluster formation can be identified, namely self-organized clusters which have mostly intra-cluster couplings and driven clusters which have mostly inter-cluster couplings.
Synchronization in interacting Scale Free Networks
Torres, M F; La Rocca, C E; Braunstein, L A
2015-01-01
We study the fluctuations of the interface, in the steady state, of the Surface Relaxation Model (SRM) in two Scale Free interacting Networks where a fraction $q$ of nodes in both networks interact one to one through external connections. We find that as $q$ increases the fluctuations on both networks decrease and thus the synchronization is better than in isolated networks. As a consequence the system is optimal synchronized for $q=1$. The decrease of the fluctuations in both networks is due mainly to the diffusion through external connections which allows to reducing the load in nodes by sending the excess of load mostly to low degree nodes. This effect enhances the matching of the heights of low and high degree nodes as $q$ increases reducing the fluctuations.
Clock Synchronization for Multihop Wireless Sensor Networks
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…
Distributed Probabilistic Synchronization Algorithms for Communication Networks
AKAR, MEHMET; Shorten, Robert
2008-01-01
In this paper, we present a probabilistic synchronization algorithm whose convergence properties are examined using tools of rowstochastic matrices. The proposed algorithm is particularly well suited for wireless sensor network applications, where connectivity is not guaranteed at all times, and energy efficiency is an important design consideration. The tradeoff between the convergence speed and the energy use is studied.
Impulsive generalized function synchronization of complex dynamical networks
International Nuclear Information System (INIS)
This Letter investigates generalized function synchronization of continuous and discrete complex networks by impulsive control. By constructing the reasonable corresponding impulsively controlled response networks, some criteria and corollaries are derived for the generalized function synchronization between the impulsively controlled complex networks, continuous and discrete networks are both included. Furthermore, the generalized linear synchronization and nonlinear synchronization are respectively illustrated by several examples. All the numerical simulations demonstrate the correctness of the theoretical results
Anti-synchronization Between Coupled Networks with Two Active Forms
International Nuclear Information System (INIS)
This paper studies anti-synchronization and its control between two coupled networks with nonlinear signal's connection and the inter-network actions. If anti-synchronization does not exist between two such networks, adaptive controllers are designed to anti-synchronize them. Different node dynamics and nonidentical topological structures are considered and useful criteria for anti-synchronization between two networks are given. Numerical examples are presented to show the efficiency of our derived results. (general)
Computer network time synchronization the network time protocol
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
Frustrated hierarchical synchronization and emergent complexity in the human connectome network
Villegas, Pablo; Moretti, Paolo; Mu??oz Mart??nez, Miguel ??ngel
2014-01-01
The spontaneous emergence of coherent behavior through synchronization plays a key role in neural function, and its anomalies often lie at the basis of pathologies. Here we employ a parsimonious (mesoscopic) approach to study analytically and computationally the synchronization (Kuramoto) dynamics on the actual human-brain connectome network. We elucidate the existence of a so-far-uncovered intermediate phase, placed between the standard synchronous and asynchronous phases, i.e. between order...
The Synchronous Network of distant Telescopes
Zhilyaev, B; Verlyuk, I; Andreev, M; Sergeev, A; Lovkaya, M; Antov, S; Konstantinova-Antova, R; Bogdanovski, R; Avgoloupis, S; Seiradakis, J; Contadakis, M
2011-01-01
The Synchronous Network of distant Telescopes (SNT) represents an innovative approach in observational astrophysics. Authors present the unique existing realization of the SNT-conception. It was founded within the international collaboration between astronomical observatories of Ukraine, Russia, Bulgaria and Greece. All the telescopes of the Network are equipped with standardized photometric systems (based on photomultipliers). The unified timing systems (based on GPS-receivers) synchronize all the apertures to UTC with an accuracy of 1 microsecond and better. The essential parts of the SNT are the original software for operating and data processing. Described international Network successfully works for more then 10 years. The obtained unique observational data made it possible to discover new fine-scale features and flare-triggered phenomena in flaring red dwarfs, as well as the recently found high-frequency variability in some chromospherically active stars.
Emergent synchronous bursting of oxytocin neuronal network.
Directory of Open Access Journals (Sweden)
Enrico Rossoni
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.
Adaptive-impulsive synchronization of uncertain complex dynamical networks
International Nuclear Information System (INIS)
This Letter studies adaptive-impulsive synchronization of uncertain complex dynamical networks. Based on the stability analysis of impulsive system, several network synchronization criteria for local and global adaptive-impulsive synchronization are established. Numerical example is also given to illustrate the results
Generalized Synchronization of Lorenz Chaotic System with Star Network
Mohammad Ali Khan
2012-01-01
In this paper, we propose the theory for generalized synchronization (GS) of a chaotic star network. We derive sufficient conditions for generalized synchronization of any chaotic system on a star network. The relationship among the state variables at GS are completely known in our method. The effectiveness and feasibility of the synchronization strategy is confirmed and demonstrated by numerical simulation.
Intermittent synchronization in a network of bursting neurons
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.
Synchronization in random networks with given expected degree sequences
Biey, Mario
2008-01-01
Synchronization in random networks with given expected degree sequences is studied. We also investigate in details the synchronization in networks whose topology is described by classical random graphs, power-law random graphs and hybrid graphs when N goes to infinity. In particular, we show that random graphs almost surely synchronize. We also show that adding small number of global edges to a local graph makes the corresponding hybrid graph to synchronize
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...
Brains swinging in concert: cortical phase synchronization while playing guitar
Directory of Open Access Journals (Sweden)
Gruber Walter
2009-03-01
Full Text Available Abstract Background Brains interact with the world through actions that are implemented by sensory and motor processes. A substantial part of these interactions consists in synchronized goal-directed actions involving two or more individuals. Hyperscanning techniques for assessing fMRI simultaneously from two individuals have been developed. However, EEG recordings that permit the assessment of synchronized neuronal activities at much higher levels of temporal resolution have not yet been simultaneously assessed in multiple individuals and analyzed in the time-frequency domain. In this study, we simultaneously recorded EEG from the brains of each of eight pairs of guitarists playing a short melody together to explore the extent and the functional significance of synchronized cortical activity in the course of interpersonally coordinated actions. Results By applying synchronization algorithms to intra- and interbrain analyses, we found that phase synchronization both within and between brains increased significantly during the periods of (i preparatory metronome tempo setting and (ii coordinated play onset. Phase alignment extracted from within-brain dynamics was related to behavioral play onset asynchrony between guitarists. Conclusion Our findings show that interpersonally coordinated actions are preceded and accompanied by between-brain oscillatory couplings. Presumably, these couplings reflect similarities in the temporal properties of the individuals' percepts and actions. Whether between-brain oscillatory couplings play a causal role in initiating and maintaining interpersonal action coordination needs to be clarified by further research.
Phase Synchronization in Small World Chaotic Neural Networks
Institute of Scientific and Technical Information of China (English)
WANG Qing-Yun; LU Qi-Shao
2005-01-01
@@ To understand collective motion of realneural networks very well, we investigate collective phase synchronization of small world chaotic Hindmarsh-Rose (HR) neural networks. By numerical simulations, we conclude that small world chaotic HR neural networks can achieve collective phase synchronization. Furthermore, it is shown that phase synchronization of small world chaotic HR neural networks is dependent on the coupling strength,the connection topology (which is determined by the probability p), as well as the coupling number. These phenomena are important to guide us to understand the synchronization of real neural networks.
Ideal synchronizer for marked pairs in fork-join network
Vyshenski, S V; Dubenskaya, Yu Yu
2008-01-01
We introduce a new functional element (synchronizer for marked pairs) meant to join results of parallel processing in two-branch fork-join queueing network. Approximations for distribution of sojourn time at the synchronizer are derived along with a validity domain. Calculations are performed assuming that: arrivals to the network form a Poisson process, each branch operates like an M/M/N queueing system. It is shown that a mean quantity of jobs in the synchronizer is bounded below by the value, defined by parameters of the network (which contains the synchronizer) and does not depend upon performance and particular properties of the synchronizer.
Global exponential synchronization criterion for switched linear coupled dynamic networks
International Nuclear Information System (INIS)
We in this paper develop a global exponential synchronization stability criterion for switched linear coupled network. By introducing a switching symmetric matrix, we prove that the stability of global exponential synchronization is governed by the largest eigenvalue of this switching symmetric matrix and the largest switching coupling strength. Meanwhile, we give the threshold of switching coupling strength which can make the switched linear network reach global exponential synchronization. Because the proposed criterion is on the basis of the original synchronization definition and the largest eigenvalue of the switching symmetric matrix, therefore, it is convenient to use in verifying global exponential synchronization of dynamic network with switching linear couplings.
Synchronization in the network of chaotic microwave oscillators
Moskalenko, O.; Phrolov, N.; Koronovskii, A.; Hramov, A.
2013-10-01
Time scale synchronization in networks of chaotic microwave oscillators with the different topologies of the links between nodes has been studied. As a node element of the network the one-dimensional distributed model of the low-voltage vircator has been used. To characterize the degree of synchronization in the whole network the synchronization index has been introduced. The transition to the synchronous regime is shown to take place via cluster time scale synchronization. Meanwhile, the spectral structure of the output signals is complicated sufficiently which allows using such devices in a number of practical applications.
Synchronous ethernet and IEEE 1588 in telecoms next generation synchronization networks
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...
Global Synchronization of Directed Networks with Fast Switching Topologies
International Nuclear Information System (INIS)
Global synchronization of a class of directed dynamical networks with switching topologies is investigated. It is found that if there is a directed spanning tree in the fixed time-average of network topology and the time-average is achieved sufficiently fast, then the network will reach global synchronization for sufficiently large coupling strength. (general)
Adaptive Synchronization of Complex Dynamical Networks with State Predictor
Yuntao Shi; Bo Liu; Xiao Han
2013-01-01
This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results.
Controlling synchronization in large laser networks using number theory
Nixon, Micha; Ronen, Eitan; Friesem, Asher A; Davidson, Nir; Kanter, Ido
2011-01-01
Synchronization in networks with delayed coupling are ubiquitous in nature and play a key role in almost all fields of science including physics, biology, ecology, climatology and sociology. In general, the published works on network synchronization are based on data analysis and simulations, with little experimental verification. Here we develop and experimentally demonstrate various multi-cluster phase synchronization scenarios within coupled laser networks. Synchronization is controlled by the network connectivity in accordance to number theory, whereby the number of synchronized clusters equals the greatest common divisor of network loops. This dependence enables remote switching mechanisms to control the optical phase coherence among distant lasers by local network connectivity adjustments. Our results serve as a benchmark for a broad range of coupled oscillators in science and technology, and offer feasible routes to achieve multi-user secure protocols in communication networks and parallel distribution...
Hybrid synchronization of two independent chaotic systems on complex network
Indian Academy of Sciences (India)
NIAN FUZHONG; LIU WEILONG
2016-06-01
The real network nodes are always interfered by other messages. So, how to realize the hybrid synchronization of two independent chaotic systems based on the complex network is very important. To solve this problem, two other problems should be considered. One is how the same network node of the complex network was affected by different information sources. Another is how to achieve hybrid synchronization on the network. In this paper, the theoretical analysis andnumerical simulation on various complex networks are implemented. The results indicate that the hybrid synchronization of two independent chaotic systems is feasible.
FROM BRAIN DRAIN TO BRAIN NETWORKING
Irina BONCEA
2015-01-01
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 co...
Stimulus-dependent synchronization in delayed-coupled neuronal networks.
Esfahani, Zahra G; Gollo, Leonardo L; Valizadeh, Alireza
2016-01-01
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. PMID:27001428
Role of Network Topology in the Synchronization of Power Systems
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...
Institute of Scientific and Technical Information of China (English)
Zou Yan-Li; Chen Guan-Rong
2009-01-01
This paper studies pinning-controlled synchronization of complex networks with bounded or unbounded synchro nized regions.To study a state-feedback pinning-controlled network with N nodes,it first converts the controlled network to an extended network of N+1 nodes without controls.It is shown that the controlled synchronizability of the given network is determined by the real part of the smallest nonzero eigenvalue of the coupling matrix of its extended network when the synchronized region is unbounded; but it is determined by the ratio of the real parts of the largest and the smallest nonzero eigenvalues of the coupling matrix when the synchronized region is bounded.Both theoretical analysis and numerical simulation show that the portion of controlled nodes has no critical values when the synchronized region is unbounded,but it has a critical value when the synchronized region is bounded.In the former case,therefore,it is possible to control the network to achieve synchronization by pinning only one node.In the latter ease,the network can achieve controlled synchronization only when the portion of controlled nodes is larger than the critical value.
Synchronization of networks of oscillators with distributed delay coupling
Kyrychko, Y. N.; Blyuss, K B; Schoell, E.
2014-01-01
This paper studies the stability of synchronized states in networks where couplings between nodes are characterized by some distributed time delay, and develops a generalized master stability function approach. Using a generic example of Stuart-Landau oscillators, it is shown how the stability of synchronized solutions in networks with distributed delay coupling can be determined through a semi-analytic computation of Floquet exponents. The analysis of stability of fully synchronized and of c...
Synchronization of Neural Networks of Neutral Type with Stochastic Perturbation
Park, Ju H.; Kwon, O. M.
In this letter, the problem of feedback controller design to achieve synchronization for neural network of neutral type with stochastic perturbation is considered. Based on Lyapunov method and LMI (linear matrix inequality) framework, the goal of this letter is to derive an existence criterion of the controller for the synchronization between master and response networks.
End-to-end synchronization in packet switched networks
Almeida, Nuno; Cabral, José; Alves, Artur
1991-01-01
The provision of real time services through a Packet Switched Network, requires the adoption of end-to-end synchronization methods. Several of these methods, are compared and a specific hardware solution is discussed. Results obtained through a specially designed Simulation Programme are included, for several network time delay jitter distributions and synchronization methods. These results are used to evaluate systems and design parameters.
Signal processing techniques for synchronization of wireless sensor networks
Lee, Jaehan; Wu, Yik-Chung; Chaudhari, Qasim; Qaraqe, Khalid; Serpedin, Erchin
2010-11-01
Clock synchronization is a critical component in wireless sensor networks, as it provides a common time frame to different nodes. It supports functions such as fusing voice and video data from different sensor nodes, time-based channel sharing, and sleep wake-up scheduling, etc. Early studies on clock synchronization for wireless sensor networks mainly focus on protocol design. However, clock synchronization problem is inherently related to parameter estimation, and recently, studies of clock synchronization from the signal processing viewpoint started to emerge. In this article, a survey of latest advances on clock synchronization is provided by adopting a signal processing viewpoint. We demonstrate that many existing and intuitive clock synchronization protocols can be interpreted by common statistical signal processing methods. Furthermore, the use of advanced signal processing techniques for deriving optimal clock synchronization algorithms under challenging scenarios will be illustrated.
Synchronous brain activity across individuals inderlies shared psychological perspectives.
Lahnakoski, Juha M.; Glerean, Enrico; Jääskeläinen, Iiro P.; Hyönä, Jukka; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri
2014-01-01
For successful communication, we need to understand the external world consistently with others. This task requires sufficiently similar cognitive schemas or psychological perspectives that act as filters to guide the selection, interpretation and storage of sensory information, perceptual objects and events. Here we show that when individuals adopt a similar psychological perspective during natural viewing, their brain activity becomes synchronized in specific brain regions. We measured brai...
Consensus and Synchronization in Complex Networks
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 ...
Synchronous brain activity across individuals underlies shared psychological perspectives.
Lahnakoski, Juha M; Glerean, Enrico; Jääskeläinen, Iiro P; Hyönä, Jukka; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri
2014-10-15
For successful communication, we need to understand the external world consistently with others. This task requires sufficiently similar cognitive schemas or psychological perspectives that act as filters to guide the selection, interpretation and storage of sensory information, perceptual objects and events. Here we show that when individuals adopt a similar psychological perspective during natural viewing, their brain activity becomes synchronized in specific brain regions. We measured brain activity with functional magnetic resonance imaging (fMRI) from 33 healthy participants who viewed a 10-min movie twice, assuming once a 'social' (detective) and once a 'non-social' (interior decorator) perspective to the movie events. Pearson's correlation coefficient was used to derive multisubject voxelwise similarity measures (inter-subject correlations; ISCs) of functional MRI data. We used k-nearest-neighbor and support vector machine classifiers as well as a Mantel test on the ISC matrices to reveal brain areas wherein ISC predicted the participants' current perspective. ISC was stronger in several brain regions--most robustly in the parahippocampal gyrus, posterior parietal cortex and lateral occipital cortex--when the participants viewed the movie with similar rather than different perspectives. Synchronization was not explained by differences in visual sampling of the movies, as estimated by eye gaze. We propose that synchronous brain activity across individuals adopting similar psychological perspectives could be an important neural mechanism supporting shared understanding of the environment. PMID:24936687
Computation emerges from adaptive synchronization of networking neurons.
Zanin, Massimiliano; Del Pozo, Francisco; Boccaletti, Stefano
2011-01-01
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. PMID:22073167
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.
Synchronization of general complex networks via adaptive control schemes
Indian Academy of Sciences (India)
Ping He; Chun-Guo Jing; Chang-Zhong Chen; Tao Fan; Hassan Saberi Nik
2014-03-01
In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.
Intra- and inter-brain synchronization during musical improvisation on the guitar.
Directory of Open Access Journals (Sweden)
Viktor Müller
Full Text Available Humans interact with the environment through sensory and motor acts. Some of these interactions require synchronization among two or more individuals. Multiple-trial designs, which we have used in past work to study interbrain synchronization in the course of joint action, constrain the range of observable interactions. To overcome the limitations of multiple-trial designs, we conducted single-trial analyses of electroencephalography (EEG signals recorded from eight pairs of guitarists engaged in musical improvisation. We identified hyper-brain networks based on a complex interplay of different frequencies. The intra-brain connections primarily involved higher frequencies (e.g., beta, whereas inter-brain connections primarily operated at lower frequencies (e.g., delta and theta. The topology of hyper-brain networks was frequency-dependent, with a tendency to become more regular at higher frequencies. We also found hyper-brain modules that included nodes (i.e., EEG electrodes from both brains. Some of the observed network properties were related to musical roles during improvisation. Our findings replicate and extend earlier work and point to mechanisms that enable individuals to engage in temporally coordinated joint action.
Intra- and inter-brain synchronization during musical improvisation on the guitar.
Müller, Viktor; Sänger, Johanna; Lindenberger, Ulman
2013-01-01
Humans interact with the environment through sensory and motor acts. Some of these interactions require synchronization among two or more individuals. Multiple-trial designs, which we have used in past work to study interbrain synchronization in the course of joint action, constrain the range of observable interactions. To overcome the limitations of multiple-trial designs, we conducted single-trial analyses of electroencephalography (EEG) signals recorded from eight pairs of guitarists engaged in musical improvisation. We identified hyper-brain networks based on a complex interplay of different frequencies. The intra-brain connections primarily involved higher frequencies (e.g., beta), whereas inter-brain connections primarily operated at lower frequencies (e.g., delta and theta). The topology of hyper-brain networks was frequency-dependent, with a tendency to become more regular at higher frequencies. We also found hyper-brain modules that included nodes (i.e., EEG electrodes) from both brains. Some of the observed network properties were related to musical roles during improvisation. Our findings replicate and extend earlier work and point to mechanisms that enable individuals to engage in temporally coordinated joint action. PMID:24040094
Effective centrality and explosive synchronization in complex networks.
Navas, A; Villacorta-Atienza, J A; Leyva, I; Almendral, J A; Sendiña-Nadal, I; Boccaletti, S
2015-12-01
Synchronization of networked oscillators is known to depend fundamentally on the interplay between the dynamics of the graph's units and the microscopic arrangement of the network's structure. We here propose an effective network whose topological properties reflect the interplay between the topology and dynamics of the original network. On that basis, we are able to introduce the effective centrality, a measure that quantifies the role and importance of each network's node in the synchronization process. In particular, in the context of explosive synchronization, we use such a measure to assess the propensity of a graph to sustain an irreversible transition to synchronization. We furthermore discuss a strategy to induce the explosive behavior in a generic network, by acting only upon a fraction of its nodes. PMID:26764757
Scale-free brain ensemble modulated by phase synchronization
Institute of Scientific and Technical Information of China (English)
Dan WU; Chao-yi LI; Jie LIU; Jing LU; De-zhong YAO
2014-01-01
To listen to brain activity as a piece of music, we proposed the scale-free brainwave music (SFBM) technology, which could translate the scalp electroencephalogram (EEG) into music notes according to the power law of both EEG and music. In the current study, this methodology was further extended to a musical ensemble of two channels. First, EEG data from two selected channels are translated into musical instrument digital interface (MIDI) sequences, where the EEG parameters modulate the pitch, duration, and volume of each musical note. The phase synchronization index of the two channels is computed by a Hilbert transform. Then the two MIDI sequences are integrated into a chorus according to the phase synchronization index. The EEG with a high synchronization index is represented by more consonant musical intervals, while the low index is expressed by inconsonant musical intervals. The brain ensemble derived from real EEG segments illustrates differences in harmony and pitch distribution during the eyes-closed and eyes-open states. Furthermore, the scale-free phenomena exist in the brainwave ensemble. Therefore, the scale-free brain ensemble modulated by phase synchronization is a new attempt to express the EEG through an auditory and musical way, and it can be used for EEG monitoring and bio-feedback.
Understanding brain networks and brain organization
Pessoa, Luiz
2014-09-01
What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. However, as others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal "true" subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different "slices" of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.
Holder continuity of generalized chaos synchronization in complex networks
International Nuclear Information System (INIS)
A complex network consisting of chaotic systems is considered and the existence of the Holder continuous generalized synchronization in the network is studied. First, we divide nodes of the network into two parts according to their dynamical behaviour. Then, based on the Schauder fixed point theorem, sufficient conditions for the existence of the generalized synchronization between them are derived. Moreover, the results are theoretically proved. Numerical simulations validate the theory. (general)
Coevolution of synchronization and cooperation in networks of coupled oscillators
Antonioni, Alberto
2016-01-01
Despite the large number of studies on the framework of synchronization, none of the previous research made the hypothesis that synchronization occurs at a given cost for involved individuals. The introduction of costly interactions leads, instead, to the formulation of a dichotomous scenario in which an individual may decide to cooperate and pay the cost in order to get synchronized with the rest of the population. Alternatively, the same individual can decide to free ride, without incurring in any cost, waiting that others get synchronized to her state. The emergence of synchronization may thus be seen as the byproduct of an evolutionary game in which individuals decide their behavior according to the benefit/cost ratio they receive in the past. We study the onset of cooperation/synchronization in networked populations of Kuramoto oscillators and report how topology is essential in order for cooperation to thrive. We display also how different classes of topology foster differently synchronization both at a...
Detection of silent cells, synchronization and modulatory activity in developing cellular networks.
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. PMID:26097169
Phase synchronization on small-world networks with community structure
International Nuclear Information System (INIS)
In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region (r ≤ 0.001), the network has even worse synchronizability than the case of isolated communities (r = 0). Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales: the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network. (general)
Synchronization of complex networks coupled by periodically intermittent noise
Li, Shuang; Yan, Huiyun; Li, Jiaorui
2016-04-01
Noise is ubiquitous in real systems, so it is important to investigate the effects of noise on the network system. In this paper, the synchronization of complex network coupled by periodically intermittent noise is investigated and a sufficient condition of noise-induced synchronization is obtained analytically via stability theory of stochastic differential equation. The sufficient condition provides a theoretical reference for the analysis of the impact of coupling noise intensity, duration, coupled oscillator number and other parameters on the synchronization behavior. As examples, Rossler-like and Lorenz network systems are presented to verify the theoretical result.
Time Synchronization in Wireless Sensor Networks: A Survey
Directory of Open Access Journals (Sweden)
Prakash Ranganathan
2010-04-01
Full Text Available Time synchronization is a critical piece of infrastructure for any distributed system. Wirelesssensor networks have emerged as an important and promising research area in the recent years. Timesynchronization is important for many sensor network applications that require very precise mapping of gatheredsensor data with the time of the events, for example, in tracking and vehicular surveillance. It also plays animportant role in energy conservation in MAC layer protocols. The paper studies different existing methods,protocols, significant time parameters (clock drift, clock speed, synchronization errors, and topologies to achieveaccurate synchronization in a sensor network. The studied Synchronization protocols include conventional time syncprotocols (RBS, Timing-sync Protocol for Sensor Networks -TPSN, FTSP, and other application specificapproaches such as all node-based approach, a diffusion-based method and group sync approaches aiming atproviding network-wide time. The goal for writing this paper is to study most common existing time synchronizationapproaches and stress the need of a new class of secure-time synchronization protocol that is scalable, topologyindependent, fast convergent, energy efficient, less latent and less application dependent in a heterogeneous hostileenvironment. Our survey provides a valuable framework by which protocol designers can compare new andexisting synchronization protocols from various metric discussed in the paper. So, we are hopeful that this paperwill serve a complete one-stop investigation to study the characteristics of existing time synchronization protocolsand its implementation mechanism in a Sensor network environment.
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.
Synchronization in complex oscillator networks and smart grids.
Dörfler, Florian; Chertkov, Michael; Bullo, Francesco
2013-02-01
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. PMID:23319658
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.
Intra- and interbrain synchronization and network properties when playing guitar in duets
Directory of Open Access Journals (Sweden)
Johanna eSänger
2012-11-01
Full Text Available To further test and explore the hypothesis that synchronous oscillatory brain activity supports interpersonally coordinated behavior during dyadic music performance, we simultaneously recorded the electroencephalogram (EEG from the brains of each of 12 guitar duets repeatedly playing a modified Rondo in two voices by C. G. Scheidler. Indicators of phase locking and of within-brain and between-brain phase coherence were obtained from complex time-frequency signals based on the Gabor transform. Analyses were restricted to the delta (1-4 Hz and theta (4-8 Hz frequency bands. We found that phase locking as well as within-brain and between-brain phase-coherence connection strengths were enhanced at frontal and central electrodes during periods that put particularly high demands on musical coordination. Phase locking was modulated in relation to the experimentally assigned musical roles of leader and follower, corroborating the functional significance of synchronous oscillations in dyadic music performance. Graph theory analyses revealed within-brain and hyperbrain networks with small-worldness properties that were enhanced during musical coordination periods, and community structures encompassing electrodes from both brains (hyperbrain modules. We conclude that brain mechanisms indexed by phase locking, phase coherence, and structural properties of within-brain and hyperbrain networks support interpersonal action coordination (IAC.
Intra- and interbrain synchronization and network properties when playing guitar in duets.
Sänger, Johanna; Müller, Viktor; Lindenberger, Ulman
2012-01-01
To further test and explore the hypothesis that synchronous oscillatory brain activity supports interpersonally coordinated behavior during dyadic music performance, we simultaneously recorded the electroencephalogram (EEG) from the brains of each of 12 guitar duets repeatedly playing a modified Rondo in two voices by C.G. Scheidler. Indicators of phase locking and of within-brain and between-brain phase coherence were obtained from complex time-frequency signals based on the Gabor transform. Analyses were restricted to the delta (1-4 Hz) and theta (4-8 Hz) frequency bands. We found that phase locking as well as within-brain and between-brain phase-coherence connection strengths were enhanced at frontal and central electrodes during periods that put particularly high demands on musical coordination. Phase locking was modulated in relation to the experimentally assigned musical roles of leader and follower, corroborating the functional significance of synchronous oscillations in dyadic music performance. Graph theory analyses revealed within-brain and hyperbrain networks with small-worldness properties that were enhanced during musical coordination periods, and community structures encompassing electrodes from both brains (hyperbrain modules). We conclude that brain mechanisms indexed by phase locking, phase coherence, and structural properties of within-brain and hyperbrain networks support interpersonal action coordination (IAC). PMID:23226120
Node Heterogeneity for Energy Efficient Synchronization for Wireless Sensor Network
DEFF Research Database (Denmark)
Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee
2016-01-01
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 is the...... sources of delay and energy consumption. To minimize the energy consumptions and delay, NHES synchronizes the time slots using TDMA based MAC protocol. The results show that level by level synchronization used in NHES is energy efficient and has less delay as compared to the state-of-the-art solutions...
Analytic treatment of the network synchronization problem with time delays
Hod, Shahar
2010-01-01
Motivated by novel results in the theory of network synchronization, we analyze the effects of nonzero time delays in stochastic synchronization problems with linear couplings in an arbitrary network. We determine {\\it analytically} the fundamental limit of synchronization efficiency in a noisy environment with uniform time delays. We show that the optimal efficiency of the network is achieved for $\\lambda\\tau={{\\pi^{3/2}}\\over{2\\sqrt{\\pi}+4}}\\approx0.738$, where $\\lambda$ is the coupling strength (relaxation coefficient) and $\\tau$ is the characteristic time delay in the communication between pairs of nodes. Our analysis reveals the underlying mechanism responsible for the trade-off phenomena observed in recent numerical simulations of network synchronization problems.
Synchronizing distant nodes: a universal classification of networks
Flunkert, V.; Yanchuk, S.; T. Dahms; Schoell, E.
2010-01-01
Stability of synchronization in delay-coupled networks of identical units generally depends in a complicated way on the coupling topology. We show that for large coupling delays synchronizability relates in a simple way to the spectral properties of the network topology. The master stability function used to determine stability of synchronous solutions has a universal structure in the limit of large delay: it is rotationally symmetric around the origin and increases monotonically with the rad...
Finite-time synchronization for memristor-based neural networks with time-varying delays.
Abdurahman, Abdujelil; Jiang, Haijun; Teng, Zhidong
2015-09-01
Memristive network exhibits state-dependent switching behaviors due to the physical properties of memristor, which is an ideal tool to mimic the functionalities of the human brain. In this paper, finite-time synchronization is considered for a class of memristor-based neural networks with time-varying delays. Based on the theory of differential equations with discontinuous right-hand side, several new sufficient conditions ensuring the finite-time synchronization of memristor-based chaotic neural networks are obtained by using analysis technique, finite time stability theorem and adding a suitable feedback controller. Besides, the upper bounds of the settling time of synchronization are estimated. Finally, a numerical example is given to show the effectiveness and feasibility of the obtained results. PMID:26024807
Statistical analysis of brain network
Sala
2013-01-01
Recent developments in the complex networks analysis, based largely on graph theory, have been used to study the brain network organization. The brain is a complex system that can be represented by a graph. A graph is a mathematical representation which can be useful to study the connectivity of the brain. Nodes in the brain can be identified dividing its volume in regions of interest and links can be identified calculating a measure of dependence between pairs of regions whose ac...
Robustness to noise in synchronization of network motifs: Experimental results
Buscarino, Arturo; Fortuna, Luigi; Frasca, Mattia; Iachello, Marco; Pham, Viet-Thanh
2012-12-01
In this work, we experimentally investigate the robustness to noise of synchronization in all the four-nodes network motifs. The experimental setup consists of four Chua's circuits diffusively coupled in order to implement the six different undirected network motifs that can be obtained with four nodes. In this experimental setup, synchronization in the presence of noise injected in one of the network nodes is investigated and network motifs are compared in terms of the synchronization error obtained. The analysis has been then extended to some selected case studies of networks with five and six nodes. Numerical simulations have been also performed and results in agreement with experiments have been obtained. A correlation between node degree and robustness to noise has been found also in these networks.
Ideal synchronizer for marked pairs in fork-join network
Vyshenski, S. V.; Grigoriev, P. V.; Dubenskaya, Yu. Yu.
2008-01-01
We introduce a new functional element (synchronizer for marked pairs) meant to join results of parallel processing in two-branch fork-join queueing network. Approximations for distribution of sojourn time at the synchronizer are derived along with a validity domain. Calculations are performed assuming that: arrivals to the network form a Poisson process, each branch operates like an M/M/N queueing system. It is shown that mean sojourn time at a real synchronizer node is bounded below by the v...
New synchronization analysis for complex networks with variable delays
International Nuclear Information System (INIS)
This paper deals with the issue of synchronization of delayed complex networks. Differing from previous results, the delay interval [0, d(t)] is divided into some variable subintervals by employing a new method of weighting delays. Thus, new synchronization criteria for complex networks with time-varying delays are derived by applying this weighting-delay method and introducing some free weighting matrices. The obtained results have proved to be less conservative than previous results. The sufficient conditions of asymptotical synchronization are derived in the form of linear matrix inequality, which are easy to verify. Finally, several simulation examples are provided to show the effectiveness of the proposed results. (general)
Adaptive cluster synchronization of directed complex networks with time delays.
Directory of Open Access Journals (Sweden)
Heng Liu
Full Text Available This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks.
Average contraction and synchronization of complex switched networks
International Nuclear Information System (INIS)
This paper introduces an average contraction analysis for nonlinear switched systems and applies it to investigating the synchronization of complex networks of coupled systems with switching topology. For a general nonlinear system with a time-dependent switching law, a basic convergence result is presented according to average contraction analysis, and a special case where trajectories of a distributed switched system converge to a linear subspace is then investigated. Synchronization is viewed as the special case with all trajectories approaching the synchronization manifold, and is thus studied for complex networks of coupled oscillators with switching topology. It is shown that the synchronization of a complex switched network can be evaluated by the dynamics of an isolated node, the coupling strength and the time average of the smallest eigenvalue associated with the Laplacians of switching topology and the coupling fashion. Finally, numerical simulations illustrate the effectiveness of the proposed methods. (paper)
Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay
International Nuclear Information System (INIS)
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
Time concurrency/phase-time synchronization in digital communications networks
Kihara, Masami; Imaoka, Atsushi
1990-01-01
Digital communications networks have the intrinsic capability of time synchronization which makes it possible for networks to supply time signals to some applications and services. A practical estimation method for the time concurrency on terrestrial networks is presented. By using this method, time concurrency capability of the Nippon Telegraph and Telephone Corporation (NTT) digital communications network is estimated to be better than 300 ns rms at an advanced level, and 20 ns rms at final level.
Kagawa, Yuki; Takamatsu, Atsuko
2009-04-01
To reveal the relation between network structures found in two-dimensional biological systems, such as protoplasmic tube networks in the plasmodium of true slime mold, and spatiotemporal oscillation patterns emerged on the networks, we constructed coupled phase oscillators on weighted planar networks and investigated their dynamics. Results showed that the distribution of edge weights in the networks strongly affects (i) the propensity for global synchronization and (ii) emerging ratios of oscillation patterns, such as traveling and concentric waves, even if the total weight is fixed. In-phase locking, traveling wave, and concentric wave patterns were, respectively, observed most frequently in uniformly weighted, center weighted treelike, and periphery weighted ring-shaped networks. Controlling the global spatiotemporal patterns with the weight distribution given by the local weighting (coupling) rules might be useful in biological network systems including the plasmodial networks and neural networks in the brain.
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.
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.
Partial synchronization in stochastic dynamical networks with switching communication channels
Huang, Chi; Ho, Daniel W. C.; Lu, Jianquan; Kurths, Jürgen
2012-06-01
In this paper, the partial synchronization problem of stochastic dynamical networks (SDNs) is investigated. Unlike the existing models, the SDN considered in this paper suffers from a class of communication constraint—only part of nodes' states can be transmitted. Thus, less nodes' states can be used to synchronize the SDN, which makes the analysis of the synchronization problem much harder. A set of channel matrices are introduced to reflect such kind of constraint. Furthermore, due to unpredictable environmental changes, the channel matrices can switch among some communication modes. The switching considered here is governed by a Markov process. To overcome the difficulty, a regrouping method is employed to derive our main results. The obtained conditions guarantee that partial synchronization can be achieved for SDNs under switching communication constraint. Finally, numerical examples are given to illustrate the effectiveness of the theoretical results and how the communication constraint influences synchronization result.
Independent Noise Can Synchronize Interacting Networks of Pulse-Coupled Oscillators
Riecke, Hermann; Meng, John
Structured networks comprised of subnetwork modules are ubiquitous. Motivated by the observation of rhythms and their interaction in different brain areas, we study a network consisting of two subnetworks of pulse-coupled integrate-fire neurons. Through mutual inhibition the neurons in the individual subnetworks can become synchronized and each subnetwork can exhibit coherent oscillatory dynamics, e.g. an ING-rhythm. In the absence of coupling between the networks the rhythms will in general have different frequencies. We investigate the interaction between these different rhythms. Strikingly, we find that increasing the noise level in the input to the individual neurons can synchronize the rhythms of the two networks, even though the inputs to different neurons are uncorrelated, sharing no common component. A heuristic phase model for the coupled networks shows that this synchronization hinges on the fact that only a fraction of the neurons may spike in a given cycle. Thus, the synchronization of the network rhythms differs qualitatively from that of individual oscillators. Supported by NSF-CMMI 1435358.
Pinning impulsive synchronization of stochastic delayed coupled networks
International Nuclear Information System (INIS)
In this paper, the pinning synchronization problem of stochastic delayed complex network (SDCN) is investigated by using a novel hybrid pinning controller. The proposed hybrid pinning controller is composed of adaptive controller and impulsive controller, where the two controllers are both added to a fraction of nodes in the network. Using the Lyapunov stability theory and the novel hybrid pinning controller, some sufficient conditions are derived for the exponential synchronization of such dynamical networks in mean square. Two numerical simulation examples are provided to verify the effectiveness of the proposed approach. The simulation results show that the proposed control scheme has a fast convergence rate compared with the conventional adaptive pinning method. (general)
Robustness to noise in synchronization of complex networks
Buscarino, Arturo; Gambuzza, Lucia Valentina; Porfiri, Maurizio; Fortuna, Luigi; Frasca, Mattia
2013-06-01
In this report, we investigate dynamical robustness of a complex network to noise injected through one of its nodes. We focus on synchronization of coupled nonlinear systems and, as a special instance, we address the classical consensus protocol for linear integrators. We establish an exact closed-form expression of the synchronization error for the consensus protocol and an approximate result for chaotic units. While structural robustness is known to be significantly affected by attacks targeted to network hubs, our results posit that dynamical robustness is controlled by both the topology of the network and the dynamics of the units. We provide examples where hubs perform better or worse than isolated nodes.
Adaptive synchronization in an array of asymmetric coupled neural networks
Institute of Scientific and Technical Information of China (English)
Gao Ming; Cui Bao-Tong
2009-01-01
This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.
Delayed feedback control of synchronization in weakly coupled oscillator networks.
Novičenko, Viktor
2015-08-01
We study control of synchronization in weakly coupled oscillator networks by using a phase-reduction approach. Starting from a general class of limit-cycle oscillators we derive a phase model, which shows that delayed feedback control changes effective coupling strengths and effective frequencies. We derive the analytical condition for critical control gain, where the phase dynamics of the oscillator becomes extremely sensitive to any perturbations. As a result the network can attain phase synchronization even if the natural interoscillatory couplings are small. In addition, we demonstrate that delayed feedback control can disrupt the coherent phase dynamic in synchronized networks. The validity of our results is illustrated on networks of diffusively coupled Stuart-Landau and FitzHugh-Nagumo models. PMID:26382488
Synchronizing computer clocks using a local area network
Levine, Judah
1990-01-01
Researchers completed the first tests of a method to synchronize the clocks of networked computers to the National Institute of Standards and Technology (NIST) time scale. The method uses a server computer to disseminate the time to other clients on the same local-area network. The server is synchronized to NIST using the ACTS protocol over a dial-up telephone line. The software in both the server and the parameters of this model are used to adjust the time of the local clock and the interval between calibration requests in a statistically optimum way. The algorithm maximizes the time between calibrations while at the same time keeping the time of the local clock correct within a specific tolerance. The method can be extended to synchronize computers linked over wide-area networks, and an experiment to test the performance of the algorithms over such networks is being planned.
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...
Synchronized Rhythmic Oscillation in a Noisy Neural Network
Yu, Yuguo; Liu, Feng; Wang, Wei
2003-12-01
The occurrence of synchronized oscillation and its critical behavior in a globally coupled stochastic Hodgkin-Huxley (HH) neuronal network are studied in this paper. It is found that there is a critical curve for the coupling strength versus noise intensity, which shows a V-shaped structure and divides the network behavior into an asynchronous firing state and a synchronous one. Analysis of the scaling behavior near the bifurcation point reveals that this transition is analogous to a second-order phase transition. The frequency of synchronized oscillations is within the range of 40-80 Hz, and its physical origin is explored by studying single HH neuron’s impedance. The intrinsic property of single neuron may account for the generation and the frequency characteristics of synchronized rhythmic oscillations.
Synchronization of networks with time-varying couplings
Institute of Scientific and Technical Information of China (English)
LU Wen-lian; CHEN Tian-ping
2013-01-01
In this paper, we present a review of our recent works on complete synchro-nization analyses of networks of the coupled dynamical systems with time-varying cou-plings. The main approach is composed of algebraic graph theory and dynamic system method. More precisely, the Hajnal diameter of matrix sequence plays a key role in in-vestigating synchronization dynamics and the joint graph across time periods possessing spanning tree is a doorsill for time-varying topologies to reach synchronization. These techniques with proper modification count for diverse models of networks of the cou-pled systems, including discrete-time and continuous-time models, linear and nonlinear models, deterministic and stochastic time-variations. Alternatively, transverse stability analysis of general time-varying dynamic systems can be employed for synchronization study as a special case and proved to be equivalent to Hajnal diameter.
Projective Synchronization in Drive-Response Networks via Impulsive Control
Institute of Scientific and Technical Information of China (English)
GUO Liu-Xiao; XU Zhen-Yuan; HU Man-Feng
2008-01-01
@@ Impulsive projective synchronization in 1 + N coupled chaotic systems are investigated with the drive-response dynamical network (DRDN) model. Based on impulsive stability theory, some simple but less conservative criteria are achieved for projective synchronization in DRDNs. Furthermore, impulsive pinning scheme is also adopted to direct the scaling factor onto the desired value. Numerical simulations on generalized chaotic unified system are illustrated to verify the theoretical results.
Adaptation to synchronization in phase-oscillator networks
Arizmendi, Fernando; Zanette, Damian H.
2008-01-01
We introduce an adaptation algorithm by which an ensemble of coupled oscillators with attractive and repulsive interactions is induced to adopt a prescribed synchronized state. While the performance of adaptation is controlled by measuring a macroscopic quantity, which characterizes the achieved degree of synchronization, adaptive changes are introduced at the microscopic level of the interaction network, by modifying the configuration of repulsive interactions. This scheme emulates the disti...
Pinning synchronization of discrete dynamical networks with delay coupling
Cheng, Ranran; Peng, Mingshu; Zuo, Jun
2016-05-01
The purpose of this paper is to investigate the pinning synchronization analysis for nonlinear coupled delayed discrete dynamical networks with the identical or nonidentical topological structure. Based on the Lyapunov stability theory, pinning control method and linear matrix inequalities, several adaptive synchronization criteria via two kinds of pinning control method are obtained. Two examples based on Rulkov chaotic system are included to illustrate the effectiveness and verification of theoretical analysis.
Energy Efficient Global Clock Synchronization for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Vinod Namboodiri
2010-05-01
Full Text Available Clock synchronization is critical to many sensor networks for the success of the application as well as energyefficiency. Achieving a global time frame through localized averaging of clock values for multiple rounds till convergenceis a promising approach to clock synchronization due to the decentralized nature of computation coupled withscalability. However, it is not clear what power levels for all nodes would make the synchronization process energyefficient.Large power levels lead to faster convergence but consume a lot of energy per round of synchronization.On the other hand, smaller powers consume little energy per round, but convergence is very slow requiring a lotof rounds to achieve synchronization. In this paper we look at the problem of finding a power assignment thatachieves global clock synchronization in the most energy-efficient manner possible. We look at the problem throughtwo dimensions; rate of convergence and energy consumed per round of synchronization. A centralized algorithm ispresented that uses the path congestion of the induced communication graph to estimate which power assignmentshave good convergence properties and find one that minimizes the total energy to achieve clock synchronization.Our evaluation demonstrates that the power assignment derived from this algorithm is very energy-efficient and isapplicable for wireless communication environments with various distance-power gradients. Further, we present asimple distributed algorithm which nodes can execute locally to derive energy-efficient power levels for global clocksynchronization, and is especially useful in large-scale deployments.
Limbic Network Synchronization and Temporal Lobe Epilepsy
Czech Academy of Sciences Publication Activity Database
Jefferys, J. G. R.; Jiruška, Přemysl; de Curtis, M.; Avoli, M.
4. New York : Oxford University Press, 2012 - (Noebels, J.; Avoli, M.; Rogawski, M.; Olsen, R.; Delgado-Escueta, A.), s. 176-189. ISBN 978-0-19-974654-5 Institutional research plan: CEZ:AV0Z50110509 Keywords : synchronization * temporal lobe epilepsy * seizures Subject RIV: FH - Neurology
Synchronous networks for bio-environmental surveillance based on cellular automata
Bao Hoai Lam; Hiep Xuan Huynh; Bernard Pottier
2016-01-01
The paper proposes a new approach to model a bio-environmental surveillance network as synchronous network systems, systems consist of components running simultaneously. In the network, bio-environmental factors compose a physical system of which executions proceed concurrently in synchronous rounds. This system is synchronized with a synchronous wireless sensor network, the observation network. Topology of the surveillance network is based on cellular automata to depict its concurrent charac...
HIGH-ACCURACY SYNCHRONIZATION CONTROL WITH HYBRID NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
Li Jingyuan; Yi Menglin; Wang Yun; Duan Hao
2005-01-01
A novel nonlinear control algorithm based on hybrid neural networks is presented to cope with the high-accuracy synchronization control problem for a dual-actuator electrohydraulic drive system which plays an important role for the development of elastomeric launchers. A new objective function for better synchronization performance is introduced and a learning algorithm to adjust the weights of the neural network, based on the gradient descent algorithm, is also derived. The hybrid neural network control algorithm guarantees high-accuracy synchronization performance of two motion cylinders and fast dynamic response as well as good stability of the control system. Prototype test results on the dual-actuator electrohydraulic drive system verifys the effectiveness of the proposed approach.
Directory of Open Access Journals (Sweden)
R. Hassan
2008-01-01
Full Text Available This study presented a new data link layer design for the virtual bus architecture. The virtual bus is a hierarchical access network offered a broadcast capability for data and signaling. The proposed of the study is to support Quality of Service (QoS in network. During study, the most promising technology capable of delivering full service access at high data rates is Synchronous Digital Hierarchy (SDH/Synchronous Optical Networking (SONET. For that reason, we develop SDH/SONET as a physical layer for the virtual bus network. The main goal of this investigation was to study the performance of transmission medium and the overall performance of the network. We exemplify a Multiple Access Protocol (MAP with Generic Frame Procedure (GFP for transmitting IP network protocol datagram over SDH/SONET. This is a novel method for transmitting MAP with GFP. The protocol adds multi-terminal access capability to an inherently point-to-point link. It enabled the design of seamless networking environments using SDH/SONET as the transmission medium for Metropolitan Area Network (MAN. The new frame mapping protocol is known as Virtual Bus Transport (VBT. The performance of multiple traffic generators in a virtual bus network is of major interest for mobile network provider. We used OMNeT simulation tool to evaluate the result. Through the simulation it proved that the VBT scheduling technique offers better treatment mainly to the higher Qos requirement traffic.
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
Explosive Synchronization and Emergence of Assortativity on Adaptive Networks
Institute of Scientific and Technical Information of China (English)
JIANG Hui-Jun; WU Hao; HOU Zhong-Huai
2011-01-01
@@ We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks,following an Achlioptas process based on dynamic clustering information.During each adaptive step of the network topology,a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules(PRs) applied to the dynamic clusters.Particularly,two types of PRs are considered,namely,the min-PR and max-PR.We demonstrate that the synchronization transition becomes explosive in both cases.Interestingly,we find that the min-PR rule can lead to disassortativity of the network topology,while the max-PR rule leads to assortativity.%We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks, following an Achlioptas process based on dynamic clustering information. During each adaptive step of the network topology, a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules (PRs) applied to the dynamic clusters. Particularly, two types of PRs are considered, namely, the min-PR and max-PR. We demonstrate that the synchronization transition becomes explosive in both cases. Interestingly, we find that the min-PR rule can lead to disassortativity of the network topology, while the max-PR rule leads to assortativity.
Synchronized oscillation in a modular neural network composed of columns
Institute of Scientific and Technical Information of China (English)
LI Su; QI Xianglin; HU Hong; WANG Yunjiu
2005-01-01
The columnar organization is a ubiquitous feature in the cerebral cortex. In this study, a neural network model simulating the cortical columns has been constructed. When fed with random pulse input with constant rate, a column generates synchronized oscillations, with a frequency varying from 3 to 43 Hz depending on parameter values. The behavior of the model under periodic stimulation was studied and the input-output relationship was non-linear. When identical columns were sparsely interconnected, the column oscillator could be locked in synchrony. In a network composed of heterogeneous columns, the columns were organized by intrinsic properties and formed partially synchronized assemblies.
From incoherence to synchronicity in the network Kuramoto model
Kalloniatis, Alexander C.
2010-12-01
We study the synchronization properties of the Kuramoto model of coupled phase oscillators on a general network. Here we distinguish the ability of such a system to self-synchronize from the stability of this behavior. While self-synchronization is a consequence of genuine nonperturbative dynamics, the stability in dynamical systems is usually accessible by fluctuations about a fixed point, here taken to be the phase synchronized solution. We examine this problem in terms of modes of the graph Laplacian, by which the absolute Lyapunov stability of the phase synchronized fixed point is readily demonstrated. Departures from stability are seen to arise at the next order in fluctuations where, depending on a truncation in the number of time-dependent Laplacian modes, the dynamical equations can be reduced to forms resembling those for species population models, the logistic and the Lotka-Volterra equations. Methods from these systems are exploited to analytically derive new critical couplings signaling deviation from classical stability. We thereby analytically explain the existence of an intermediate regime of behavior between incoherence and synchronization, where system wide periodic behaviors are exhibited and stable, unstable, and hyperbolic fixed points can be identified. We discuss these results in light of numerical solutions of the equations of motion for various networks.
Pattern Synchronization in a Two-Layer Neuronal Network
Institute of Scientific and Technical Information of China (English)
SUN Xiao-Juan; LU Qi-Shao
2009-01-01
Pattern synchronization in a two-layer neuronal network is studied.For a single-layer network of Rulkov map neurons,there are three kinds of patterns induced by noise.Additive noise can induce ordered patterns at some intermediate noise intensities in a resonant way;however,for small and large noise intensities there exist excitable patterns and disordered patterns,respectively.For a neuronal network coupled by two single-layer networks with noise intensity differences between layers,we find that the two-layer network can achieve synchrony as the interlayer coupling strength increases.The synchronous states strongly depend on the interlayer coupling strength and the noise intensity difference between layers.
Graph partitions and cluster synchronization in networks of oscillators
Schaub, Michael T; Billeh, Yazan N; Delvenne, Jean-Charles; Lambiotte, Renaud; Barahona, Mauricio
2016-01-01
Synchronization over networks depends strongly on the structure of the coupling between the oscillators.When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges, and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned grap...
Parameter identification and synchronization for uncertain network group with different structures
Li, Chengren; Lü, Ling; Sun, Ying; Wang, Ying; Wang, Wenjun; Sun, Ao
2016-09-01
We design a novel synchronization technique to research the synchronization of network group constituted of uncertain networks with different structures. Based on Lyapunov theorem, the selection principles of the control inputs and the parameter identification laws of the networks are determined, and synchronization conditions of the network group are obtained. Some numerical simulations are provided to verify the correctness and effectiveness of the synchronization technique. We find that the network number, the number of network nodes and network connections indeed will not affect the stability of synchronization of network group.
Concurrent enhancement of percolation and synchronization in adaptive networks
Eom, Young-Ho; Boccaletti, Stefano; Caldarelli, Guido
2016-06-01
Co-evolutionary adaptive mechanisms are not only ubiquitous in nature, but also beneficial for the functioning of a variety of systems. We here consider an adaptive network of oscillators with a stochastic, fitness-based, rule of connectivity, and show that it self-organizes from fragmented and incoherent states to connected and synchronized ones. The synchronization and percolation are associated to abrupt transitions, and they are concurrently (and significantly) enhanced as compared to the non-adaptive case. Finally we provide evidence that only partial adaptation is sufficient to determine these enhancements. Our study, therefore, indicates that inclusion of simple adaptive mechanisms can efficiently describe some emergent features of networked systems’ collective behaviors, and suggests also self-organized ways to control synchronization and percolation in natural and social systems.
Robustness of Diversity Induced Synchronization Transition in a Delayed Small-World Neuronal Network
Institute of Scientific and Technical Information of China (English)
TANG Jun; QU Li-Cheng; LUO Jin-Ming
2011-01-01
In a diverse and delayed small-world neuronal network, we have identified the oscillatory-like synchronization transition between anti-phase and complete synchronization [Phys. Rev. E 83(2011)046207]. Here we study the influence of the network topology and noise on the synchronization transition. The robustness of this transition is investigated. The results show that: (I) the synchronization transition is robust to the neuron number N in the network; (ii) only when the coupled neighbor number k is in the region [4,10], does the synchronization transition exist; (iii) to some extent, the synchronization is destroyed by noise and the oscillatory-like synchronization transition exists for relatively weak noise (D <0.003).%In a diverse and delayed small-world neuronal network,we have identified the oscillatory-like synchronization transition between anti-phase and complete synchronization [Phys.Rev.E 83 (2011) 046207].Here we study the influence of the network topology and noise on the synchronization transition.The robustness of this transition is investigated.The results show that:(i) the synchronization transition is robust to the neuron number N in the network;(ii) only when the coupled neighbor number k is in the region [4,10],does the synchronization transition exist;(iii) to some extent,the synchronization is destroyed by noise and the oscillatory-like synchronization transition exists for relatively weak noise (D ＜0.003).In the theoretical study of neuron systems,the synchronized behavior of a population of interacting neurons,namely,a neuronal network,is a hot issue due to its importance to the processing and transmission of information.[1] Many types of synchronization are identified in neuronal networks,such as complete synchronization,phase synchronization,anti-phase synchronization,phase-lock synchronization,cluster synchronization and lag synchronization.[2
Erosion of synchronization: Coupling heterogeneity and network structure
Skardal, Per Sebastian; Taylor, Dane; Sun, Jie; Arenas, Alex
2016-06-01
We study the dynamics of network-coupled phase oscillators in the presence of coupling frustration. It was recently demonstrated that in heterogeneous network topologies, the presence of coupling frustration causes perfect phase synchronization to become unattainable even in the limit of infinite coupling strength. Here, we consider the important case of heterogeneous coupling functions and extend previous results by deriving analytical predictions for the total erosion of synchronization. Our analytical results are given in terms of basic quantities related to the network structure and coupling frustration. In addition to fully heterogeneous coupling, where each individual interaction is allowed to be distinct, we also consider partially heterogeneous coupling and homogeneous coupling in which the coupling functions are either unique to each oscillator or identical for all network interactions, respectively. We demonstrate the validity of our theory with numerical simulations of multiple network models, and highlight the interesting effects that various coupling choices and network models have on the total erosion of synchronization. Finally, we consider some special network structures with well-known spectral properties, which allows us to derive further analytical results.
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.
Chaos synchronization of two stochastically coupled random Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Hung, Y.-C. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China) and Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)]. E-mail: d9123801@student.nsysu.edu.tw; Ho, M.-C. [Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)]. E-mail: t1603@nknucc.nknu.edu.tw; Lih, J.-S. [Department of Physics and Geoscience, National Pingtung University of Education, Pingtung, Taiwan (China); Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China); Jiang, I-M. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China); Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)
2006-07-24
In this Letter, we study the chaos synchronization of two stochastically coupled random Boolean networks (RBNs). Instead of using the 'site-by-site and all-to-all' coupling, the coupling mechanism we consider here is that: the nth cell in a network is linked by an arbitrarily chosen cell in the other network with probability {rho}, and it possesses no links with probability 1-{rho}. The mechanism is useful to investigate the coevolution of biological species via horizontal genetic exchange. We show that the density evolution of networks can be described by two deterministic coupled polynomial maps. The complete synchronization occurs when the coupling parameter exceeds a critical value. Moreover, the reverse bifurcations in inhomogeneous condition are observed and under our discussion.
Synergistic effect of repulsive inhibition in synchronization of excitatory networks.
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. PMID:26172784
Synchronization Stability in Weighted Complex Networks with Coupling Delays
Institute of Scientific and Technical Information of China (English)
WANG Qing-Yun; DUAN Zhi-Sheng; CHEN Guan-Rong; LU Qi-Shao
2009-01-01
Realistic networks display not only a complex topological structure, but also a heterogeneous distribution of weights in connection strengths.In addition, the information spreading through a complex network is often associated with time delays due to the finite speed of signal transmission over a distance.Hence, the weighted complex network with coupling delays have meaningful implications in real world, and resultantly ga/ns increasing attention in various fields of science and engineering.Based on the theory of asymptotic stability of linear time-delay systems, synchronization stability of the weighted complex dynamical network with coupling delays is investigated, and simple criteria are obtained for both delay-independent and delay-dependent stabilities of synchronization states.The obtained criteria in this paper encompass the established results in the literature as special cases.Some examples are given to illustrate the theoretical results.
GENERAL: Synchronization Stability in Weighted Complex Networks with Coupling Delays
Wang, Qing-Yun; Duan, Zhi-Sheng; Chen, Guan-Rong; Lu, Qi-Shao
2009-04-01
Realistic networks display not only a complex topological structure, but also a heterogeneous distribution of weights in connection strengths. In addition, the information spreading through a complex network is often associated with time delays due to the finite speed of signal transmission over a distance. Hence, the weighted complex network with coupling delays have meaningful implications in real world, and resultantly gains increasing attention in various fields of science and engineering. Based on the theory of asymptotic stability of linear time-delay systems, synchronization stability of the weighted complex dynamical network with coupling delays is investigated, and simple criteria are obtained for both delay-independent and delay-dependent stabilities of synchronization states. The obtained criteria in this paper encompass the established results in the literature as special cases. Some examples are given to illustrate the theoretical results.
Explosive synchronization of complex networks with different chaotic oscillators
International Nuclear Information System (INIS)
Recent studies have shown that explosive synchronization transitions can be observed in networks of phase oscillators [Gómez-Gardeñes J, Gómez S, Arenas A and Moreno Y 2011 Phys. Rev. Lett. 106 128701] and chaotic oscillators [Leyva I, Sevilla-Escoboza R, Buldú J M, Sendiña-Nadal I, Gómez-Gardeñes J, Arenas A, Moreno Y, Gómez S, Jaimes-Reátegui R and Boccaletti S 2012 Phys. Rev. Lett. 108 168702]. Here, we study the effect of different chaotic dynamics on the synchronization transitions in small world networks and scale free networks. The continuous transition is discovered for Rössler systems in both of the above complex networks. However, explosive transitions take place for the coupled Lorenz systems, and the main reason is the abrupt change of dynamics before achieving complete synchronization. Our results show that the explosive synchronization transitions are accompanied by the change of system dynamics
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...
Adaptive projective synchronization with different scaling factors in networks
Institute of Scientific and Technical Information of China (English)
Guo Liu-Xiao; Xu Zhen-Yuan; Hu Man-Feng
2008-01-01
We study projective synchronization with different scaling factors (PSDF) in N coupled chaotic systems networks.By using the adaptive linear control,some sufficient criteria for the PSDF in symmetrical and asymmetrical coupled networks are separately given based on the Lyapunov function method and the left eigenvalue theory.Numerical simulations for a generalized chaotic unified system are illustrated to verify the theoretical results.
Zhou, Lili; Wang, Chunhua; He, Haizhen; Lin, Yuan
2015-05-01
In this paper, a time-controllable combinatorial inner synchronization and outer synchronization of anti-star networks, each of which consists of four-wing hyper-chaotic system as node dynamics, is investigated. Based on the adaptive technique and the stability of Lyapunov function, some sufficient conditions, which can ensure the realization of not only combinatorial inner synchronization within an anti-star network with unknown parameters and external perturbations in the computable time, but also combinatorial outer synchronization between different sub-networks with external perturbations in the computable time, are obtained. Moreover, a simple secure communication scheme, which is based on the adaptive combinatorial outer synchronization between different sub-networks under the influence of stochastic noise and time-delay, is presented. Numerical simulation results show the feasibility and validity of the proposed method.
Synchronization in Small-World-Connected Computer Networks
Guclu, H
2006-01-01
In this thesis we study synchronization phenomena in natural and artificial coupled multi-component systems, applicable to the scalability of parallel discrete-event simulation for systems with asynchronous dynamics. We analyze the properties of the virtual time horizon or synchronization landscape (corresponding to the progress of the processing elements) of these networks by using the framework of non-equilibrium surface growth. When the communication topology mimics that of the short-range interacting underlying system, the virtual time horizon exhibits Kardar-Parisi-Zhang-like kinetic roughening. Although the virtual times, on average, progress at a nonzero rate, their statistical spread diverges with the number of processing elements, hindering efficient data collection. We show that when the synchronization topology is extended to include quenched random communication links (small-world links) between the processing elements, they make a close-to-uniform progress with a nonzero rate, without global sync...
Bai, Hao; Han, Baohui
2013-01-01
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...
Transient to Zero-Lag Synchronization in Excitable Networks
Brama, H.; Peleg, Y; Kinzel, W.; Kanter, I.
2012-01-01
The scaling of transient times to zero-lag synchronization in networks composed of excitable units is shown to be governed by three features of the graph representing the network: the longest path between pairs of neurons (diameter), the largest loop (circumference) and the loop with the maximal average out degree. The upper bound of transient times can vary between O(1) and O(N2), where N is the size of the network, and its scaling can be predicted in many scenarios from finite time accumula...
International Nuclear Information System (INIS)
This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers constructed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua's circuit network example is finally given to show the effectiveness of the proposed synchronization criteria. (general)
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.
Atzil, Shir; Hendler, Talma; Feldman, Ruth
2011-12-01
The mother-infant bond provides the foundation for the infant's future mental health and adaptation and depends on the provision of species-typical maternal behaviors that are supported by neuroendocrine and motivation-affective neural systems. Animal research has demonstrated that natural variations in patterns of maternal care chart discrete profiles of maternal brain-behavior relationships that uniquely shape the infant's lifetime capacities for stress regulation and social affiliation. Such patterns of maternal care are mediated by the neuropeptide Oxytocin and by stress- and reward-related neural systems. Human studies have similarly shown that maternal synchrony--the coordination of maternal behavior with infant signals--and intrusiveness--the excessive expression of maternal behavior--describe distinct and stable maternal styles that bear long-term consequences for infant well-being. To integrate brain, hormones, and behavior in the study of maternal-infant bonding, we examined the fMRI responses of synchronous vs intrusive mothers to dynamic, ecologically valid infant videos and their correlations with plasma Oxytocin. In all, 23 mothers were videotaped at home interacting with their infants and plasma OT assayed. Sessions were micro-coded for synchrony and intrusiveness. Mothers were scanned while observing several own and standard infant-related vignettes. Synchronous mothers showed greater activations in the left nucleus accumbens (NAcc) and intrusive mothers exhibited higher activations in the right amygdala. Functional connectivity analysis revealed that among synchronous mothers, left NAcc and right amygdala were functionally correlated with emotion modulation, theory-of-mind, and empathy networks. Among intrusive mothers, left NAcc and right amygdala were functionally correlated with pro-action areas. Sorting points into neighborhood (SPIN) analysis demonstrated that in the synchronous group, left NAcc and right amygdala activations showed clearer
Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling
International Nuclear Information System (INIS)
A new general network model for two complex networks with time-varying delay coupling is presented. Then we investigate its synchronization phenomena. The two complex networks of the model differ in dynamic nodes, the number of nodes and the coupling connections. By using adaptive controllers, a synchronization criterion is derived. Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion. This study may widen the application range of synchronization, such as in chaotic secure communication. (general)
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.
Flit Synchronous Aelite Network on Chip
Subburaman, Mahesh Balaji
2008-01-01
The deep sub micron process technology and application convergence increases the design challenges in System-on-Chip (SoC). The traditional bus based on chip communication are not scalable and fails to deliver the performance requirements of the complex SoC. The Network on Chip (NoC) has been emerged as a solution to address these complexities of a efficient, high performance, scalable SoC design. The Aethereal NoC provides the latency and throughput bounds by pipelined timedivision multipl...
Synchronization of Switched Interval Networks and Applications to Chaotic Neural Networks
Jinde Cao; Abdulaziz Alofi; Abdullah Al-Mazrooei; Ahmed Elaiw
2013-01-01
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 der...
SynUTC - high precision time synchronization over ethernet networks
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...
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
Ali Muhammad Ali Rushdi; Adnan Ahmad Alsogati
2013-01-01
A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s). Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable a...
Robust synchronization of a class of chaotic networks
Czech Academy of Sciences Publication Activity Database
Čelikovský, Sergej; Lynnyk, Volodymyr; Chen, G.
2013-01-01
Roč. 350, č. 10 (2013), s. 2936-2948. ISSN 0016-0032 R&D Projects: GA ČR(CZ) GAP103/12/1794 Institutional support: RVO:67985556 Keywords : generalized Lorenz system * robust synchronization * dynamical complex network Subject RIV: BC - Control Systems Theory Impact factor: 2.260, year: 2013 http://library.utia.cas.cz/separaty/2013/TR/celikovsky-0398127.pdf
Network synchronization: optimal and pessimal scale-free topologies
Energy Technology Data Exchange (ETDEWEB)
Donetti, Luca [Departamento de Electronica y Tecnologia de Computadores and Instituto de Fisica Teorica y Computacional Carlos I, Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain); Hurtado, Pablo I; Munoz, Miguel A [Departamento de Electromagnetismo y Fisica de la Materia and Instituto Carlos I de Fisica Teorica y Computacional Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain)], E-mail: mamunoz@onsager.ugr.es
2008-06-06
By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability.
Aging and Functional Brain Networks
Tomasi, Dardo; Volkow, Nora D.
2011-01-01
Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the “default-mode” network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis we evaluated resting-state datasets corresponding to 913 hea...
Onset Synchronization in Weighted Complex Networks: the Effect of Weight-Degree Correlation
Li, Menghui; Fan, Ying; Di, Zengru; Lai, Choy-Heng
2011-01-01
By numerical simulations, we compare the onset synchronization of networked phase oscillators under two different weighting schemes. In scheme-I the link weight is proportional to the degrees of the link ending nodes, and is named as the weight-degree correlated (WDC) network; in scheme-II the link weight is distributed randomly regardless of the node degrees, and is named as the weight-degree uncorrelated (WDU) network. We find that by increasing a parameter that governs the weight distribution, the onset synchronization in WDC network is monotonically enhanced, while in WDU network there is a reverse in the synchronization performance. We explain this phenomenon from the viewpoint of gradient network, and obtain the contrary roles of coupling gradient on network synchronization: gradient promotes synchronization in WDC network, while deteriorating synchronization in WDU network. The findings highlight the fact that, besides the link weight, the correlation between the weight and node degree is also importan...
Stability and synchronization control of stochastic neural networks
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.
Computer network time synchronization the network time protocol on earth and in space
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
Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.
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. PMID:26462246
International Nuclear Information System (INIS)
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.
Synchronization in heterogeneous FitzHugh-Nagumo networks with hierarchical architecture
Plotnikov, S. A.; Lehnert, J.; Fradkov, A.L.; Schöll, E.
2016-01-01
We study synchronization in heterogeneous FitzHugh-Nagumo networks. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. Here, we develop a controller to counteract the impact of these heterogeneities. We first analyze the stability of the equilibrium point in a ring network of heterogeneous nodes. We then derive a sufficient condition for synchronization in the absence of control. Based on these results we derive the controller providing synchron...
Energy Technology Data Exchange (ETDEWEB)
Cui Baotong [School of Communication and Control Engineering, Southern Yangtze University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)], E-mail: btcui@vip.sohu.com; Lou Xuyang [School of Communication and Control Engineering, Southern Yangtze University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)], E-mail: louxuyang28945@163.com
2009-01-15
In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme.
Synchronization and coherence resonance in chaotic neural networks
Institute of Scientific and Technical Information of China (English)
Wang Mao-Sheng; Hou Zhong-Huai; Xin Hou-Wen
2006-01-01
Synchronization and coherence of chaotic Morris-Lecar (ML) neural networks have been investigated by numerical methods. The synchronization of the neurons can be enhanced by increasing the number of the shortcuts, even though all neurons are chaotic when uncoupled. Moreover, the coherence of the neurons exhibits a non-monotonic dependence on the density of shortcuts. There is an optimal number of shortcuts at which the neurons' motion is most ordered, i.e. the order parameter (the characteristic correlation time) that is introduced to measure the coherence of the neurons has a maximum. These phenomena imply that stochastic shortcuts can tame spatiotemporal chaos. The effects of the coupling strength have also been studied. The value of the optimal number of shortcuts goes down as the coupling strength increases.
Synchronized states and multistability in a random network of coupled discontinuous maps
Energy Technology Data Exchange (ETDEWEB)
Nag, Mayurakshi; Poria, Swarup, E-mail: swarup-p@yahoo.com [Department of Applied Mathematics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700 009 (India)
2015-08-15
The synchronization behavior of coupled chaotic discontinuous maps over a ring network with dynamic random connections is reported in this paper. It is observed that random rewiring stabilizes one of the two strongly unstable fixed points of the local map. Depending on initial conditions, the network synchronizes to different unstable fixed points, which signifies the existence of synchronized multistability in the complex network. Moreover, the length of discontinuity of the local map has an important role in generating windows of different synchronized fixed points. Synchronized fixed point and synchronized periodic orbits are found in the network depending on coupling strength and different parameter values of the local map. We have identified the existence of period subtracting bifurcation with respect to coupling strength in the network. The range of coupling strength for the occurrence of synchronized multistable spatiotemporal fixed points is determined. This range strongly depends upon the dynamic rewiring probability and also on the local map.
Synchronized states and multistability in a random network of coupled discontinuous maps
International Nuclear Information System (INIS)
The synchronization behavior of coupled chaotic discontinuous maps over a ring network with dynamic random connections is reported in this paper. It is observed that random rewiring stabilizes one of the two strongly unstable fixed points of the local map. Depending on initial conditions, the network synchronizes to different unstable fixed points, which signifies the existence of synchronized multistability in the complex network. Moreover, the length of discontinuity of the local map has an important role in generating windows of different synchronized fixed points. Synchronized fixed point and synchronized periodic orbits are found in the network depending on coupling strength and different parameter values of the local map. We have identified the existence of period subtracting bifurcation with respect to coupling strength in the network. The range of coupling strength for the occurrence of synchronized multistable spatiotemporal fixed points is determined. This range strongly depends upon the dynamic rewiring probability and also on the local map
Synchronization versus neighborhood similarity in complex networks of nonidentical oscillators
Freitas, Celso; Macau, Elbert; Viana, Ricardo Luiz
2015-09-01
Does the assignment order of a fixed collection of slightly distinct subsystems into given communication channels influence the overall ensemble behavior? We discuss this question in the context of complex networks of nonidentical interacting oscillators. Three types of connection configurations are considered: Similar, Dissimilar, and Neutral patterns. These different groups correspond, respectively, to oscillators alike, distinct, and indifferent relative to their neighbors. To construct such scenarios we define a vertex-weighted graph measure, the total dissonance, which comprises the sum of the dissonances between all neighbor oscillators in the network. Our numerical simulations show that the more homogeneous a network, the higher tend to be both the coupling strength required for phase locking and the associated final phase configuration spread over the circle. On the other hand, the initial spread of partial synchronization occurs faster for Similar patterns in comparison to Dissimilar ones, while neutral patterns are an intermediate situation between both extremes.
Human intelligence and brain networks.
Colom, Roberto; Karama, Sherif; Jung, Rex E; Haier, Richard J
2010-01-01
Intelligence can be defined as a general mental ability for reasoning, problem solving, and learning. Because of its general nature, intelligence integrates cognitive functions such as perception, attention, memory, language, or planning. On the basis of this definition, intelligence can be reliably measured by standardized tests with obtained scores predicting several broad social outcomes such as educational achievement, job performance, health, and longevity. A detailed understanding of the brain mechanisms underlying this general mental ability could provide significant individual and societal benefits. Structural and functional neuroimaging studies have generally supported a frontoparietal network relevant for intelligence. This same network has also been found to underlie cognitive functions related to perception, short-term memory storage, and language. The distributed nature of this network and its involvement in a wide range of cognitive functions fits well with the integrative nature of intelligence. A new key phase of research is beginning to investigate how functional networks relate to structural networks, with emphasis on how distributed brain areas communicate with each other. PMID:21319494
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...
Stability of synchronous state in networks of chaotic maps by matrix measure approach
Directory of Open Access Journals (Sweden)
F Aghaei
2015-07-01
Full Text Available Stability of synchronous state is a fundamental problem in synchronization. We study Matrix Measure as an approach for investigating of stability of synchronous states of chaotic maps on complex networks. Matrix Measure is a measure which depends on network structure. Using this measure and comparing with synchronization threshold which depends on the function of the map, show us how the synchronous state can be stabilized. We use these methods for networks with different parameters and topologies. Our numerical calculation shows that synchronous states on more dense networks are more stable. Network’s size is another effective parameter that order of value and extent of stability interval is determined by network’s size. Our results also show that among dense networks, Random and Scale-Free networks have larger stability interval of coupling strength. Finally, we use Error Function to test a prediction of Matrix Measure approach.
Shi, Xia; Xi, Wenqi
2016-05-01
In this paper, burst synchronization and rhythm dynamics of a small-world neuronal network consisting of mixed bursting types of neurons coupled via inhibitory-excitatory chemical synapses are explored. Two quantities, the synchronization parameter and average width factor, are used to characterize the synchronization degree and rhythm dynamics of the neuronal network. Numerical results show that the percentage of the inhibitory synapses in the network is the major factor for we get a similarly bell-shaped dependence of synchronization on it, and the decrease of the average width factor of the network. We also find that not only the value of the coupling strength can promote the synchronization degree, but the probability of random edges adding to the small-world network also can. The ratio of the long bursting neurons has little effect on the burst synchronization and rhythm dynamics of the network.
Aging and functional brain networks
Energy Technology Data Exchange (ETDEWEB)
Tomasi D.; Tomasi, D.; Volkow, N.D.
2011-07-11
Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the 'default-mode' network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis, we evaluated resting-state data sets corresponding to 913 healthy subjects from a public magnetic resonance imaging database using functional connectivity density mapping (FCDM), a voxelwise and data-driven approach, together with parallel computing. Aging was associated with pronounced long-range FCD decreases in DMN and dorsal attention network (DAN) and with increases in somatosensory and subcortical networks. Aging effects in these networks were stronger for long-range than for short-range FCD and were also detected at the level of the main functional hubs. Females had higher short- and long-range FCD in DMN and lower FCD in the somatosensory network than males, but the gender by age interaction effects were not significant for any of the networks or hubs. These findings suggest that long-range connections may be more vulnerable to aging effects than short-range connections and that, in addition to the DMN, the DAN is also sensitive to aging effects, which could underlie the deterioration of attention processes that occurs with aging.
Synchronization of cellular neural networks of neutral type via dynamic feedback controller
Energy Technology Data Exchange (ETDEWEB)
Park, Ju H. [Robust Control and Nonlinear Dynamics Laboratory, Department of Electrical Engineering, Yeungnam University, 214-1 Dae-Dong, Kyongsan 712-749 (Korea, Republic of)], E-mail: jessie@ynu.ac.kr
2009-11-15
In this paper, we aim to study global synchronization for neural networks with neutral delay. A dynamic feedback control scheme is proposed to achieve the synchronization between drive network and response network. By utilizing the Lyapunov function and linear matrix inequalities (LMIs), we derive simple and efficient criterion in terms of LMIs for synchronization. The feedback controllers can be easily obtained by solving the derived LMIs.
Traffic Signal Synchronization in the Saturated High-Density Grid Road Network
Xiaojian Hu; Jian Lu; Wei Wang,; Ye Zhirui
2015-01-01
Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essen...
Adaptive approach to global synchronization of directed networks with fast switching topologies
International Nuclear Information System (INIS)
Global synchronization of directed networks with switching topologies is investigated. It is found that if there exists at least one directed spanning tree in the network with the fixed time-average topology and the time-average topology is achieved sufficiently fast, the network will reach global synchronization for appreciate coupling strength. Furthermore, this appreciate coupling strength may be obtained by local adaptive approach. A sufficient condition about the global synchronization is given. Numerical simulations verify the effectiveness of the adaptive strategy.
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...
Multilayer motif analysis of brain networks
Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito
2016-01-01
In the last decade network science has shed new light on the anatomical connectivity and on correlations in the activity of different areas of the human brain. The study of brain networks has made possible in fact 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 structural and functional networks as separate entities. The recently ...
Spatiotemporal chaos synchronization of an uncertain network based on sliding mode control
International Nuclear Information System (INIS)
The sliding mode control method is used to study spatiotemporal chaos synchronization of an uncertain network. The method is extended from synchronization between two chaotic systems to the synchronization of complex network composed of N spatiotemporal chaotic systems. The sliding surface of the network and the control input are designed. Furthermore, the effectiveness of the method is analysed based on the stability theory. The Burgers equation with spatiotemporal chaos behavior is taken as an example to simulate the experiment. It is found that the synchronization performance of the network is very stable
Model for synchronizer of marked pairs in fork-join network
Vyshenski, S V; Dubenskaya, Yu Yu
2008-01-01
We introduce a model for synchronizer of marked pairs, which is a node for joining results of parallel processing in two-branch fork-join queueing network. A distribution for number of jobs in the synchronizer is obtained. Calculations are performed assuming that: arrivals to the network form a Poisson process, each branch operates like an M/M/N queueing system. It is shown that a mean quantity of jobs in the synchronizer is bounded below by the value, defined by parameters of the network (which contains the synchronizer) and does not depend upon performance and particular properties of the synchronizer. A domain of network parameters is found, where the flow of jobs departing from the synchronizer does not manifest a statistically significant difference from the Poisson type, despite the correlation between job flows from both branches of the fork-join network.
Switching synchronization in one-dimensional memristive networks
Slipko, Valeriy A.; Shumovskyi, Mykola; Pershin, Yuriy V.
2015-11-01
We report on a switching synchronization phenomenon in one-dimensional memristive networks, which occurs when several memristive systems with different switching constants are switched from the high- to low-resistance state. Our numerical simulations show that such a collective behavior is especially pronounced when the applied voltage slightly exceeds the combined threshold voltage of memristive systems. Moreover, a finite increase in the network switching time is found compared to the average switching time of individual systems. An analytical model is presented to explain our observations. Using this model, we have derived asymptotic expressions for memory resistances at short and long times, which are in excellent agreement with results of our numerical simulations.
Finite-Time Synchronizing Control for Chaotic Neural Networks
Directory of Open Access Journals (Sweden)
Chao Zhang
2014-01-01
Full Text Available This paper addresses the finite-time synchronizing problem for a class of chaotic neural networks. In a real communication network, parameters of the master system may be time-varying and the system may be perturbed by external disturbances. A simple high-gain observer is designed to track all the nonlinearities, unknown system functions, and disturbances. Then, a dynamic active compensatory controller is proposed and by using the singular perturbation theory, the control method can guarantee the finite-time stability of the error system between the master system and the slave system. Finally, two illustrative examples are provided to show the effectiveness and applicability of the proposed scheme.
Synchronization in dynamical networks with unconstrained structure switching
del Genio, Charo I; Criado, Regino; Boccaletti, Stefano
2015-01-01
We provide a rigorous solution to the problem of constructing a structural evolution for a network of coupled identical dynamical units that switches between specified topologies without constraints on their structure. The evolution of the structure is determined indirectly, from a carefully built transformation of the eigenvector matrices of the coupling Laplacians, which are guaranteed to change smoothly in time. In turn, this allows to extend the Master Stability Function formalism, which can be used to assess the stability of a synchronized state. This approach is independent from the particular topologies that the network visits, and is not restricted to commuting structures. Also, it does not depend on the time scale of the evolution, which can be faster than, comparable to, or even secular with respect to the the dynamics of the units.
Switching synchronization in one-dimensional memristive networks.
Slipko, Valeriy A; Shumovskyi, Mykola; Pershin, Yuriy V
2015-11-01
We report on a switching synchronization phenomenon in one-dimensional memristive networks, which occurs when several memristive systems with different switching constants are switched from the high- to low-resistance state. Our numerical simulations show that such a collective behavior is especially pronounced when the applied voltage slightly exceeds the combined threshold voltage of memristive systems. Moreover, a finite increase in the network switching time is found compared to the average switching time of individual systems. An analytical model is presented to explain our observations. Using this model, we have derived asymptotic expressions for memory resistances at short and long times, which are in excellent agreement with results of our numerical simulations. PMID:26651772
Successive lag synchronization on dynamical networks with communication delay
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.
Exponential synchronization of general chaotic delayed neural networks via hybrid feedback
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, recurrent multilayer perceptrons (RMLPs). By virtue of LyapunovKrasovskii stability theory and linear matrix inequality (LMI) technique, some exponential synchronization criteria are derived.Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.
Adaptive pinning synchronization in fractional-order uncertain complex dynamical networks with delay
Liang, Song; Wu, Ranchao; Chen, Liping
2016-02-01
Based on the stability theory of fractional-order systems, synchronization of general fractional-order uncertain complex networks with delay is investigated in this paper. By the inequality of the fractional derivative and the comparison principle of the linear fractional equation with delay, synchronization of complex networks with delay is realized under adaptive control. Some sufficient criteria ensuring local asymptotical synchronization under adaptive control and global asymptotical synchronization under adaptive pinning control are derived, respectively. Finally, numerical simulations are presented to demonstrate the validity and feasibility of the proposed synchronization criteria.
Complete characterization of the stability of cluster synchronization in complex dynamical networks.
Sorrentino, Francesco; Pecora, Louis M; Hagerstrom, Aaron M; Murphy, Thomas E; Roy, Rajarshi
2016-04-01
Synchronization is an important and prevalent phenomenon in natural and engineered systems. In many dynamical networks, the coupling is balanced or adjusted to admit global synchronization, a condition called Laplacian coupling. Many networks exhibit incomplete synchronization, where two or more clusters of synchronization persist, and computational group theory has recently proved to be valuable in discovering these cluster states based on the topology of the network. In the important case of Laplacian coupling, additional synchronization patterns can exist that would not be predicted from the group theory analysis alone. Understanding how and when clusters form, merge, and persist is essential for understanding collective dynamics, synchronization, and failure mechanisms of complex networks such as electric power grids, distributed control networks, and autonomous swarming vehicles. We describe a method to find and analyze all of the possible cluster synchronization patterns in a Laplacian-coupled network, by applying methods of computational group theory to dynamically equivalent networks. We present a general technique to evaluate the stability of each of the dynamically valid cluster synchronization patterns. Our results are validated in an optoelectronic experiment on a five-node network that confirms the synchronization patterns predicted by the theory. PMID:27152349
Synchronized RACH-less Handover Solution for LTE Heterogeneous Networks
DEFF Research Database (Denmark)
Barbera, Simone; Pedersen, Klaus I.; Rosa, Claudio; Michaelsen, Per Henrik; Frederiksen, Frank; Shah, Ejaz; Baumgartner, Al
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 reduct...... link failure probability as a result of faster handover execution....
Synchronized RACH-less Handover Solution for LTE Heterogeneous Networks
DEFF Research Database (Denmark)
Barbera, Simone; Pedersen, Klaus I.; Rosa, Claudio; Michaelsen, Per Henrik; Frederiksen, Frank; Shah, Ejaz; Baumgartner, Al
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 reduct...... link failure probability as a result of faster handover execution....
Dynamic transitions among multiple oscillators of synchronized bursts in cultured neural networks
International Nuclear Information System (INIS)
Synchronized neural bursts are a salient dynamic feature of biological neural networks, having important roles in brain functions. This report investigates the deterministic nature behind seemingly random temporal sequences of inter-burst intervals generated by cultured networks of cortical cells. We found that the complex sequences were an intricate patchwork of several noisy ‘burst oscillators’, whose periods covered a wide dynamic range, from a few tens of milliseconds to tens of seconds. The transition from one type of oscillator to another favored a particular passage, while the dwelling time between two neighboring transitions followed an exponential distribution showing no memory. With different amounts of bicuculline or picrotoxin application, we could also terminate the oscillators, generate new ones or tune their periods. (paper)
Contagious Synchronization and Endogenous Network Formation in Financial Networks
Christoph Aymanns and Co-Pierre Georg
2014-01-01
When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observing a larger group of peers conveys more information and thus leads to a stronger social belief. Extending the standard model of Bayesian updating in social networks, we show th...
Complete and phase synchronization in a heterogeneous small-world neuronal network
International Nuclear Information System (INIS)
Synchronous firing of neurons is thought to be important for information communication in neuronal networks. This paper investigates the complete and phase synchronization in a heterogeneous small-world chaotic Hindmarsh–Rose neuronal network. The effects of various network parameters on synchronization behaviour are discussed with some biological explanations. Complete synchronization of small-world neuronal networks is studied theoretically by the master stability function method. It is shown that the coupling strength necessary for complete or phase synchronization decreases with the neuron number, the node degree and the connection density are increased. The effect of heterogeneity of neuronal networks is also considered and it is found that the network heterogeneity has an adverse effect on synchrony. (general)
GENERAL: Complete and phase synchronization in a heterogeneous small-world neuronal network
Han, Fang; Lu, Qi-Shao; Wiercigroch, Marian; Ji, Quan-Bao
2009-02-01
Synchronous firing of neurons is thought to be important for information communication in neuronal networks. This paper investigates the complete and phase synchronization in a heterogeneous small-world chaotic Hindmarsh-Rose neuronal network. The effects of various network parameters on synchronization behaviour are discussed with some biological explanations. Complete synchronization of small-world neuronal networks is studied theoretically by the master stability function method. It is shown that the coupling strength necessary for complete or phase synchronization decreases with the neuron number, the node degree and the connection density are increased. The effect of heterogeneity of neuronal networks is also considered and it is found that the network heterogeneity has an adverse effect on synchrony.
Synchronization and information transmission in spatio-temporal networks of deformable units
Indian Academy of Sciences (India)
F M Moukam Kakmeni; M S Baptista
2008-06-01
We study the relationship between synchronization and the rate with which information is exchanged between nodes in a spatio-temporal network that describes the dynamics of classical particles under a substrate Remoissenet-Peyrard potential. We also show how phase and complete synchronization can be detected in this network. The difficulty in detecting phase synchronization in such a network appears due to the highly non-coherent character of the particle dynamics which unables a proper definition of the phase dynamics. The di±culty in detecting complete synchronization appears due to the spatio character of the potential which results in an asymptotic state highly dependent on the initial state.
Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms
Energy Technology Data Exchange (ETDEWEB)
Sheng Li [School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China); Yang Huizhong [School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)], E-mail: victory8209@yahoo.com.cn; Lou Xuyang [School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)
2009-04-30
This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.
Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling
Institute of Scientific and Technical Information of China (English)
CHEN Jian-Rui; JIAO Li-Cheng; WU Jian-She; WANG Xiao-Hua
2009-01-01
A new general network model for two complex networks with time-varying delay coupling is presented.Then we investigate its synchronization phenomena.The two complex networks of the model differ in dynamic nodes,the number of nodes and the coupling connections.By using adaptive controllers,a synchronization criterion is derived.Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion.This study may widen the application range of synchronization,such as in chaotic secure communication.
Impulsive control for synchronizing delayed discrete complex networks with switching topology
Li, Chaojie; Gao, David Y; Liu, Chao; Chen, Guo
2013-01-01
In this paper, global exponential synchronization of a class of discrete delayed complex networks with switching topology has been investigated by using Lyapunov-Ruzimiki method. The impulsive scheme is designed to work at the time instant of switching occurrence. A time-varying delay-dependent criterion for impulsive synchronization is given to ensure the delayed discrete complex networks switching topology tending to a synchronous state. Furthermore, a numerical simulation is given to illus...
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.
Synchronization of the small-world neuronal network with unreliable synapses
International Nuclear Information System (INIS)
As is well known, synchronization phenomena are ubiquitous in neuronal systems. Recently a lot of work concerning the synchronization of the neuronal network has been accomplished. In these works, the synapses are usually considered reliable, but experimental results show that, in biological neuronal networks, synapses are usually unreliable. In our previous work, we have studied the synchronization of the neuronal network with unreliable synapses; however, we have not paid attention to the effect of topology on the synchronization of the neuronal network. Several recent studies have found that biological neuronal networks have typical properties of small-world networks, characterized by a short path length and high clustering coefficient. In this work, mainly based on the small-world neuronal network (SWNN) with inhibitory neurons, we study the effect of network topology on the synchronization of the neuronal network with unreliable synapses. Together with the network topology, the effects of the GABAergic reversal potential, time delay and noise are also considered. Interestingly, we found a counter-intuitive phenomenon for the SWNN with specific shortcut adding probability, that is, the less reliable the synapses, the better the synchronization performance of the SWNN. We also consider the effects of both local noise and global noise in this work. It is shown that these two different types of noise have distinct effects on the synchronization: one is negative and the other is positive
Complete and phase synchronization in a heterogeneous small-world neuronal network
Institute of Scientific and Technical Information of China (English)
Han Fang; Lu Qi-Shao; Wiercigroch Marian; Ji Quan-Bao
2009-01-01
Synchronous firing of neurons is thought to be important for information communication in neuronal networks. This paper investigates the complete and phase synchronization in a heterogeneous small-world chaotic Hindmarsh-Rose neuronal network. The effects of various network parameters on synchronization behaviour are discussed with some biological explanations. Complete synchronization of small-world neuronal nctworks is studied theoretically by the master stability function method. It is shown that the coupling strength necessary for complete or phase synchronization decreases with the neuron number, the node degree and the connection density are increased. The cffect of heterogeneity of neuronal networks is also considered and it is found that the network heterogeneity has an advcrse effect on synchrony.
Excitement and synchronization of small-world neuronal networks with short-term synaptic plasticity.
Han, Fang; Wiercigroch, Marian; Fang, Jian-An; Wang, Zhijie
2011-10-01
Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically coupled network but impairs synchronization for the chemically coupled one. Both the introduction of shortcuts and the increase of the coupling strength improve synchronization and they are helpful in increasing the excitement for the chemically coupled network, but have little effect on the excitement of the electrically coupled one. PMID:21956933
An adaptive blind watermarking scheme utilizing neural network for synchronization
Institute of Scientific and Technical Information of China (English)
WU Jian-zhen; XIE Jian-ying; YANG Yu-pu
2007-01-01
An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper,which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image's brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust watermark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural network. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.
Synchronization of switched neural networks with mixed delays via impulsive control
International Nuclear Information System (INIS)
Highlights: → Synchronization of neural networks with delay via impulsive control technique is studied. → Synchronization criterion is developed for switched neural networks model. → The concept 'elasticity number' is introduced. - Abstract: This paper concerns the problem of global exponential synchronization for a class of switched neural networks with time-varying delays and unbounded distributed delays via impulsive control method. By using Lyapunov stability theory, new synchronization criterion is derived. In our synchronization criterion, the switching law can be arbitrary and the concept of average impulsive interval is utilized such that the obtained synchronization criterion is less conservative than those based on maximum of impulsive intervals. Numerical simulations are given to show the effectiveness and less conservativeness of the theoretical results.
Scaling in topological properties of brain networks.
Singh, Soibam Shyamchand; Khundrakpam, Budhachandra; Reid, Andrew T; Lewis, John D; Evans, Alan C; Ishrat, Romana; Sharma, B Indrajit; Singh, R K Brojen
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. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of network structure, which reveals the self-similar rules governing the network structure. Further, the calculated fractal dimensions of brain networks of different species are found to decrease when one goes from lower to higher level species which implicates the more ordered and self-organized topography at higher level species. The sparsely distributed hubs in brain networks may be most influencing nodes but their absence may not cause network breakdown, and centrality parameters characterizing them also follow one parameter scaling law indicating self-similar roles of these hubs at different levels of organization in brain networks. The local-community-paradigm decomposition plot and calculated local-community-paradigm-correlation co-efficient of brain networks also shows the evidence for self-organization in these networks. PMID:27112129
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.
Dong, Hui; Ling, Rongyao; Zhang, Dan
2016-03-01
This paper is concerned with the network-based H∞ synchronization control for a class of discrete time-delay neural networks, and attention is focused on how to reduce the communication rate since the communication resource is limited. Techniques such as the measurement size reduction, signal quantization and stochastic signal transmission are introduced to achieve the above goal. An uncertain switched system model is first proposed to capture the above-networked uncertainties. Based on the switched system theory and Lyapunov stability approach, a sufficient condition is obtained such that the closed-loop synchronization system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. The controller gains are determined by solving a set of linear matrix inequalities (LMIs). A numerical example is finally presented to show the effectiveness of the proposed design method.
Institute of Scientific and Technical Information of China (English)
Zhang Qing-Ling; Lu Ling; Zhang Yi
2011-01-01
A method to eliminate spiral waves and spatiotemporal chaos by using the synchronization transmission technology of network signals is proposed in this paper. The character of the spiral waves and the spatiotemporal chaos in the Fitzhugh-Nagumo model is presented. The network error evolution equation with spatiotemporal variables and the corresponding eigenvalue equation are determined based on the stability theory,and the global synchronization condition is obtained. Simulations are made in a complex network with Fitzhugh-Nagumo models as the nodes to verify the effectiveness of the synchronization transmission principle of the network signal.
International Nuclear Information System (INIS)
A method to eliminate spiral waves and spatiotemporal chaos by using the synchronization transmission technology of network signals is proposed in this paper. The character of the spiral waves and the spatiotemporal chaos in the Fitzhugh—Nagumo model is presented. The network error evolution equation with spatiotemporal variables and the corresponding eigenvalue equation are determined based on the stability theory, and the global synchronization condition is obtained. Simulations are made in a complex network with Fitzhugh—Nagumo models as the nodes to verify the effectiveness of the synchronization transmission principle of the network signal. (general)
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.
Support Network Responses to Acquired Brain Injury
Chleboun, Steffany; Hux, Karen
2011-01-01
Acquired brain injury (ABI) affects social relationships; however, the ways social and support networks change and evolve as a result of brain injury is not well understood. This study explored ways in which survivors of ABI and members of their support networks perceive relationship changes as recovery extends into the long-term stage. Two…
Limitations and tradeoffs in synchronization of large-scale networks with uncertain links
Diwadkar, Amit; Vaidya, Umesh
2016-04-01
The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies.
Local extinction synchronizes population dynamics in spatial networks.
Matter, Stephen F; Roland, Jens
2010-03-01
Spatial population theory predicts that synchrony in the dynamics of local populations should decrease as dispersal among populations decreases. Thus, it would be expected that the extinction of local populations and the attendant loss of immigrants to surrounding populations would reduce synchrony. We tested this hypothesis through a large-scale experiment, simulation of the experimental system and general models. Experimental removal of two adjacent subpopulations of the Rocky Mountain Apollo butterfly, Parnassius smintheus within a network consisting of 15 other local populations resulted in a decrease in immigration to surrounding populations that was proportional to their connectivity to the removal populations. These populations also showed a significant increase in synchrony during population removal. The spatial extent of the synchrony showed good agreement with the predicted loss of immigrants owing to the removals. Simulation of the Parnassius system showed a similar short-term result and also indicated that permanent loss of populations produces structural changes increasing synchrony. General models indicate that an increase in synchrony following extinction occurs when populations undergoing extinction have different carrying capacities than surrounding populations. The result is not owing to biased migration per se, but rather is because of the number of immigrants relative to the carrying capacity. Synchrony following extinction should be most common for patchy populations, but can occur in any situation where carrying capacities differ. Overall, our results indicate that local extinction can create a positive feedback for extinction risk, increasing the probability of extinction for population networks by synchronizing their dynamics. PMID:19889700
Wen, Guanghui; Yu, Wenwu; Hu, Guoqiang; Cao, Jinde; Yu, Xinghuo
2015-12-01
This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M -matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topology-dependent multiple Lyapunov functions is proposed for analyzing the synchronization behavior of the whole network. It is theoretically shown that the global pinning synchronization in switched complex networks can be ensured if some nodes are appropriately pinned and the coupling is carefully selected. Interesting issues of how many and which nodes should be pinned for possibly realizing global synchronization are further addressed. Finally, some numerical simulations on coupled neural networks are provided to verify the theoretical results. PMID:26595418
Mutated Genes in Schizophrenia Map to Brain Networks
... 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal cortex area of the brain. ... of spontaneous mutations in genes that form a network in the front region of the brain. The ...
Stochastic Synchronization of Neutral-Type Neural Networks with Multidelays Based on M-Matrix
Wuneng Zhou; Xueqing Yang; Jun Yang; Jun Zhou
2015-01-01
The problem of stochastic synchronization of neutral-type neural networks with multidelays based on M-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional and M-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switc...
Du, Wen-ju; Zhang, Jian-gang; An, Xin-lei; Qin, Shuang; Yu, Jian-ning
2016-01-01
The paper presents a new urban public traffic supernetwork model by using the existing bus network modeling method, consisting of the conventional bus traffic network and the urban rail traffic network. We investigate the synchronization problem of urban public traffic supernetwork model by using the coupled complex network’s outer synchronization theory. Analytical and numerical simulations are given to illustrate the impact of traffic dispatching frequency and traffic lines optimization to ...
Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
Huaiqin Wu; Luying Zhang; Sanbo Ding; Xueqing Guo; Lingling Wang
2013-01-01
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 memrist...
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.
Simulation and Analysis of Reciever-Receiver Time Synchronization in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Devendrasingh Thakore
2012-07-01
Full Text Available Wireless sensor networks were initially deployed for military applications. Gradually researchers found them to be very useful in applications like weather monitoring, target tracking, agriculture, industrial applications, and recently smart homes and kindergartens. All the WSN applications need partial or full time synchronization. Applications like acoustic ranging, target tracking or monitoring need a common notion of time. Every data is time stamped sensor nodes local clock. Two main approaches to time synchronization are receiver-receiver synchronization and sender-receiver synchronization. In this paper we analyze the receiver-receiver synchronization and discuss the results of simulation in network simulator. This study, design considerations and simulation methodology will help a lot to the designer for designing a time synchronization scheme or system.
A novel approach to synchronization of nonlinearly coupled network systems with delays
Tseng, Jui-Pin
2016-06-01
In this investigation, a novel approach to establishing the global synchronization of coupled network systems is presented. Under this approach, individual subsystems can be non-autonomous, and the coupling configuration is rather general. The coupling terms can be non-diffusive, nonlinear, time-dependent, asymmetric, and with time delays. With an iteration scheme, the problem of synchronization is transformed into solving a corresponding linear system of algebraic equations. Subsequently, delay-dependent and delay-independent criteria for global synchronization can be established. We implement the present approach to analyze synchronization of the FitzHugh-Nagumo systems under delayed and nonlinear sigmoidal coupling. Two examples are presented to demonstrate new dynamical scenarios, where oscillatory behavior and multistability emerge or are suppressed as the coupled neurons synchronize under the synchronization criterion. In addition, asynchrony induced by the coupling strength or coupling delay occurs while the synchronization criterion is violated.
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.
Scaling in topological properties of brain networks
Soibam Shyamchand Singh; Budhachandra Khundrakpam; Andrew T. Reid; Lewis, John D.; Evans, Alan C.; Romana Ishrat; B. Indrajit Sharma; R K Brojen Singh
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. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networ...
Scaling in topological properties of brain networks
Singh, Soibam Shyamchand; Singh, Khundrakpam Budhachandra; Ishrat, Romana; Sharma, B. Indrajit; Singh, R. K. Brojen
2015-01-01
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. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of netwo...
Balancing the Brain: Resting State Networks and Deep Brain Stimulation
Kringelbach, Morten L.
2011-01-01
Over the last three decades, large numbers of patients with otherwise treatment-resistant disorders have been helped by deep brain stimulation, yet a full scientific understanding of the underlying neural mechanisms is still missing. We have previously proposed that efficacious deep brain stimulation works by restoring the balance of the brain’s resting state networks. Here, we extend this proposal by reviewing how detailed investigations of the highly coherent functional and structural brain...
Institute of Scientific and Technical Information of China (English)
Yang Dai; YunZe Cai; Xiao-Ming Xu
2009-01-01
Exponential estimates and sufficient conditions for the exponential synchronization of complex dynamical networks with bounded time-varying delays are given in terms of linear matrix inequalities (LMIs). A generalized complex networks model involving both neutral delays and retarded ones is presented. The exponential synchronization problem of the complex networks is converted equivalently into the exponential stability problem of a group of uncorrelated delay functional differential equations with mixed time-varying delays. By utilizing the free weighting matrix technique, a less conservative delay-dependent synchronization criterion is derived. An illustrative example is provided to demonstrate the effectiveness of the proposed method.
Model for synchronizer of marked pairs in fork-join network
Vyshenski, S. V.; Grigoriev, P. V.; Dubenskaya, Yu. Yu.
2008-01-01
We introduce a model for synchronizer of marked pairs, which is a node for joining results of parallel processing in two-branch fork-join queueing network. A distribution for number of jobs in the synchronizer is obtained. Calculations are performed assuming that: arrivals to the network form a Poisson process, each branch operates like an M/M/N queueing system. It is shown that a mean quantity of jobs in the synchronizer is bounded below by the value, defined by parameters of the network (wh...
Non-fragile H∞ synchronization of memristor-based neural networks using passivity theory.
Mathiyalagan, K; Anbuvithya, R; Sakthivel, R; Park, Ju H; Prakash, P
2016-02-01
In this paper, we formulate and investigate the mixed H∞ and passivity based synchronization criteria for memristor-based recurrent neural networks with time-varying delays. Some sufficient conditions are obtained to guarantee the synchronization of the considered neural network based on the master-slave concept, differential inclusions theory and Lyapunov-Krasovskii stability theory. Also, the memristive neural network is considered with two different types of memductance functions and two types of gain variations. The results for non-fragile observer-based synchronization are derived in terms of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed criterion is demonstrated through numerical examples. PMID:26655373
Robust networked H∞ synchronization of nonidentical chaotic Lur'e systems
International Nuclear Information System (INIS)
We mainly investigate the robust networked H∞ synchronization problem of nonidentical chaotic Lur'e systems. In the design of the synchronization scheme, some network characteristics, such as nonuniform sampling, transmission-induced delays, and data packet dropouts, are considered. The parameters of master—slave chaotic Lur'e systems often allow differences. The sufficient condition in terms of linear matrix inequality (LMI) is obtained to guarantee the dissipative synchronization of nonidentical chaotic Lur'e systems in network environments. A numerical example is given to illustrate the validity of the proposed method. (general)
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-based...... fronthaul imposes. We analyze possible contributions to frequency and phase error. We verify the feasibility of using the IEEE 1588v2 also know as Precision Time Protocol (PTP) for providing accurate phase and frequency synchronization. The study is based on simulations made in OPNET modeler. Thereby we...
Scaling in topological properties of brain networks
Singh, S.S.; Khundrakpam, B.; 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.
Tuncay, Caglar
2010-01-01
The electroencephalographic (EEG) data intracerebrally recorded from 20 epileptic humans with different brain origins of focal epilepsies or types of seizures, ages and sexes are investigated (nearly 700 million data). Multi channel univariate amplitude analyses are performed and it is shown that time dependent Shannon entropies can be used to predict focal epileptic seizure onsets in different epileptogenic brain zones of different patients. Formations or time evolutions of the synchronizations in the brain signals from epileptogenic or non epileptogenic areas of the patients in ictal interval or inter-ictal interval are further investigated employing spatial or temporal differences of the entropies.
Synchronization of fractional-order complex-valued neural networks with time delay.
Bao, Haibo; Park, Ju H; Cao, Jinde
2016-09-01
This paper deals with the problem of synchronization of fractional-order complex-valued neural networks with time delays. By means of linear delay feedback control and a fractional-order inequality, sufficient conditions are obtained to guarantee the synchronization of the drive-response systems. Numerical simulations are provided to show the effectiveness of the obtained results. PMID:27268259
Qiuxiang Bian; Jinde Cao; Jie Wu; Hongxing Yao; Tingfang Zhang; Xiaoxu Ling
2013-01-01
A new model of switched complex bipartite neural network (SCBNN) with infinite distributed delays and derivative coupling is established. Using linear matrix inequality (LMI) approach, some synchronization criteria are proposed to ensure the synchronization between two SCBNNs by constructing effective controllers. Some numerical simulations are provided to illustrate the effectiveness of the theoretical results obtained in this paper.
Cross-Frequency Coupling in Real and Virtual Brain Networks
Directory of Open Access Journals (Sweden)
Viktor Jirsa
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.
Small-world brain networks in schizophrenia
Institute of Scientific and Technical Information of China (English)
Mingli LI; Zhuangfei CHEN; Tao LI
2012-01-01
Over the last decade the combination of brain neuroimaging techniques and graph theoretical analysis of the complex anatomical and functional networks in the brain have provided an exciting new platform for exploring the etiology of mental disorders such as schizophrenia. This review introduces the current status of this work, focusing on these networks in schizophrenia. The evidence supporting the findings of reduced efficiency of information exchange in schizophrenia both within local brain regions and globally throughout the brain is reviewed and the potential relationship of these changes to cognitive and clinical symptoms is discussed. Finally we propose some suggestions for future research.
Bit Level Synchronized MAC Protocol for Multireader RFID Networks
Directory of Open Access Journals (Sweden)
Namboodiri Vinod
2010-01-01
Full Text Available The operation of multiple RFID readers in close proximity results in interference between the readers. This issue is termed the reader collision problem and cannot always be solved by assigning them to different frequency channels due to technical and regulatory limitations. The typical solution is to separate the operation of such readers across time. This sequential operation, however, results in a long delay to identify all tags. We present a bit level synchronized (BLSync MAC protocol for multi-reader RFID networks that allows multiple readers to operate simultaneously on the same frequency channel. The BLSync protocol solves the reader collision problem by allowing all readers to transmit the same query at the same time. We analyze the performance of using the BLSync protocol and demonstrate benefits of 40%–50% in terms of tag reading delay for most settings. The benefits of BLSync, first demonstrated through analysis, are then validated and quantified through simulations on realistic reader-tag layouts.
Neural Network Controllers in DTC of Synchronous Motor Drives
Directory of Open Access Journals (Sweden)
Sudhakar Ambarapu
2013-07-01
Full Text Available In recent times, permanent magnet synchronous motors (PMSM have gained numerous industrial applications, because of simple structure, high efficiency and ease of maintenance. But these motors have a nonlinear mathematical model. To resolve this problem several studies have suggested the application of vector control (VC and direct torque control (DTC with soft-computing (SC techniques. This paper presents neuro direct torque control (NDTC of PMSM. Hence this paper aims to present a technique to control speed and torque with reduced ripple compared to previous techniques. The outputs of Artificial Neural Network(ANN controller mechanism is compared with that of classical DTC and the results demonstrate the influence of ANN is improved compared to classical DTC topology. The system is also verified and proved to be operated stably with reduced torque ripple, very low speed, sudden speed reversals, at low torque and at high torque. The proposed method validity and effectiveness has been verified by computer simulations using Matlab/Simulink®. These results are compared with the ones obtained with a classical DTC using PI speed controller.
Pain: a distributed brain information network?
Directory of Open Access Journals (Sweden)
Hiroaki Mano
2015-01-01
Full Text Available 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.
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
Directory of Open Access Journals (Sweden)
Ali Muhammad Ali Rushdi
2013-01-01
Full Text Available A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s. Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable at a latter instance t2 to its value at an earlier instance t1. This equation remains valid when the times t1 and t2 are both augmented by any integral multiple of the underlying time period. In other words, there are infinitely many versions of a reduced scalar equation, any of which is useful for deducing information about the cyclic behavior of the network. However, to obtain correct information about the transient behavior of the network, one must find the true reduced scalar equation for which instances t1 and t2 are minimal. This study investigates the nature, derivation and utilization of reduced scalar equations. It relies on Boolean-algebraic manipulations for the derivation of such equations and suggests that this derivation can be facilitated by seeking certain orthogonality relations among certain successive (albeit not necessarily consecutive instances of the same scalar variable. We demonstrate, contrary to previously published assumptions or assertions, that there is typically no common reduced scalar equation for all the scalar variables. Each variable usually satisfies its own distinct reduced scalar equation. We also demonstrate that the derivation of a reduced scalar equation is achieved not only by proving it but also by disproving an immediately preceding version of it when such a version might exist. We also demonstrate that, despite the useful insight supplied by the reduced scalar equations, they do not provide a total solution like the
Multilayer motif analysis of brain networks
Battiston, Federico; Chavez, Mario; Latora, Vito
2016-01-01
In the last decade network science has shed new light on the anatomical connectivity and on correlations in the activity of different areas of the human brain. The study of brain networks has made possible in fact 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 structural and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows to perform a multiplex analysis of the human brain where the structural and functional layers are considered at the same time. In this work we describe how to classify subgraphs in multiplex networks, and we extend motif analysis to networks with many layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, respectively obtained from diffusion and functional magnetic resonance imaging. Results i...
Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network
Energy Technology Data Exchange (ETDEWEB)
Zhu, Darui, E-mail: zdarui@163.com [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an 710049 (China); School of Electrical Engineering, Xi' an Jiaotong University, Xi' an 710049 (China); Liu, Chongxin; Yan, Bingnan [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an 710049 (China); School of Electrical Engineering, Xi' an Jiaotong University, Xi' an 710049 (China)
2014-01-24
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.
Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network
International Nuclear Information System (INIS)
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.
Zhou, Guangye; Li, Chengren; Li, Tingting; Yang, Yi; Wang, Chen; He, Fangjun; Sun, Jingchang
2016-09-01
Some typical dual-ring erbium-doped fiber lasers with hyperchaos behaviors are taken as nodes to construct two kinds of small-world networks-NW and WS networks. Based on Lyapunov stability theorem, the appropriate controllers are designed and the outer synchronization between the small-world networks with diverse structures and different node numbers is further investigated. The simulation results show that the perfect synchronization between the complex small-world networks is realized, which is of potential application for all optical communication network.
Wenbing Zhang; Yang Tang; Qingying Miao; Wei Du
2013-08-01
This paper investigates the synchronization problem of coupled switched neural networks (SNNs) with mode-dependent impulsive effects and time delays. The main feature of mode-dependent impulsive effects is that impulsive effects can exist not only at the instants coinciding with mode switching but also at the instants when there is no system switching. The impulses considered here include those that suppress synchronization or enhance synchronization. Based on switching analysis techniques and the comparison principle, the exponential synchronization criteria are derived for coupled delayed SNNs with mode-dependent impulsive effects. Finally, simulations are provided to illustrate the effectiveness of the results. PMID:24808570
A plasma switch synchronous closing operations in high-voltage networks
International Nuclear Information System (INIS)
Overvoltages and overcurrent arising in energizing or in fast reclosing operations are a concerning problem in high-voltage networks. Reduction of overvoltages and overcurrents is possible using the synchronous closing technique. Some attempts have been done to perform the synchronous closing with conventional circuit-breakers. But since the requirements to synchronous closing and to current interruption are very contradictory this technique is not yet a common practice. Three simple cases may be used as examples to show the benefits of synchronous closing; energizaton of grounded star capacitor bank; back-to-back switching of large capacitor banks; and fast reclosing on transmission lines
Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings
Energy Technology Data Exchange (ETDEWEB)
Zhang, Jianbao [School of Science, Hangzhou Dianzi University, Hangzhou 310018 (China); Ma, Zhongjun, E-mail: mzj1234402@163.com [School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004 (China); Zhang, Gang [College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024 (China)
2013-12-15
This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network.
Time synchronization of a wired sensor network for structural health monitoring
International Nuclear Information System (INIS)
This paper introduces a time synchronization system for wired smart sensor networks to be applied to the structural health monitoring of gigantic structures. The jitter of sensor nodes in the wired network depends on the wire length between the origin and the destination of the time synchronization signals. The proposed system can theoretically achieve the accuracy to limit the jitter of sensors within 34 ns by adjusting the timing depending on the wire length, and experimentally showed the jitter of 190 m separation to be within 25 ns. The proposed system uses local area network (LAN) cables and does not require additional cabling for synchronization. Thus the proposed synchronization system can be embedded in the sensor network with minimal cost
Yang, Dong-Sheng; Liu, Zhen-Wei; Zhao, Yan; Liu, Zhao-Bing
2012-04-01
The networked synchronization problem of a class of master-slave chaotic systems with time-varying communication topologies is investigated in this paper. Based on algebraic graph theory and matrix theory, a simple linear state feedback controller is designed to synchronize the master chaotic system and the slave chaotic systems with a time-varying communication topology connection. The exponential stability of the closed-loop networked synchronization error system is guaranteed by applying Lyapunov stability theory. The derived novel criteria are in the form of linear matrix inequalities (LMIs), which are easy to examine and tremendously reduce the computation burden from the feedback matrices. This paper provides an alternative networked secure communication scheme which can be extended conveniently. An illustrative example is given to demonstrate the effectiveness of the proposed networked synchronization method.
Institute of Scientific and Technical Information of China (English)
Yang Dong-Sheng; Liu Zhen-Wei; Zhao Yan; Liu Zhao-Bing
2012-01-01
The networked synchronization problem of a class of master-slave chaotic systems with time-varying communication topologies is investigated in this paper. Based on algebraic graph theory and matrix theory,a simple linear state feedback controller is designed to synchronize the master chaotic system and the slave chaotic systems with a timevarying communication topology connection.The exponential stability of the closed-loop networked synchronization error system is guaranteed by applying Lyapunov stability theory.The derived novel criteria are in the form of linear matrix inequalities (LMIs),which are easy to examine and tremendously reduce the computation burden from the feedback matrices.This paper provides an alternative networked secure communication scheme which can be extended conveniently.An illustrative example is given to demonstrate the effectiveness of the proposed networked synchronization method.
International Nuclear Information System (INIS)
The networked synchronization problem of a class of master-slave chaotic systems with time-varying communication topologies is investigated in this paper. Based on algebraic graph theory and matrix theory, a simple linear state feedback controller is designed to synchronize the master chaotic system and the slave chaotic systems with a time-varying communication topology connection. The exponential stability of the closed-loop networked synchronization error system is guaranteed by applying Lyapunov stability theory. The derived novel criteria are in the form of linear matrix inequalities (LMIs), which are easy to examine and tremendously reduce the computation burden from the feedback matrices. This paper provides an alternative networked secure communication scheme which can be extended conveniently. An illustrative example is given to demonstrate the effectiveness of the proposed networked synchronization method. (general)
Synchronizations in small-world networks of spiking neurons: Diffusive versus sigmoid couplings
International Nuclear Information System (INIS)
By using a semianalytical dynamical mean-field approximation previously proposed by the author [H. Hasegawa, Phys. Rev. E 70, 066107 (2004)], we have studied the synchronization of stochastic, small-world (SW) networks of FitzHugh-Nagumo neurons with diffusive couplings. The difference and similarity between results for diffusive and sigmoid couplings have been discussed. It has been shown that with introducing the weak heterogeneity to regular networks, the synchronization may be slightly increased for diffusive couplings, while it is decreased for sigmoid couplings. This increase in the synchronization for diffusive couplings is shown to be due to their local, negative feedback contributions, but not due to the short average distance in SW networks. Synchronization of SW networks depends not only on their structure but also on the type of couplings
Indian Academy of Sciences (India)
Arindam Saha; R E Amritkar
2014-12-01
Kuramoto oscillators have been proposed earlier as a model for interacting systems that exhibit synchronization. In this article, we study the difference between networks with symmetric and asymmetric distribution of natural frequencies. We first indicate that synchronization frequency of oscillators in a completely connected network is always equal to the mean of the natural frequency distribution. In particular, shape of the natural frequency distribution does not affect the synchronization frequency in this case. Then, we analyse the case of oscillators in a directed ring network, where asymmetry in the natural frequency distribution is seen to shift the synchronization frequency of the network. We also present an estimate of the shift in the frequencies for slightly asymmetric distributions.
The application of graph theoretical analysis to complex networks in the brain.
Reijneveld, Jaap C; Ponten, Sophie C; Berendse, Henk W; Stam, Cornelis J
2007-11-01
Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features. PMID:17900977
Size-dependent regulation of synchronized activity in living neuronal networks
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.
Pinning synchronization of the drive and response dynamical networks with lag
Directory of Open Access Journals (Sweden)
Wen Bohui
2014-09-01
Full Text Available This paper investigates the pinning synchronization of two general complex dynamical networks with lag. The coupling configuration matrices in the two networks are not need to be symmetric or irreducible. Several convenient and useful criteria for lag synchronization are obtained based on the lemma of Schur complement and the Lyapunov stability theory. Especially, the minimum number of controllers in pinning control can be easily obtained. At last, numerical simulations are provided to verify the effectiveness of the criteria
International Nuclear Information System (INIS)
The spatiotemporal dynamics of networks based on a ring of coupled oscillators with regular shortcuts beyond the nearest-neighbor couplings is studied by using master stability equations and numerical simulations. The generic criterion for dynamic synchronization has been extended to arbitrary network topologies with zero row-sum. The symmetry-breaking oscillation patterns that resulted from the Hopf bifurcation from synchronous states are analyzed by the symmetry group theory
Synchronization criteria for coupled Hopfield neural networks with time-varying delays
International Nuclear Information System (INIS)
This paper proposes new delay-dependent synchronization criteria for coupled Hopfield neural networks with time-varying delays. By construction of a suitable Lyapunov—Krasovskii's functional and use of Finsler's lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed methods. (general)
Projective synchronization of fractional-order memristor-based neural networks.
Bao, Hai-Bo; Cao, Jin-De
2015-03-01
This paper investigates the projective synchronization of fractional-order memristor-based neural networks. Sufficient conditions are derived in the sense of Caputo's fractional derivation and by combining a fractional-order differential inequality. Two numerical examples are given to show the effectiveness of the main results. The results in this paper extend and improve some previous works on the synchronization of fractional-order neural networks. PMID:25463390
Synchronization stability of general complex dynamical networks with time-varying delays
International Nuclear Information System (INIS)
The synchronization problem of some general complex dynamical networks with time-varying delays is investigated. Both time-varying delays in the network couplings and time-varying delays in the dynamical nodes are considered. The novel delay-dependent criteria in terms of linear matrix inequalities (LMI) are derived based on free-weighting matrices technique and appropriate Lyapunov functional proposed recently. Numerical examples are given to illustrate the effectiveness and advantage of the proposed synchronization criteria
GENERAL: Synchronization of time-delay chaotic systems on small-world networks with delayed coupling
Qi, Wei; Wang, Ying-Hai
2009-04-01
By using the well-known Ikeda model as the node dynamics, this paper studies synchronization of time-delay systems on small-world networks where the connections between units involve time delays. It shows that, in contrast with the undelayed case, networks with delays can actually synchronize more easily. Specifically, for randomly distributed delays, time-delayed mutual coupling suppresses the chaotic behaviour by stabilizing a fixed point that is unstable for the uncoupled dynamical system.
Identifying modular relations in complex brain networks
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Mørup, Morten; Siebner, Hartwig; Madsen, Kristoffer H.; Hansen, Lars Kai
2012-01-01
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...... overfit 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 in...
Resting Network Plasticity Following Brain Injury
Toru Nakamura; Hillary, Frank G.; Biswal, Bharat B.
2009-01-01
The purpose of this study was to examine neural network properties at separate time-points during recovery from traumatic brain injury (TBI) using graph theory. Whole-brain analyses of the topological properties of the fMRI signal were conducted in 6 participants at 3 months and 6 months following severe TBI. Results revealed alterations of network properties including a change in the degree distribution, reduced overall strength in connectivity, and increased "small-worldness" from 3 months ...
Stress Impact on Resting State Brain Networks
Soares, José Miguel; Sampaio, Adriana; Ferreira, Luís Miguel, colab.; 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 s...
The formation of synchronization cliques during the development of modular neural networks
International Nuclear Information System (INIS)
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
Li, Ning; Cao, Jinde
2015-01-01
In this paper, we investigate synchronization for memristor-based neural networks with time-varying delay via an adaptive and feedback controller. Under the framework of Filippov's solution and differential inclusion theory, and by using the adaptive control technique and structuring a novel Lyapunov functional, an adaptive updated law was designed, and two synchronization criteria were derived for memristor-based neural networks with time-varying delay. By removing some of the basic literature assumptions, the derived synchronization criteria were found to be more general than those in existing literature. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results. PMID:25299765
Impulsive synchronization of two coupled complex networks with time-delayed dynamical nodes
International Nuclear Information System (INIS)
In this paper, we investigate the impulsive synchronization between two coupled complex networks with time-delayed dynamical nodes. Based on the Lyapunov stability, the linear feedback control and the impulsive control theories, the linear feedback and the impulsive controllers are designed separately. By using the generalized Barbalat's lemma, the global asymptotic impulsive synchronization of the drive—response complex networks is derived and some corresponding sufficient conditions are also obtained. Numerical examples are presented to verify the effectiveness and the correctness of the synchronization criteria. (general)
Indian Academy of Sciences (India)
JIANG SHENGQIN; LU XIAOBO
2016-06-01
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 most of pinning intermittent controls are only appliedto the nodes from 1 to l or centralized nodes. Moreover, sufficient conditions are derived to guarantee the synchronization of coloured delayed networks based on Lyapunov stability theorem. Finally, numerical simulations are provided to verify the validity of the obtained results.
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.
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. PMID:26797471
Finite-Time Synchronization of Chaotic Complex Networks with Stochastic Disturbance
Directory of Open Access Journals (Sweden)
Liangliang Li
2014-12-01
Full Text Available This paper is concerned with the problem of finite-time synchronization in complex networks with stochastic noise perturbations. By using a novel finite-time ℒ -operator differential inequality and other inequality techniques, some novel sufficient conditions are obtained to ensure finite-time stochastic synchronization for the complex networks concerned, where the coupling matrix need not be symmetric. The effects of control parameters on synchronization speed and time are also analyzed, and the synchronization time in this paper is shorter than that in the existing literature. The results here are also applicable to both directed and undirected weighted networks without any information of the coupling matrix. Finally, an example with numerical simulations is given to demonstrate the effectiveness of the proposed method.
Synchronization of fractional-order colored dynamical networks via open-plus-closed-loop control
Yang, Lixin; Jiang, Jun; Liu, Xiaojun
2016-02-01
In this paper, the synchronization of a fractional-order colored complex dynamical network model is studied for the first time. In this network model, color edges imply that both the outer coupling topology and the inner interactions between any pair of nodes may be different, and color nodes mean that local dynamics may be different. Based on the stability theory of fractional-order systems, the scheme of synchronization for fractional-order colored complex dynamical networks is presented. To achieve the synchronization of a complex fractional-order edge-colored network, the open-plus-closed-loop (OPCL) strategy is adopted and effective controllers for synchronization are designed. The open-plus-closed-loop (OPCL) strategy avoids the need for computation of eigenvalues of a very large matrix. Then, a synchronization method for a class of fractional-order colored complex network, containing both colored edges and colored nodes, is developed and some effective synchronization conditions via close-loop control are presented. Two examples of numerical simulations are presented to show the effectiveness of the proposed control strategies.
Synchronization enhancement via an oscillatory bath in a network of self-excited cells
Indian Academy of Sciences (India)
B R Nana Nbendjo; H G Enjieu Kadji; Hilda A Cerdeira
2015-02-01
The possibility of using a dynamic environment to achieve and optimize phase synchronization in a network of self-excited cells with free-end boundary conditions is addressed in this paper. The dynamic environment is an oscillatory bath coupled linearly to a network of four cells. The boundaries of the stable solutions of the dynamical states as well as the ranges of coupling parameters leading to stability and instability of synchronization are determined. Numerical simulations are used to check the accuracy and to complement the result obtained from analytical treatment. The robustness of synchronization strategy is tested using a local and global injection of Gaussian white noise in the network. The control gain parameter of the bath coupling can modulate the occurrence of synchronization in the network without prior requirement of direct coupling among all the cells. The process of synchronization obtained through local injection is independent of the node at which noise is injected into the system. As compared to local injection, the global injection scheme increases the range of noise amplitude for which synchronization occurs in the network.
Abe, Mitsunari; Fukuyama, Hidenao; Mima, Tatsuya
2014-01-01
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. PMID:24619090
An Adaptive Complex Network Model for Brain Functional Networks
Gomez Portillo, Ignacio J.; Gleiser, Pablo M.
2009-01-01
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 diffe...
Hierarchical modularity in human brain functional networks
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 ...
Online Social Networks and the Consumer Brain
Adina Zara
2011-01-01
Online social networks have moved from being seen as trends or communication channels to becoming an effective tool for business. They play a large role in consumer’s life. The online purchase decisions are not as rational as we consider them to be. This paper shows why it is important the study of the human brain for social networks website. By researching how they react to different online marketing stimuli is a key factor in having success on an online social network.
Node Heterogeneity for Energy Efficient Synchronization for Wireless Sensor Network
DEFF Research Database (Denmark)
Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee
2016-01-01
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 is the i......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...
Cluster synchronization in the adaptive complex dynamical networks via a novel approach
International Nuclear Information System (INIS)
This Letter investigates cluster synchronization in the adaptive complex dynamical networks with nonidentical nodes by a local control method and a novel adaptive strategy for the coupling strengths of the networks. In this approach, the coupling strength of each node adjusts adaptively only based on the state information of its neighborhood. By means of the proposed scheme, the sufficient conditions for achieving cluster synchronization are derived analytically by utilizing Lyapunov stability theory. It is demonstrated that the synchronization performance is sensitively affected by the control gain, the inner-coupling matrix and the network topological structure. The numerical simulations are performed to verify the effectiveness of the theoretical results. - Highlights: → We present a more realistic adaptive complex network model with diverse nodes. → The local controllers are designed based the community structure of the network. → Each node's coupling strength adapts self only by the state of its neighborhood. → The synchronization effect is affected by the network structure and control gain. → The Cluster synchronization method is robust against noise perturbation.
Discrete-time systems with random switches: From systems stability to networks synchronization.
Guo, Yao; Lin, Wei; Ho, Daniel W C
2016-03-01
In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developed approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks. PMID:27036191
Discrete-time systems with random switches: From systems stability to networks synchronization
Guo, Yao; Lin, Wei; Ho, Daniel W. C.
2016-03-01
In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developed approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks.
International business cycle synchronization since the 1870s: Evidence from a novel network approach
Antonakakis, Nikolaos; Gogas, Periklis; Papadimitriou, Theophilos; Sarantitis, Georgios Antonios
2016-04-01
In this study, we examine the issue of business cycle synchronization from a historical perspective in 27 developed and developing countries. Based on a novel complex network approach, the Threshold-Minimum Dominating Set (T-MDS), our results reveal heterogeneous patterns of international business cycle synchronization during fundamental globalization periods since the 1870s. In particular, the proposed methodology reveals that worldwide business cycles de-coupled during the Gold Standard, though they were synchronized during the Great Depression. The Bretton Woods era was associated with a lower degree of synchronization as compared to that during the Great Depression, while worldwide business cycle synchronization increased to unprecedented levels during the latest period of floating exchange rates and the Great Recession.
Individual diversity of functional brain network economy.
Hahn, Andreas; Kranz, Georg S; Sladky, Ronald; Ganger, Sebastian; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert
2015-04-01
On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders. PMID:25411715
Generalized Projective Synchronization between Two Complex Networks with Time-Varying Coupling Delay
Institute of Scientific and Technical Information of China (English)
SUN Mei; ZENG Chang-Yan; TIAN Li-Xin
2009-01-01
Generalized projective synchronization (GPS) between two complex networks with time-varying coupling delay is investigated. Based on the Lyapunov stability theory, a nonlinear controller and adaptive updated laws are designed. Feasibility of the proposed scheme is proven in theory. Moreover, two numerical examples are presented, using the energy resource system and Lü's system [Physica A 382 (2007) 672] as the nodes of the networks. GPS between two energy resource complex networks with time-varying coupling delay is achieved. This study can widen the application range of the generalized synchronization methods and will be instructive for the demand-supply of energy resource in some regions of China.
Experimental realization of synchronization in complex networks with Chua's circuits like nodes
International Nuclear Information System (INIS)
In this paper, an experimental study on practical realization of synchronization in globally coupled networks with Chua's circuits like nodes is presented. Synchronization of coupled multiple Chua's circuits is achieved by appealing to results from complex systems theory. In particular, we design and implement electronically complex dynamical networks composed by three coupled Chua's circuits, considering two scenarios: (i) without master node, and (ii) with (periodic and chaotic) master node. The interactions in the networks are defined by coupling the first state of each Chua's circuit.
H∞ synchronization of chaotic neural networks with time-varying delays
International Nuclear Information System (INIS)
In this paper, we investigate the problem of H∞ synchronization for chaotic neural networks with time-varying delays. A new model of the networks with disturbances in both master and slave systems is presented. By constructing a suitable Lyapunov—Krasovskii functional and using a reciprocally convex approach, a novel H∞ synchronization criterion for the networks concerned is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed method
Zhang, Wei; Li, Chuandong; Huang, Tingwen; He, Xing
2015-12-01
Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential inequality and a new delay impulsive differential inequality, some sufficient conditions are derived, which depend on impulsive and coupling delays to guarantee the exponential synchronization of the memristor-based recurrent neural networks. Impulses with and without delay and time-varying delay are considered for modeling the coupled neural networks simultaneously, which renders more practical significance of our current research. Finally, numerical simulations are given to verify the effectiveness of the theoretical results. PMID:26054076
Finite-time synchronization control of a class of memristor-based recurrent neural networks.
Jiang, Minghui; Wang, Shuangtao; Mei, Jun; Shen, Yanjun
2015-03-01
This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method. PMID:25536233
Generalized Projective Synchronization between Two Complex Networks with Time-Varying Coupling Delay
International Nuclear Information System (INIS)
Generalized projective synchronization (GPS) between two complex networks with time-varying coupling delay is investigated. Based on the Lyapunov stability theory, a nonlinear controller and adaptive updated laws are designed. Feasibility of the proposed scheme is proven in theory. Moreover, two numerical examples are presented, using the energy resource system and Lü's system [Physica A 382 (2007) 672] as the nodes of the networks. GPS between two energy resource complex networks with time-varying coupling delay is achieved. This study can widen the application range of the generalized synchronization methods and will be instructive for the demand–supply of energy resource in some regions of China
Explosive synchronization in clustered scale-free networks: Revealing the existence of chimera state
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.
Projective synchronization of a complex network with different fractional order chaos nodes
International Nuclear Information System (INIS)
Based on the stability theory of the linear fractional order system, projective synchronization of a complex network is studied in the paper, and the coupling functions of the connected nodes are identified. With this method, the projective synchronization of the network with different fractional order chaos nodes can be achieved, besides, the number of the nodes does not affect the stability of the whole network. In the numerical simulations, the chaotic fractional order Lü system, Liu system and Coullet system are chosen as examples to show the effectiveness of the scheme. (general)
Experimental realization of synchronization in complex networks with Chua's circuits like nodes
Energy Technology Data Exchange (ETDEWEB)
Posadas-Castillo, C. [Engineering Faculty, Baja California Autonomous University (UABC), Km. 103, Carretera Tijuana-Ensenada, 22860 Ensenada, BC (Mexico); Engineering Mechanic and Electric Faculty of Nuevo Leon Autonomous University (UANL), Pedro de Alba, S.N., Cd. Universitaria, San Nicolas de los Garza, NL (Mexico); Cruz-Hernandez, C. [Electronics and Telecommunications Department, Scientific Research and Advanced Studies of Ensenada (CICESE), Km. 107, Carretera Tijuana-Ensenada, 22860 Ensenada, BC (Mexico)], E-mail: ccruz@cicese.mx; Lopez-Gutierrez, R.M. [Engineering Faculty, Baja California Autonomous University (UABC), Km. 103, Carretera Tijuana-Ensenada, 22860 Ensenada, BC (Mexico)
2009-05-30
In this paper, an experimental study on practical realization of synchronization in globally coupled networks with Chua's circuits like nodes is presented. Synchronization of coupled multiple Chua's circuits is achieved by appealing to results from complex systems theory. In particular, we design and implement electronically complex dynamical networks composed by three coupled Chua's circuits, considering two scenarios: (i) without master node, and (ii) with (periodic and chaotic) master node. The interactions in the networks are defined by coupling the first state of each Chua's circuit.
Wang, Dongshu; Huang, Lihong; Tang, Longkun
2015-08-01
This paper is concerned with the synchronization dynamical behaviors for a class of delayed neural networks with discontinuous neuron activations. Continuous and discontinuous state feedback controller are designed such that the neural networks model can realize exponential complete synchronization in view of functional differential inclusions theory, Lyapunov functional method and inequality technique. The new proposed results here are very easy to verify and also applicable to neural networks with continuous activations. Finally, some numerical examples show the applicability and effectiveness of our main results.
Indian Academy of Sciences (India)
Suman Acharyya; R E Amritkar
2015-02-01
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 oscillators. From the stability criteria of the MSF, we construct optimal networks with better synchronization property, i.e., the synchronization is stable for widest possible range of coupling parameters. In the optimized networks the nodes with parameter value at one extreme are selected as hubs. The pair of nodes with larger parameter difference are preferred to create links in the optimized networks, and the optimized networks are found to be disassortative in nature, i.e., the nodes with high degree tend to connect with nodes with low degree.
Pinning synchronization of fractional-order complex networks with Lipschitz-type nonlinear dynamics.
Wang, Junwei; Ma, Qinghua; Chen, Aimin; Liang, Zhipeng
2015-07-01
This paper deals with pinning synchronization problem of fractional-order complex networks with Lipschitz-type nonlinear nodes and directed communication topology. We first reformulate the problem as a global asymptotic stability problem by describing network evolution in terms of error dynamics. Then, a novel frequency domain approach is developed by using Laplace transform, algebraic graph theory and generalized Gronwall inequality. We show that pinning synchronization can be ensured if the extended network topology contains a spanning tree and the coupling strength is large enough. Furthermore, we provide an easily testable criterion for global pinning synchronization depending on fractional-order, network topology, oscillator dynamics and state feedback. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. PMID:25721408
Megam Ngouonkadi, Elie B.; Nono, Martial Kabong; Tamba, Victor Kamdoum; Fotsin, Hilaire B.
2015-11-01
Diffusive electrical connections in neuronal networks are instantaneous, while excitatory or inhibitory couplings through chemical synapses contain a transmission time-delay. Moreover, chemical synapses are nonlinear dynamical systems whose behavior can be described by nonlinear differential equations. In this work, neuronal networks with diffusive electrical couplings and time-delayed dynamic chemical couplings are considered. We investigate the effects of distributed time delays on phase synchronization of bursting neurons. We observe that in both excitatory and Inhibitory chemical connections, the phase synchronization might be enhanced when time-delay is taken into account. This distributed time delay can induce a variety of phase-coherent dynamical behaviors. We also study the collective dynamics of network of bursting neurons. The network model presents the so-called Small-World property, encompassing neurons whose dynamics have two time scales (fast and slow time scales). The neuron parameters in such Small-World network, are supposed to be slightly different such that, there may be synchronization of the bursting (slow) activity if the coupling strengths are large enough. Bounds for the critical coupling strengths to obtain burst synchronization in terms of the network structure are given. Our studies show that the network synchronizability is improved, as its heterogeneity is reduced. The roles of synaptic parameters, more precisely those of the coupling strengths and the network size are also investigated.
Kuptsov, Pavel V.; Kuptsova, Anna V.
2016-04-01
A generalized model of star-like network is suggested that takes into account non-additive coupling and nonlinear transformation of coupling variables. For this model a method of analysis of synchronized cluster stability is developed. Using this method three star-like networks based on Ikeda, predator-prey and Hénon maps are studied.
Robust projective lag synchronization in drive-response dynamical networks via adaptive control
Al-mahbashi, G.; Noorani, M. S. Md; Bakar, S. A.; Al-sawalha, M. M.
2016-02-01
This paper investigates the problem of projective lag synchronization behavior in drive-response dynamical networks (DRDNs) with identical and non-identical nodes. An adaptive control method is designed to achieve projective lag synchronization with fully unknown parameters and unknown bounded disturbances. These parameters were estimated by adaptive laws obtained by Lyapunov stability theory. Furthermore, sufficient conditions for synchronization are derived analytically using the Lyapunov stability theory and adaptive control. In addition, the unknown bounded disturbances are also overcome by the proposed control. Finally, analytical results show that the states of the dynamical network with non-delayed coupling can be asymptotically synchronized onto a desired scaling factor under the designed controller. Simulation results show the effectiveness of the proposed method.
Complex networks in brain electrical activity
Ruffini, G; Grau, C; Marco, J; Ray, C
2005-01-01
We analyze the complex networks associated with brain electrical activity. Multichannel EEG measurements are first processed to obtain 3D voxel activations using the tomographic algorithm LORETA. Then, the correlation of the current intensity activation between voxel pairs is computed to produce a voxel cross-correlation coefficient matrix. Using several correlation thresholds, the cross-correlation matrix is then transformed into a network connectivity matrix and analyzed. To study a specific example, we selected data from an earlier experiment focusing on the MMN brain wave. The resulting analysis highlights significant differences between the spatial activations associated with Standard and Deviant tones, with interesting physiological implications. When compared to random data networks, physiological networks are more connected, with longer links and shorter path lengths. Furthermore, as compared to the Deviant case, Standard data networks are more connected, with longer links and shorter path lengths--i....
International Nuclear Information System (INIS)
This paper studies chaos-based data gathering scheme in multiple sink wireless sensor networks. In the proposed scheme, each wireless sensor node has a simple chaotic oscillator. The oscillators generate spike signals with chaotic interspike intervals, and are impulsively coupled by the signals via wireless communication. Each wireless sensor node transmits and receives sensor information only in the timing of the couplings. The proposed scheme can exhibit various chaos synchronous phenomena and their breakdown phenomena, and can effectively gather sensor information with the significantly small number of transmissions and receptions compared with the conventional scheme. Also, the proposed scheme can flexibly adapt various wireless sensor networks not only with a single sink node but also with multiple sink nodes. This paper introduces our previous works. Through simulation experiments, we show effectiveness of the proposed scheme and discuss its development potential.
Nakano, Hidehiro; Utani, Akihide; Miyauchi, Arata; Yamamoto, Hisao
2011-04-01
This paper studies chaos-based data gathering scheme in multiple sink wireless sensor networks. In the proposed scheme, each wireless sensor node has a simple chaotic oscillator. The oscillators generate spike signals with chaotic interspike intervals, and are impulsively coupled by the signals via wireless communication. Each wireless sensor node transmits and receives sensor information only in the timing of the couplings. The proposed scheme can exhibit various chaos synchronous phenomena and their breakdown phenomena, and can effectively gather sensor information with the significantly small number of transmissions and receptions compared with the conventional scheme. Also, the proposed scheme can flexibly adapt various wireless sensor networks not only with a single sink node but also with multiple sink nodes. This paper introduces our previous works. Through simulation experiments, we show effectiveness of the proposed scheme and discuss its development potential.
International Nuclear Information System (INIS)
In this paper, the exponential synchronization between two nonlinearly coupled complex networks with non-delayed and delayed coupling is investigated with Lyapunov-Krasovskii-type functionals. Based on the stability analysis of the impulsive differential equation and the linear matrix inequality, sufficient delay-dependent conditions for exponential synchronization are derived, and a linear impulsive controller and simple updated laws are also designed. Furthermore, the coupling matrices need not be symmetric or irreducible. Numerical examples are presented to verify the effectiveness and correctness of the synchronization criteria obtained.
Adaptive Synchronization of Memristor-Based Neural Networks with Time-Varying Delays.
Wang, Leimin; Shen, Yi; Yin, Quan; Zhang, Guodong
2015-09-01
In this paper, adaptive synchronization of memristor-based neural networks (MNNs) with time-varying delays is investigated. The dynamical analysis here employs results from the theory of differential equations with discontinuous right-hand sides as introduced by Filippov. Sufficient conditions for the global synchronization of MNNs are established with a general adaptive controller. The update gain of the controller can be adjusted to control the synchronization speed. The obtained results complement and improve the previously known results. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results. PMID:25389244
Global generalized synchronization in networks of different time-delay systems
Senthilkumar, D. V.; Suresh, R.; Lakshmanan, M.; Kurths, J.
2013-09-01
We show that global generalized synchronization (GS) exists in structurally different time-delay systems, even with different orders, with quite different fractal (Kaplan-Yorke) dimensions, which emerges via partial GS in symmetrically coupled regular networks. We find that there exists a smooth transformation in such systems, which maps them to a common GS manifold as corroborated by their maximal transverse Lyapunov exponent. In addition, an analytical stability condition using the Krasvoskii-Lyapunov theory is deduced. This phenomenon of GS in strongly distinct systems opens a new way for an effective control of pathological synchronous activity by means of extremely small perturbations to appropriate variables in the synchronization manifold.
Finite-time synchronization of complex dynamical networks with multi-links via intermittent controls
Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhao, Hui; Ren, Jingfeng
2016-02-01
This paper considers finite-time synchronization of complex multi-links dynamical networks with or without internal time delays via intermittent controls. Two simple intermittent feedback controllers are designed to achieve finite-time synchronization between the drive and response system. Some novel and effective finite-time synchronization criteria are derived based on finite-time stability analysis techniques. By constructing suitable Lyapunov functions, we theoretically prove its correctness. Finally, two numerical simulation examples are given to show the effectiveness of proposed method in this paper.
Efficient Topology-aware Coarse Graining for Synchronization in Directed Networks
Zeng, An
2010-01-01
Coarse graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve the same statistical properties as well as the dynamic behaviors as the initial networks. Some methods have been proposed and found effective in undirected networks, while the study on coarse graining in directed networks lacks of consideration. In this paper, we proposed a Topology-aware Coarse Graining (TCG) method to coarse grain the directed networks. Performing the linear stability analysis of synchronization and numerical simulation of the Kuramoto model on four kinds of directed networks, including tree-like networks and variants of Barab\\'{a}si-Albert networks, Watts-Strogatz networks and Erd\\"{o}s-R\\'{e}nyi networks, we find our method can effectively preserve the network synchronizability.
Graph theoretical analysis of organization of functional brain networks in ADHD.
Ahmadlou, Mehran; Adeli, Hojjat; Adeli, Amir
2012-01-01
This article presents a new methodology for investigation of the organization of the overall and hemispheric brain network of patients with attention-deficit hyperactivity disorder (ADHD) using theoretical analysis of a weighted graph with the goal of discovering how the brain topology is affected in such patients. The synchronization measure used is the nonlinear fuzzy synchronization likelihood (FSL) developed by the authors recently. Recent evidence indicates a normal neocortex has a small-world (SW) network with a balance between local structure and global structure characteristics. Such a network results in optimal balance between segregation and integration which is essential for high synchronizabilty and fast information transmission in a complex network. The SW network is characterized by the coexistence of dense clustering of connections (C) and short path lengths (L) among the network units. The results of investigation of C show the local structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is recognizable in the delta electroencephalograph (EEG) sub-band. Also, the results of investigation for L show the global structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is observable in the delta EEG sub-band. It is concluded that the changes in left-hemisphere brain's structure of ADHD from that of the non-ADHD are so much that L and C can distinguish the ADHD brain from the non-ADHD brain in the delta EEG sub-band. PMID:22423545
PTN Bearer Network Synchronization Transmission Scheme%PTN承载网同步传送方案
Institute of Scientific and Technical Information of China (English)
牛耕
2012-01-01
It briefly describes clock and time synchronization technologies, focuses on the PTN bearer network synchronization solution, the clock protection and planning principles.% 简要介绍了时钟和时间2种同步技术，重点阐述了PTN承载网同步技术方案、时钟保护和规划设计原则。
Random Long-Range Interaction Induced Synchronization in Coupled Networks of Inertial Ratchets
Institute of Scientific and Technical Information of China (English)
WEI Du-Qu; LUO Xiao-Shu; CHEN Hong-Bin; ZHANG Bo
2011-01-01
We investigate how the random long-range interactions affect the synchronization features in networks of inertial ratchets, where each ratchet is driven by a periodic time-dependent external force, under the influence of an asymmetric periodic potential. It is found that for a given coupling strength C, the synchronization of the coupled ratchets is induced as the fraction of random long-range interactions p increases and the ratchet networks reach full synchronization for a larger p. It is also found that the system reaches synchronization more effectively for a stronger coupling strength.%We investigate how the random long-range interactions affect the synchronization features in networks of inertial ratchets,where each ratchet is driven by a periodic time-dependent external force,under the influence of an asymmetric periodic potential.It is found that for a given coupling strength C,the synchronization of the coupled ratchets is induced as the fraction of random long-range interactions p increases and the ratchet networks reachfull synchronization for a larger p.It is also found that the system reaches synchronization more effectively for a stronger coupling strength.Transport phenomena play a crucial role in a large variety of processes in nature,from biological through physical to social systems.[1-6] In the past two decades,there have been attempts to model the transport properties of classical deterministic inertial ratchets.[7-9] For example,Mateos[10,11] found the origin of the current reversal as a bifurcation from a chaotic to a periodic regime;Vale et al.[12] and Endow et al.[13] found that the kinesin direction of motion along microtubules could be reversed by adjusting the architecture of a small domain of the protein.More interestingly,recent investigations have found that mutual connection plays a vital role in cooperative transport properties of groups of interacting elements.[14
Wang, Jin-Liang; Wu, Huai-Ning; Huang, Tingwen; Ren, Shun-Yan
2016-04-01
Two types of coupled neural networks with reaction-diffusion terms are considered in this paper. In the first one, the nodes are coupled through their states. In the second one, the nodes are coupled through the spatial diffusion terms. For the former, utilizing Lyapunov functional method and pinning control technique, we obtain some sufficient conditions to guarantee that network can realize synchronization. In addition, considering that the theoretical coupling strength required for synchronization may be much larger than the needed value, we propose an adaptive strategy to adjust the coupling strength for achieving a suitable value. For the latter, we establish a criterion for synchronization using the designed pinning controllers. It is found that the coupled reaction-diffusion neural networks with state coupling under the given linear feedback pinning controllers can realize synchronization when the coupling strength is very large, which is contrary to the coupled reaction-diffusion neural networks with spatial diffusion coupling. Moreover, a general criterion for ensuring network synchronization is derived by pinning a small fraction of nodes with adaptive feedback controllers. Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the theoretical results. PMID:25955852
Creative Cognition and Brain Network Dynamics.
Beaty, Roger E; Benedek, Mathias; Silvia, Paul J; Schacter, Daniel L
2016-02-01
Creative thinking is central to the arts, sciences, and everyday life. How does the brain produce creative thought? A series of recently published papers has begun to provide insight into this question, reporting a strikingly similar pattern of brain activity and connectivity across a range of creative tasks and domains, from divergent thinking to poetry composition to musical improvisation. This research suggests that creative thought involves dynamic interactions of large-scale brain systems, with the most compelling finding being that the default and executive control networks, which can show an antagonistic relation, tend to cooperate during creative cognition and artistic performance. These findings have implications for understanding how brain networks interact to support complex cognitive processes, particularly those involving goal-directed, self-generated thought. PMID:26553223
Functional network organization of the human brain
Power, Jonathan D.; Cohen, Alexander L.; Nelson, Steven M.; Wig, Gagan S.; Barnes, Kelly Anne; Church, Jessica A.; Vogel, Alecia C.; Laumann, Timothy O.; Miezin, Fran M.; Schlaggar, Bradley L.; Petersen, Steven E.
2011-01-01
Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional br...
Altered temporal variance and neural synchronization of spontaneous brain activity in anesthesia.
Huang, Zirui; Wang, Zhiyao; Zhang, Jianfeng; Dai, Rui; Wu, Jinsong; Li, Yuan; Liang, Weimin; Mao, Ying; Yang, Zhong; Holland, Giles; Zhang, Jun; Northoff, Georg
2014-11-01
Recent studies at the cellular and regional levels have pointed out the multifaceted importance of neural synchronization and temporal variance of neural activity. For example, neural synchronization and temporal variance has been shown by us to be altered in patients in the vegetative state (VS). This finding nonetheless leaves open the question of whether these abnormalities are specific to VS or rather more generally related to the absence of consciousness. The aim of our study was to investigate the changes of inter- and intra-regional neural synchronization and temporal variance of resting state activity in anesthetic-induced unconsciousness state. Applying an intra-subject design, we compared resting state activity in functional magnetic resonance imaging (fMRI) between awake versus anesthetized states in the same subjects. Replicating previous studies, we observed reduced functional connectivity within the default mode network (DMN) and thalamocortical network in the anesthetized state. Importantly, intra-regional synchronization as measured by regional homogeneity (ReHo) and temporal variance as measured by standard deviation (SD) of the BOLD signal were significantly reduced in especially the cortical midline regions, while increased in the lateral cortical areas in the anesthetized state. We further found significant frequency-dependent effects of SD in the thalamus, which showed abnormally high SD in Slow-5 (0.01-0.027 Hz) in the anesthetized state. Our results show for the first time of altered temporal variance of resting state activity in anesthesia. Combined with our findings in the vegetative state, these findings suggest a close relationship between temporal variance, neural synchronization and consciousness. PMID:24867379
Cross-frequency coupling in real and virtual brain networks
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 organi...
Cross-Frequency Coupling in Real and Virtual Brain Networks
Viktor Jirsa; Viktor Müller
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 organi...
L2 norm performance index of synchronization and optimal control synthesis of complex networks
Liu, Chao; Chen, Guanrong; Huang, Lin
2007-01-01
In this paper, the synchronizability problem of dynamical networks is addressed, where better synchronizability means that the network synchronizes faster with lower-overshoot. The L2 norm of the error vector e is taken as a performance index to measure this kind of synchronizability. For the equilibrium synchronization case, it is shown that there is a close relationship between the L2 norm of the error vector e and the H2 norm of the transfer function G of the linearized network about the equilibrium point. Consequently, the effect of the network coupling topology on the H2 norm of the transfer function G is analyzed. Finally, an optimal controller is designed, according to the so-called LQR problem in modern control theory, which can drive the whole network to its equilibrium point and meanwhile minimize the L2 norm of the output of the linearized network.
Resting network plasticity following brain injury.
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Toru Nakamura
Full Text Available The purpose of this study was to examine neural network properties at separate time-points during recovery from traumatic brain injury (TBI using graph theory. Whole-brain analyses of the topological properties of the fMRI signal were conducted in 6 participants at 3 months and 6 months following severe TBI. Results revealed alterations of network properties including a change in the degree distribution, reduced overall strength in connectivity, and increased "small-worldness" from 3 months to 6 months post injury. The findings here indicate that, during recovery from injury, the strength but not the number of network connections diminishes, so that over the course of recovery, the network begins to approximate what is observed in healthy adults. These are the first data examining functional connectivity in a disrupted neural system during recovery.
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. PMID:26635703
Robust adaptive synchronization of general dynamical networks with multiple delays and uncertainties
Indian Academy of Sciences (India)
LU YIMING; HE PING; MA SHU-HUA; LI GUO-ZHI; MOBAYBEN SALEH
2016-06-01
In this article, a general complex dynamical network which contains multiple delays and uncertainties is introduced, which contains time-varying coupling delays, time-varying node delay, and uncertainties of both the inner- and outer-coupling matrices. A robust adaptive synchronization scheme for these general complex networks with multiple delays and uncertainties is established and raised by employing the robust adaptive control principle and the Lyapunov stability theory. We choose some suitable adaptive synchronization controllers to ensure the robust synchronization of this dynamical network. The numerical simulations of the time-delay Lorenz chaotic system as local dynamical node are provided to observe and verify the viability and productivity of the theoretical research in this paper. Compared to the achievement of previous research, theresearch in this paper seems quite comprehensive and universal.
Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
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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.
A protocol for Self-synchronized duty-cycling in sensor networks: Generic implementation in Wiselib
Hernandez H.; Blesa M.J.; Blum C.; Baumgartner T.; Fekete S.P.; Kroller A.
2010-01-01
In this work we present a protocol for self-synchronized duty-cycling in wireless sensor networks with energy harvesting capabilities. The protocol is implemented in Wiselib, a library of generic algorithms for sensor networks. Simulations are conducted with the sensor network simulator Shawn. They are based on the specifications of real hardware known as iSense sensor nodes. The experimental results show that the proposed mechanism is able to adapt to changing energy availabilities. Moreo...
Functional connectivity and brain networks in schizophrenia
Lynall, Mary-Ellen; Bassett, Danielle S.; Kerwin, Robert; McKenna, Peter J.; Kitzbichler, Manfred; Müller, Ulrich; Bullmore, Ed
2010-01-01
Schizophrenia has often been conceived as a disorder of connectivity between components of large-scale brain networks. We tested this hypothesis by measuring aspects of both functional connectivity and functional network topology derived from resting state fMRI time series acquired at 72 cerebral regions over 17 minutes from 15 healthy volunteers (14 male, 1 female) and 12 people diagnosed with schizophrenia (10 male, 2 female). We investigated between-group differences in strength and divers...
Optimal exponential synchronization of general chaotic delayed neural networks: an LMI approach.
Liu, Meiqin
2009-09-01
This paper investigates the optimal exponential synchronization problem of general chaotic neural networks with or without time delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) technique. This general model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, and recurrent multilayer perceptrons (RMLPs) with or without delays. Using the drive-response concept, time-delay feedback controllers are designed to synchronize two identical chaotic neural networks as quickly as possible. The control design equations are shown to be a generalized eigenvalue problem (GEVP) which can be easily solved by various convex optimization algorithms to determine the optimal control law and the optimal exponential synchronization rate. Detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws. PMID:19443178
Generalized chaos synchronization of a weighted complex network with different nodes
International Nuclear Information System (INIS)
This paper proposes a method of realizing generalized chaos synchronization of a weighted complex network with different nodes. Chaotic systems with diverse structures are taken as the nodes of the complex dynamical network, the nonlinear terms of the systems are taken as coupling functions, and the relations among the nodes are built through weighted connections. The structure of the coupling functions between the connected nodes is obtained based on Lyapunov stability theory. A complex network with nodes of Lorenz system, Coullet system, Rössler system and the New system is taken as an example for simulation study and the results show that generalized chaos synchronization exists in the whole weighted complex network with different nodes when the coupling strength among the nodes is given with any weight value. The method can be used in realizing generalized chaos synchronization of a weighted complex network with different nodes. Furthermore, both the weight value of the coupling strength among the nodes and the number of the nodes have no effect on the stability of synchronization in the whole complex network. (general)
FPGA based, modular, configurable controller with fast synchronous optical network
International Nuclear Information System (INIS)
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.)
Synchronicity from Synchronized Chaos
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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”.
Kim, Sang-Yoon; Lim, Woochang
2016-07-01
We investigate the effect of network architecture on burst and spike synchronization in a directed scale-free network (SFN) of bursting neurons, evolved via two independent α- and β-processes. The α-process corresponds to a directed version of the Barabási-Albert SFN model with growth and preferential attachment, while for the β-process only preferential attachments between pre-existing nodes are made without addition of new nodes. We first consider the "pure" α-process of symmetric preferential attachment (with the same in- and out-degrees), and study emergence of burst and spike synchronization by varying the coupling strength J and the noise intensity D for a fixed attachment degree. Characterizations of burst and spike synchronization are also made by employing realistic order parameters and statistical-mechanical measures. Next, we choose appropriate values of J and D where only burst synchronization occurs, and investigate the effect of the scale-free connectivity on the burst synchronization by varying (1) the symmetric attachment degree and (2) the asymmetry parameter (representing deviation from the symmetric case) in the α-process, and (3) the occurrence probability of the β-process. In all these three cases, changes in the type and the degree of population synchronization are studied in connection with the network topology such as the degree distribution, the average path length Lp, and the betweenness centralization Bc. It is thus found that just taking into consideration Lp and Bc (affecting global communication between nodes) is not sufficient to understand emergence of population synchronization in SFNs, but in addition to them, the in-degree distribution (affecting individual dynamics) must also be considered to fully understand for the effective population synchronization. PMID:27131467
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.
Synchronization of Coupled Oscillators on Newman-Watts Small-World Networks
Institute of Scientific and Technical Information of China (English)
GUAN Jian-Yue; XU Xin-Jian; WU Zhi-Xi; WANG Ying-Hai
2006-01-01
We investigate the collection behaviour of coupled phase oscillators on Newman- Watts small-world networks in one and two dimensions. Each component of the network is assumed as an oscillator and each interacts with the others following the Kuramoto model. We then study the onset of global synchronization of phases and frequencies based on dynamic simulations and finite-size scah'ng. Both the phase and frequency synchronization are observed to emerge in the presence of a tiny fraction of shortcuts and enhanced with the increases of nearest neighbours and lattice dimensions.
Finite-time synchronization of fractional-order memristor-based neural networks with time delays.
Velmurugan, G; Rakkiyappan, R; Cao, Jinde
2016-01-01
In this paper, we consider the problem of finite-time synchronization of a class of fractional-order memristor-based neural networks (FMNNs) with time delays and investigated it potentially. By using Laplace transform, the generalized Gronwall's inequality, Mittag-Leffler functions and linear feedback control technique, some new sufficient conditions are derived to ensure the finite-time synchronization of addressing FMNNs with fractional order α:1memristor-based neural networks. Finally, three numerical examples are presented to show the effectiveness of our proposed theoretical results. PMID:26547242
Energy Technology Data Exchange (ETDEWEB)
Gong Yubing, E-mail: gongyubing09@hotmail.co [School of Physics, Ludong University, Yantai 264025 (China); Xie Yanhang; Lin Xiu; Hao Yinghang; Ma Xiaoguang [School of Physics, Ludong University, Yantai 264025 (China)
2010-12-15
Research highlights: Chemical delay and chemical coupling can tame chaotic bursting. Chemical delay-induced transitions from bursting synchronization to intermittent multiple spiking synchronizations. Chemical coupling-induced different types of delay-dependent firing transitions. - Abstract: Chemical synaptic connections are more common than electric ones in neurons, and information transmission delay is especially significant for the synapses of chemical type. In this paper, we report a phenomenon of ordering spatiotemporal chaos and synchronization transitions by the delays and coupling through chemical synapses of modified Hodgkin-Huxley (MHH) neurons on scale-free networks. As the delay {tau} is increased, the neurons exhibit transitions from bursting synchronization (BS) to intermittent multiple spiking synchronizations (SS). As the coupling g{sub syn} is increased, the neurons exhibit different types of firing transitions, depending on the values of {tau}. For a smaller {tau}, there are transitions from spatiotemporal chaotic bursting (SCB) to BS or SS; while for a larger {tau}, there are transitions from SCB to intermittent multiple SS. These findings show that the delays and coupling through chemical synapses can tame the chaotic firings and repeatedly enhance the firing synchronization of neurons, and hence could play important roles in the firing activity of the neurons on scale-free networks.
International Nuclear Information System (INIS)
Research highlights: → Chemical delay and chemical coupling can tame chaotic bursting. → Chemical delay-induced transitions from bursting synchronization to intermittent multiple spiking synchronizations. → Chemical coupling-induced different types of delay-dependent firing transitions. - Abstract: Chemical synaptic connections are more common than electric ones in neurons, and information transmission delay is especially significant for the synapses of chemical type. In this paper, we report a phenomenon of ordering spatiotemporal chaos and synchronization transitions by the delays and coupling through chemical synapses of modified Hodgkin-Huxley (MHH) neurons on scale-free networks. As the delay τ is increased, the neurons exhibit transitions from bursting synchronization (BS) to intermittent multiple spiking synchronizations (SS). As the coupling gsyn is increased, the neurons exhibit different types of firing transitions, depending on the values of τ. For a smaller τ, there are transitions from spatiotemporal chaotic bursting (SCB) to BS or SS; while for a larger τ, there are transitions from SCB to intermittent multiple SS. These findings show that the delays and coupling through chemical synapses can tame the chaotic firings and repeatedly enhance the firing synchronization of neurons, and hence could play important roles in the firing activity of the neurons on scale-free networks.
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.
Network coordination and synchronization in a noisy environment with time delays
Hunt, D.; Szymanski, B. K.; Korniss, G.
2012-11-01
We study the effects of nonzero time delays in stochastic synchronization problems with linear couplings in complex networks. We consider two types of time delays: transmission delays between interacting nodes and local delays at each node (due to processing, cognitive, or execution delays). By investigating the underlying fluctuations for several delay schemes, we obtain the synchronizability threshold (phase boundary) and the scaling behavior of the width of the synchronization landscape, in some cases for arbitrary networks and in others for specific weighted networks. Numerical computations allow the behavior of these networks to be explored when direct analytical results are not available. We comment on the implications of these findings for simple locally or globally weighted network couplings and possible trade-offs present in such systems.
Brain network adaptability across task states.
Directory of Open Access Journals (Sweden)
Elizabeth N Davison
2015-01-01
Full Text Available Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific and across (task-general brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
Global impulsive exponential synchronization of stochastic perturbed chaotic delayed neural networks
Institute of Scientific and Technical Information of China (English)
Zhang Hua-Guang; Ma Tie-Dong; Fu Jie; Tong Shao-Cheng
2009-01-01
In this paper,the global impulsive exponential synchronization problem of a class of chaotic delayed neural networks (DNNs) with stochastic perturbation is studied. Based on the Lyapunov stability theory,stochastic analysis approach and an efficient impulsive delay differential inequality,some new exponential synchronization criteria expressed in the form of the linear matrix inequality (LMI) are derived. The designed impulsive controUer not only can globally exponentially stabilize the error dynamics in mean square,but also can control the exponential synchronization rate. Furthermore,to estimate the stable region of the synchronization error dynamics,a novel optimization control algorithm is proposed,which can deal with the minimum problem with two nonlinear terms coexisting in LMIs effectively.Simulation results finally demonstrate the effectiveness of the proposed method.
Changes in cognitive state alter human functional brain networks
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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.
The frustrated brain: from dynamics on motifs to communities and networks.
Gollo, Leonardo L; Breakspear, Michael
2014-10-01
Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems-resonance pairs-promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes. PMID:25180310
Hamiltonian mean field model: Effect of network structure on synchronization dynamics.
Virkar, Yogesh S; Restrepo, Juan G; Meiss, James D
2015-11-01
The Hamiltonian mean field model of coupled inertial Hamiltonian rotors is a prototype for conservative dynamics in systems with long-range interactions. We consider the case where the interactions between the rotors are governed by a network described by a weighted adjacency matrix. By studying the linear stability of the incoherent state, we find that the transition to synchrony begins when the coupling constant K is inversely proportional to the largest eigenvalue of the adjacency matrix. We derive a closed system of equations for a set of local order parameters to study the effect of network heterogeneity on the synchronization of the rotors. When K is just beyond the transition to synchronization, we find that the degree of synchronization is highly dependent on the network's heterogeneity, but that for large K the degree of synchronization is robust to changes in the degree distribution. Our results are illustrated with numerical simulations on Erdös-Renyi networks and networks with power-law degree distributions. PMID:26651739
Abnormal Brain Default-Mode Network Functional Connectivity in Drug Addicts
Ma, Ning; Liu, Ying; Fu, Xian-ming; Li, Nan; Wang, Chang-Xin; Zhang, Hao; Qian, Ruo-Bing; Xu, Hu-Sheng; Hu, Xiaoping; Zhang, Da-Ren
2011-01-01
Background The default mode network (DMN) is a set of brain regions that exhibit synchronized low frequency oscillations at resting-state, and is believed to be relevant to attention and self-monitoring. As the anterior cingulate cortex and hippocampus are impaired in drug addiction and meanwhile are parts of the DMN, the present study examined addiction-related alteration of functional connectivity of the DMN. Methodology Resting-state functional magnetic resonance imaging data of chronic he...
Lag Synchronization Between Two Coupled Networks via Open-Plus-Closed-Loop and Adaptive Controls
Hu, Tong-Chun; Wu, Yong-Qing; Li, Shi-Xing
2016-01-01
In this paper, we study lag synchronization between two coupled networks and apply two types of control schemes, including the open-plus-closed-loop (OPCL) and adaptive controls. We then design the corresponding control algorithms according to the OPCL and adaptive feedback schemes. With the designed controllers, we obtain two theorems on the lag synchronization based on Lyapunov stability theory and Barbalat's lemma. Finally we provide numerical examples to show the effectiveness of the obtained controllers and see that the adaptive control is stronger than the OPCL control when realizing the lag synchronization between two coupled networks with different coupling structures. Supported by the National Natural Science Foundation of China under Grant No. 61304173, Foundation of Liaoning Educational Committee (No. 13-1069) and Hangzhou Polytechnic (No. KZYZ-2009-2)
Distributed Time Synchronization in Wireless Sensor Networks with Coupled Discrete-Time Oscillators
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Spagnolini U
2007-01-01
Full Text Available In wireless sensor networks, distributed timing synchronization based on pulse-coupled oscillators at the physical layer is currently being investigated as an interesting alternative to packet synchronization. In this paper, the convergence properties of such a system are studied through algebraic graph theory, by modeling the nodes as discrete-time clocks. A general scenario where clocks may have different free-oscillation frequencies is considered, and both time-invariant and time-variant network topologies (or fading channels are discussed. Furthermore, it is shown that the system of oscillators can be studied as a set of coupled discrete-time PLLs. Based on this observation, a generalized system design is discussed, and it is proved that known results in the context of conventional PLLs for carrier acquisition have a counterpart in distributed systems. Finally, practical details of the implementation of the distributed synchronization algorithm over a bandlimited noisy channel are covered.
Distributed Time Synchronization in Wireless Sensor Networks with Coupled Discrete-Time Oscillators
Directory of Open Access Journals (Sweden)
O. Simeone
2007-06-01
Full Text Available In wireless sensor networks, distributed timing synchronization based on pulse-coupled oscillators at the physical layer is currently being investigated as an interesting alternative to packet synchronization. In this paper, the convergence properties of such a system are studied through algebraic graph theory, by modeling the nodes as discrete-time clocks. A general scenario where clocks may have different free-oscillation frequencies is considered, and both time-invariant and time-variant network topologies (or fading channels are discussed. Furthermore, it is shown that the system of oscillators can be studied as a set of coupled discrete-time PLLs. Based on this observation, a generalized system design is discussed, and it is proved that known results in the context of conventional PLLs for carrier acquisition have a counterpart in distributed systems. Finally, practical details of the implementation of the distributed synchronization algorithm over a bandlimited noisy channel are covered.
Zhai, Shidong
2016-04-01
This paper studies the collective behavior in a network of nonlinear systems with antagonistic interactions and switching topologies. The concept of modulus synchronization is introduced to characterize the case that the moduli of corresponding components of the agent (node) states reach a synchronization. The network topologies are modeled by a set of directed signed graphs. When all directed signed graphs are structurally balanced and the nonlinear system satisfies a one-sided Lipschitz condition, by using matrix measure and contraction theory, we show that modulus synchronization can be evaluated by the time average of some matrix measures. These matrices are about the second smallest eigenvalue of undirected graphs corresponding to directed signed graphs. Finally, we present two numerical examples to illustrate the effectiveness of the obtained results.
Institute of Scientific and Technical Information of China (English)
Hui LI; Sheng-cai HOU; Bin HU; Tong LI; Yang Wang; Jin-bai Miao; Bin You; Yi-li Fu
2009-01-01
Objective: The aim of this study is to compare the effectiveness of surgery with stereotactic radiosurgery (SRS) for patients with a single synchronous brain metastasis from successfully treated non-small cell lung cancer.Methods: Between 1995 and 2002, 53 patients underwent resection of both primary non-small cell lung cancer and the associated single brain metastasis. There were 33 men and 20 women with a mean age of 57 years (range, 32(85 years). At the time of diagnosis, 42 patients experienced lung cancer related symptoms, whereas 11 patients experienced brain metastases-related symptoms. 42 patients had received thoracic surgery first, and 11 patients had undergone neurosurgery or radiosurgery first. Pneumonectomy was performed in 9 out of 42 patients (21.4%), lobectomies in 30 (71.4%), and wedge resection in 3 (7.2%). 48 patients (90.5%) underwent complete lymphadenectomy. 35 patients underwent brain metastasectomy. 18 underwent SRS.Results: There was no postoperative mortality and severe complications after either lung or brain surgery. Histology showed 34 adenocarcinomas, 16 squamous cell carcinomas, and 3 large cell lung cancers. 15 patients (28.3%) had no evidence of lymph node metastases (N0), 20 patients (37.7%) had hilar metastases (N1), and 18 patients (34%) had mediastinal metastases (N2). The 1-, 2-, 3- and 5-year overall survival rates were 49%, 19%, 10%, and 5%, respectively. The corresponding data for neurosurgery group were 55%, 17%, 11%, and 6%, respectively. The median survival time was 13 months. For SRS group the corresponding data were 44.8%, 20.9% 10.5%, and 2%, respectively. The median survival time was 14 months. The differences between the two groups were not significant (P>0.05). In lymph node negative patients (N0), the overall 5-year survival rate was 10%, as compared with a 1% survival rate in patients with lymph node metastases (N1(2). The difference was significant (P0.05).Conclusion: Although the overall survival rate for
Network effects of deep brain stimulation.
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. PMID:26269552
Hierarchical modularity in human brain functional networks
Directory of Open Access Journals (Sweden)
Renaud Lambiotte
2009-10-01
Full Text Available 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 the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.
Collective frequency variation in network synchronization and reverse PageRank
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.
Cluster synchronization in a network of non-identical dynamic systems
International Nuclear Information System (INIS)
Dynamic systems is studied in this paper, using two-cluster synchronization for detailed analysis and discussion. The results show that the common intercluster coupling condition is not always needed for the diffusively coupled network. Several sufficient conditions are obtained by using the Schur unitary triangularization theorem, which extends previous results. Some numerical examples are presented for illustration. (general)
Finite-time analysis of global projective synchronization on coloured networks
Indian Academy of Sciences (India)
Cai Guoliang; Jiang Shengqin; Cai Shuiming; Tian Lixin
2016-03-01
A novel finite-time analysis is given to investigate the global projective synchronization on coloured networks. Some less conservative conditions are derived by utilizing finite-time control techniques and Lyapunov stability theorem. In addition, two illustrative numerical simulations are provided to verify the effectiveness of the proposed theoretical results.
INFORMATION BASED METAPHORS: FROM BRAIN TO NETWORK
Directory of Open Access Journals (Sweden)
Marcos Cortez Campomar
2009-10-01
Full Text Available Metaphoric language has grown in popularity over the last years. Once simple figure of speech, metaphors were transformed into a respectable approach for organizational analysis. The brain metaphor constitutes an attractive system of ideas for studying organizational phenomena. In this paper, it is used the brain metaphor as a point of departure in developing another informational metaphor: the network metaphor. It is suggested that the latter might provide a better perspective for studying contemporary organizations in this age of information and telecommunications.
International Nuclear Information System (INIS)
Based on the comparison theorem for the stability of impulsive control system, adaptive-impulsive synchronization in drive-response networks of continuous systems with time-delay and non-time-delay is investigated. And the continuous control input, the simple updated laws and a linear impulsive controller are proposed. Moreover, two numerical examples are presented to verify the effectiveness and correctness of the theorem, using the energy resource system and Lue's system as the nodes of the networks.
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...
Sun, Mei; Zeng, Changyan; Tao, Yangwei; Tian, Lixin
2009-08-01
Based on the comparison theorem for the stability of impulsive control system, adaptive-impulsive synchronization in drive-response networks of continuous systems with time-delay and non-time-delay is investigated. And the continuous control input, the simple updated laws and a linear impulsive controller are proposed. Moreover, two numerical examples are presented to verify the effectiveness and correctness of the theorem, using the energy resource system and Lü's system as the nodes of the networks.
Synchronization and Transient Stability in Power Networks and Non-Uniform Kuramoto Oscillators
Dorfler, Florian; Bullo, Francesco
2009-01-01
Motivated by recent interest for multi-agent systems and smart power grid architectures, we discuss the synchronization problem for the network-reduced model of a power system with non-trivial transfer conductances. Our key insight is to exploit the relationship between the power network model and a first-order model of coupled oscillators. Assuming overdamped generators (possibly due to local excitation controllers), a singular perturbation analysis shows the equivalence between the classic ...
Bezzo, Nicola; Davalos, Patricio J. Cruz; Sorrentino, Francesco; Fierro, Rafael
2013-01-01
In this paper we propose an application of adaptive synchronization of chaos to detect changes in the topology of a mobile robotic network. We assume that the network may evolve in time due to the relative motion of the mobile robots and due to unknown environmental conditions, such as the presence of obstacles in the environment. We consider that each robotic agent is equipped with a chaotic oscillator whose state is propagated to the other robots through wireless communication, with the goa...
Chaotic, informational and synchronous behaviour of multiplex networks
Baptista, M. S.; Szmoski, R. M.; Pereira, R. F.; Pinto, S. E. De Souza
2016-03-01
The understanding of the relationship between topology and behaviour in interconnected networks would allow to charac- terise and predict behaviour in many real complex networks since both are usually not simultaneously known. Most previous studies have focused on the relationship between topology and synchronisation. In this work, we provide analytical formulas that shows how topology drives complex behaviour: chaos, information, and weak or strong synchronisation; in multiplex net- works with constant Jacobian. We also study this relationship numerically in multiplex networks of Hindmarsh-Rose neurons. Whereas behaviour in the analytically tractable network is a direct but not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may strongly depend on the break of symmetry in the topology of interconnections, in Hindmarsh-Rose neural networks the nonlinear nature of the chemical synapses breaks the elegant mathematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of the network, creating networks whose behaviour strongly depends on the nature (chemical or electrical) of the inter synapses.
Approach of Complex Networks for the Determination of Brain Death
Institute of Scientific and Technical Information of China (English)
SUN Wei-Gang; CAO Jian-Ting; WANG Ru-Bin
2011-01-01
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 Sndings might provide valuable insights on the determination of brain death.%@@ 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 axe 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.
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.
Flexible brain network reconfiguration supporting inhibitory control.
Spielberg, Jeffrey M; Miller, Gregory A; Heller, Wendy; Banich, Marie T
2015-08-11
The ability to inhibit distracting stimuli from interfering with goal-directed behavior is crucial for success in most spheres of life. Despite an abundance of studies examining regional brain activation, knowledge of the brain networks involved in inhibitory control remains quite limited. To address this critical gap, we applied graph theory tools to functional magnetic resonance imaging data collected while a large sample of adults (n = 101) performed a color-word Stroop task. Higher demand for inhibitory control was associated with restructuring of the global network into a configuration that was more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). In addition, there were regional changes with right inferior frontal sulcus and right anterior insula occupying more central positions as network hubs, and dorsal anterior cingulate cortex becoming more tightly coupled with its regional subnetwork. Given the crucial role of inhibitory control in goal-directed behavior, present findings identifying functional network organization supporting inhibitory control have the potential to provide additional insights into how inhibitory control may break down in a wide variety of individuals with neurological or psychiatric difficulties. PMID:26216985
Are networks with more edges easier to synchronize,or not?
Institute of Scientific and Technical Information of China (English)
Duan Zhi-Sheng; Wang Wen-Xu; Liu Chao; Chen Guan-Rong
2009-01-01
In this paper,the relationship between network synchronizability and the edge-addition of its associated graph is investigated.First,it is shown that adding one edge to a cycle definitely decreases the network synchronizability.Then,since sometimes the synchronizability can be enhanced by changing the network structure,the question of whether the networks with more edges are easier to synchronize is addressed.Based on a subgraph and complementary graph method,it is shown by examples that the answer is negative even if the network structure is arbitrarily optimized.This reveals that generally there are redundant edges in a network,which not only make no contributions to synchronization but actually may reduce the synchronizability.Moreover,a simple example shows that the node betweennees centrality is not always a good indicator for the network synchronizability.Finally,some more examples are presented to illustrate how the network synchronizability varies following the addition of edges,where all the examples show that the network synchronizability globally increases but locally fluctuates as the number of added edges increases.
Inferring topologies via driving-based generalized synchronization of two-layer networks
Wang, Yingfei; Wu, Xiaoqun; Feng, Hui; Lu, Jun-an; Xu, Yuhua
2016-05-01
The interaction topology among the constituents of a complex network plays a crucial role in the network’s evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delays are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.
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.
Time signal distribution in communication networks based on synchronous digital hierarchy
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.
Traffic signal synchronization in the saturated high-density grid road network.
Hu, Xiaojian; Lu, Jian; Wang, Wei; Zhirui, Ye
2015-01-01
Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN. PMID:25663835
Institute of Scientific and Technical Information of China (English)
王宝英; 龚玉兵
2015-01-01
We numerically study the effect of the channel noise on the spiking synchronization of a scale-free Hodgkin–Huxley neuron network with time delays. It is found that the time delay can induce synchronization transitions at an intermediate and proper channel noise intensity, and the synchronization transitions become strongest when the channel noise intensity is optimal. The neurons can also exhibit synchronization transitions as the channel noise intensity is varied, and this phenomenon is enhanced at around the time delays that can induce the synchronization transitions. It is also found that the synchronization transitions induced by the channel noise are dependent on the coupling strength and the network average degree, and there is an optimal coupling strength or network average degree with which the synchronization transitions become strongest. These results show that by inducing synchronization transitions, the channel noise has a big regulation effect on the synchronization of the neuronal network. These findings could find potential implications for the information transmission in neural systems.
Stability and synchronization of memristor-based fractional-order delayed neural networks.
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. PMID:26282374
Synchronization of Kuramoto model with time-varying forcing in complex networks
Directory of Open Access Journals (Sweden)
T. Malakoutikhah
2014-03-01
Full Text Available In this paper, a generalization of the Kuramoto model is introduced by explicit consideration of deterministically time-varying periodic external force. In this model, the oscillator's natural frequencies and amplitude of collective oscillations are influenced by external forces with constant or random strengths. Then, the synchronization behavior of forced Kuramoto model is studied in some complex networks. In the model, a new collective dynamics behavior is observed, i.e., all the oscillators are synchronized at one second, and after a little time, all of them become unsynchronized. Also, a distinct behavior of small-world networks is observed. When the strength of external force has a bimodal distribution, a resonant behavior in small-world network is seen.
Synchronization of spatiotemporal chaos in a class of complex dynamical networks
International Nuclear Information System (INIS)
This paper studies the synchronization of complex dynamical networks constructed by spatiotemporal chaotic systems with unknown parameters. The state variables in the systems with uncertain parameters are used to construct the parameter recognizers, and the unknown parameters are identified. Uncertain spatiotemporal chaotic systems are taken as the nodes of complex dynamical networks, connection among the nodes of all the spatiotemporal chaotic systems is of nonlinear coupling. The structure of the coupling functions between the connected nodes and the control gain are obtained based on Lyapunov stability theory. It is seen that stable chaos synchronization exists in the whole network when the control gain is in a certain range. The Gray—Scott models which have spatiotemporal chaotic behaviour are taken as examples for simulation and the results show that the method is very effective. (general)
RBF neural network based $\\mathcal{H}_{\\infty}$ synchronization for unknown chaotic systems
Indian Academy of Sciences (India)
Choon Ki Ahn
2010-08-01
In this paper, we propose a new $\\mathcal{H}_{\\infty}$ synchronization strategy, called a Radial Basis Function Neural Network $\\mathcal{H}_{\\infty}$ synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the proposed framework, a radial basis function neural network (RBFNN) is constructed as an alternative to approximate the unknown nonlinear function of the chaotic system. 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 $\\mathcal{H}_{\\infty}$ norm constraint. It is shown that ﬁnding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed RBFNNHS scheme.
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.
Dorfler, Florian
2009-01-01
In the current discussion about the future smart power grid one of the major problems is that of transient stability, which is the power system's ability to maintain synchronism in the presence of transient disturbances. This paper proposes a novel network-based approach to this problem resulting in concise and purely algebraic conditions that relate transient stability of a power network to the underlying network parameters and state. Our technical approach considers a network-reduction model of a power system with non-trivial transfer conductances and bridges the gap from transient stability analysis to an apparently related problem in physics, the synchronization of coupled Kuramoto oscillators. In a singular perturbation analysis it is shown that transient stability analysis of a power network model reduces on a long time-scale to the problem of synchronizing non-uniform Kuramoto oscillators with multiple time constants, non-homogeneous coupling, and non-uniform phase-shifts. The analysis of the later syn...
Time Synchronization In ANT Wireless Low Power Sensor Network
Sheriff, Nathirulla
2011-01-01
Short range wireless data communication networks that are used for sport and health care are sometimes called Wireless Body Area Networks (WBANs) and they are located more or less on a person. Sole Integrated Gait Sensor (SIGS) is a research project in WBAN, where wireless pressure sensors are placed like soles in the shoes of persons with different kinds of deceases. The sensors can measure the pressure of the foot relative to the shoe i.e. the load of the two legs is measured. This informat...
Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?
Jalili, Mahdi
2016-07-01
The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.
Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?
Jalili, Mahdi
2016-01-01
The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer's Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals. PMID:27417262
The Virtual Brain: a simulator of primate brain network dynamics.
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. PMID:23781198
Graph analysis of spontaneous brain network using EEG source connectivity
Kabbara, Aya; Falou, Wassim El; Khalil, Mohamad; Wendling, Fabrice; Hassan, Mahmoud
2016-01-01
Exploring the human brain networks during rest is a topic of great interest. Several structural and functional studies have previously been conducted to study the intrinsic brain networks. In this paper, we focus on investigating the human brain network topology using dense Electroencephalography (EEG) source connectivity approach. We applied graph theoretical methods on functional networks reconstructed from resting state data acquired using EEG in 14 healthy subjects. Our findings confirmed...
Directory of Open Access Journals (Sweden)
Yanwei Chen
2010-01-01
Full Text Available Firefighting is a dangerous profession, which often faces complicate, variable, and uncertain situation. Only depending on conventional tools, can not the firefighters cooperatively work effectively. We present a mobile fuzzy decision support application solutions for firefighter cooperation in ad hoc networks based on the decision support architecture proposed by William J. O’Brien. The application solutions, involve path navigation, danger reminder, rescue cooperation, and extinguishing cooperation. By these solutions, the firefighters can obtain complete situation information of the fire scene. To realize the application solutions, we discuss two main issues, map synchronization and alternative optimization. For an effective map synchronization scheme, we present synchronization table strategy. For alternative optimization we give a selection process based on triangular fuzzy set. The realization of the decision support applications, and the interfaces’ patterns are discussed in the end.
Synchronization transition of identical phase oscillators in a directed small-world network.
Tönjes, Ralf; Masuda, Naoki; Kori, Hiroshi
2010-09-01
We numerically study a directed small-world network consisting of attractively coupled, identical phase oscillators. While complete synchronization is always stable, it is not always reachable from random initial conditions. Depending on the shortcut density and on the asymmetry of the phase coupling function, there exists a regime of persistent chaotic dynamics. By increasing the density of shortcuts or decreasing the asymmetry of the phase coupling function, we observe a discontinuous transition in the ability of the system to synchronize. Using a control technique, we identify the bifurcation scenario of the order parameter. We also discuss the relation between dynamics and topology and remark on the similarity of the synchronization transition to directed percolation. PMID:20887048
A Protocol for Self-Synchronized Duty-Cycling in Sensor Networks: Generic Implementation in Wiselib
Hernández, Hugo; Blesa, Maria J; Blum, Christian; Kröller, Alexander; Fekete, Sandor P
2010-01-01
In this work we present a protocol for self-synchronized duty-cycling in wireless sensor networks with energy harvesting capabilities. The protocol is implemented in Wiselib, a library of generic algorithms for sensor networks. Simulations are conducted with the sensor network simulator Shawn. They are based on the specifications of real hardware known as iSense sensor nodes. The experimental results show that the proposed mechanism is able to adapt to changing energy availabilities. Moreover, it is shown that the system is very robust against packet loss.
The Union of Shortest Path Trees of Functional Brain Networks
Meier, J.; Tewarie, P.; Van Mieghem, P.
2015-01-01
Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major
Hamilton, Benjamin Russell
In this work, we investigate the application of Bayesian filtering techniques such as Kalman Filtering and Particle filtering to the problems of network time synchronization, self-localization and radio-frequency (RF) tomography in wireless networks. Networks of large numbers of small, cheap, mobile wireless devices have shown enormous potential in applications ranging from intrusion detection to environmental monitoring. These applications require the devices to have accurate time and position estimates, however traditional techniques may not be available. Additionally RF tomography offers a new paradigm to sense the network environment and could greatly enhance existing network capabilities. While there are some existing works addressing these problems, they all suffer from limitations. Current time synchronization methods are not energy efficient on small wireless devices with low quality oscillators. Existing localization methods do not consider additional sources of information available to nodes in the network such as measurements from accelerometers or models of the shadowing environment in the network. RF tomography has only been examined briefly in such networks, and current algorithms can not handle node mobility and rely on shadowing models that have not been experimentally verified. We address the time synchronization problem by analyzing the characteristics of the clocks in small wireless devices, developing a model for it, and then applying a Kalman filter to track both clock offset and skew. In our investigation into RF tomography, we present a method using a Kalman filter which jointly estimates and tracks static and dynamic objects in the environment. We also use channel measurements collected from a field test of our RF tomography testbed to compare RF shadowing models. For the localization problem, we present two algorithms incorporating additional information for improved localization: one based on a distributed extended Kalman filter that
International Nuclear Information System (INIS)
Study of hypersynchronous activity is of prime importance for combating epilepsy. Studies on network structure typically reconstruct the network by measuring various aspects of the interaction between neurons and subsequently measure the properties of the reconstructed network. In sub-sampled networks such methods lead to significant errors in reconstruction. Using rat hippocampal neurons cultured on a multi-electrode array dish and a glutamate injury model of epilepsy in vitro, we studied synchronous activity in neuronal networks. Using the first spike latencies in various neurons during a network burst, we extract various recurring spatio-temporal onset patterns in the networks. Comparing the patterns seen in control and injured networks, we observe that injured networks express a wide diversity in their foci (origin) and activation pattern, while control networks show limited diversity. Furthermore, we note that onset patterns in glutamate injured networks show a positive correlation between synchronization delay and physical distance between neurons, while control networks do not. (paper)
Wang, Qi; Gong, Yubing; Xie, Huijuan
2016-05-01
In neural networks, there exist both synaptic delays among different neurons and autaptic self-feedback delays in a neuron itself. In this paper, we study synchronization transitions induced by synaptic and autaptic delays in scale-free neuron networks, mainly exploring how these two time delays affect synchronization transitions induced by each other. It is found that the synchronization transitions induced by synaptic (autaptic) delay are intermittently enhanced when autaptic (synaptic) delay is varied. There are optimal autaptic strength and synaptic coupling strength by which the synchronization transitions induced by autaptic and synaptic delays become strongest. The underlying mechanisms are briefly discussed in terms of the relationships of autaptic delay, synaptic delay, and inter-burst interval. These results show that synaptic and autaptic delays could contribute to each other and enhance synchronization transitions in the neuronal networks. This implies that autaptic and synaptic delays could play a vital role for the information transmission in neural systems.
Spectral properties of the temporal evolution of brain network structure.
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. PMID:26723151
International Nuclear Information System (INIS)
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.
Institute of Scientific and Technical Information of China (English)
Wu Wei; Cui Bao-Tong
2007-01-01
In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented.This class of chaotic neural networks covers several well-known neural network, such a Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
Power law scaling in synchronization of brain signals depends on cognitive load
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. PMID:24822039
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.
Spikes Synchronization in Neural Networks with Synaptic Plasticity
Borges, Rafael R; Batista, Antonio M; Caldas, Iberê L; Borges, Fernando S; Lameu, Ewandson L
2015-01-01
In this paper, we investigated the neural spikes synchronisation in a neural network with synaptic plasticity and external perturbation. In the simulations the neural dynamics is described by the Hodgkin Huxley model considering chemical synapses (excitatory) among neurons. According to neural spikes synchronisation is expected that a perturbation produce non synchronised regimes. However, in the literature there are works showing that the combination of synaptic plasticity and external perturbation may generate synchronised regime. This article describes the effect of the synaptic plasticity on the synchronisation, where we consider a perturbation with a uniform distribution. This study is relevant to researches of neural disorders control.
Synchronization and Control of Halo-Chaos in Beam Transport Network with Small World Topology
Institute of Scientific and Technical Information of China (English)
LIU Qiang; FANG Jin-Qing; LI Yong
2007-01-01
The synchronous conditions of two kinds of the small-world (SW) network are studied.The small world topology can affect on dynamical behaviors of the beam transport network (BTN) largely,if the BTN is constructed with the SWtopology,the global linear coupling and special linear feedback can realize the synchronization control of beam halo-chaos as well as periodic state in the BTN with the SW topology,respectively.This important result can provide an effective way for the experimental study and the engineering design of the BTN in the high-current accelerator driven radioactive clean nuclear power systems,and may have potential use in prospective applications for halo-chaos secure communication.
Directory of Open Access Journals (Sweden)
Isak Shabani
2012-01-01
Full Text Available 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 control of professional staff that manages the performance of this system, has interruption to the online work. In order to continue work in such conditions, are founded adequate solutions to work in offline mode and later data synchronization in normal conditions.
Energy Technology Data Exchange (ETDEWEB)
Li Hongjie, E-mail: lhjymlly@163.com [College of Mathematics and Information Engineering, Jiaxing University, Zhejing 314001 (China)
2011-03-11
The paper investigates the cluster synchronization stability problem for a class of complex dynamical networks with stochastic nonlinearities and probabilistic interval time-varying delays. The stochastic nonlinearities are considered here to reflect more realistic dynamical behaviors of complex networks; the delay in this paper is assumed to be random and its probability distribution is known a priori. Based on the stochastic analysis techniques and the properties of the Kronecker product, delay-dependent cluster synchronization stability criteria are derived in the form of linear matrix inequalities which can be readily solved by using the LMI toolbox in MATLAB; the solvability of derived conditions depends on not only the size of the delay but also the probability of the delay taking values in some intervals. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.
Noise-induced synchronous stochastic oscillations in small scale cultured heart-cell networks
International Nuclear Information System (INIS)
This paper reports that the synchronous integer multiple oscillations of heart-cell networks or clusters are observed in the biology experiment. The behaviour of the integer multiple rhythm is a transition between super- and subthreshold oscillations, the stochastic mechanism of the transition is identified. The similar synchronized oscillations are theoretically reproduced in the stochastic network composed of heterogeneous cells whose behaviours are chosen as excitable or oscillatory states near a Hopf bifurcation point. The parameter regions of coupling strength and noise density that the complex oscillatory rhythms can be simulated are identified. The results show that the rhythm results from a simple stochastic alternating process between super- and sub-threshold oscillations. Studies on single heart cells forming these clusters reveal excitable or oscillatory state nearby a Hopf bifurcation point underpinning the stochastic alternation. In discussion, the results are related to some abnormal heartbeat rhythms such as the sinus arrest. (general)
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.
The Union of Shortest Path Trees of Functional Brain Networks.
Meier, Jil; Tewarie, Prejaas; Van Mieghem, Piet
2015-11-01
Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major role in the communication within the brain. So far, for the functional brain network, only the average length of the shortest paths has been analyzed. In this article, we propose to construct the union of shortest path trees (USPT) as a new topology for the functional brain network. The minimum spanning tree, which has been successful in a lot of recent studies to comprise important features of the functional brain network, is always included in the USPT. After interpreting the link weights of the functional brain network as communication probabilities, the USPT of this network can be uniquely defined. Using data from magnetoencephalography, we applied the USPT as a method to find differences in the network topology of multiple sclerosis patients and healthy controls. The new concept of the USPT of the functional brain network also allows interesting interpretations and may represent the highways of the brain. PMID:26027712
Task-Based Cohesive Evolution of Dynamic Brain Networks
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.
Zhilyaev, B. E.; Andreev, M. V.; Romanyuk, Ya. O.; Sergeev, A. V.; Tarady, V. K.
2007-01-01
UNIT (The Ukrainian synchronous Network of small Internet Telescopes) is a system of automated telescopes that search for simultaneous optical activity of transient objects associated with variable stars, small bodies of the Solar system, Near-Earth objects (NEOs), gamma ray bursts, etc. Their instruments are sensitive down to $M_{V} \\approx 18$ and require an average of 60 seconds to obtain the first images of the transient events after the alarm or GCN notice. Telescopes of UNIT are equippe...
Synchronized Multimedia Streaming on the iPhone Platform with Network Coding
Vingelmann, Peter; Fitzek, Frank; Pedersen, Morten Videbæk; Heide, Janus; Charaf, Hassan
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 t...
A SAT-based algorithm for finding attractors in synchronous Boolean networks.
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. PMID:21778527
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. PMID:25940565
Brain and Cognitive Reserve: Translation via Network Control Theory
Medaglia, John D.; Pasqualetti, Fabio; Hamilton, Roy H.; Thompson-Schill, Sharon L.; Bassett, Danielle S.
2016-01-01
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 approa...
Human brain networks function in connectome-specific harmonic waves
Atasoy, Selen; Donnelly, Isaac; Pearson, Joel
2016-01-01
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...
Cognitive fitness of cost-efficient brain functional networks
Bassett, Danielle S; Bullmore, Edward T.; Meyer-Lindenberg, Andreas; Apud, José A; Weinberger, Daniel R.; Coppola, Richard
2009-01-01
The human brain's capacity for cognitive function is thought to depend on coordinated activity in sparsely connected, complex networks organized over many scales of space and time. Recent work has demonstrated that human brain networks constructed from neuroimaging data have economical small-world properties that confer high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of complex brain networks functioning at differe...
Buchweitz, Augusto; Keller, Timothy A.; Meyler, Ann; Just, Marcel Adam
2011-01-01
The study used fMRI to investigate brain activation in participants who were able to listen to and successfully comprehend two people speaking at the same time (dual-tasking). The study identified brain mechanisms associated with high-level, concurrent dual-tasking, as compared to comprehending a single message. Results showed an increase in the functional connectivity among areas of the language network in the dual task. The increase in synchronization of brain activation for dual-tasking wa...
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....
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.
Brain networks: small-worlds, after all?
International Nuclear Information System (INIS)
Since its introduction, the ‘small-world’ effect has played a central role in network science, particularly in the analysis of the complex networks of the nervous system. From the cellular level to that of interconnected cortical regions, many analyses have revealed small-world properties in the networks of the brain. In this work, we revisit the quantification of small-worldness in neural graphs. We find that neural graphs fall into the ‘borderline’ regime of small-worldness, residing close to that of a random graph, especially when the degree sequence of the network is taken into account. We then apply recently introducted analytical expressions for clustering and distance measures, to study this borderline small-worldness regime. We derive theoretical bounds for the minimal and maximal small-worldness index for a given graph, and by semi-analytical means, study the small-worldness index itself. With this approach, we find that graphs with small-worldness equivalent to that observed in experimental data are dominated by their random component. These results provide the first thorough analysis suggesting that neural graphs may reside far away from the maximally small-world regime. (paper)
International Nuclear Information System (INIS)
The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat's lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure. (general)
Institute of Scientific and Technical Information of China (English)
Dai Hao; Jia Li-Xin; Zhang Yan-Bin
2012-01-01
The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper.Based on Lyapunov stability theory and Barbalat's lemma,generalized matrix projective lag synchronization criteria are derived by using the adaptive control method.Furthermore,each network can be undirected or directed,connected or disconnected,and nodes in either network may have identical or different dynamics.The proposed strategy is applicable to almost all kinds of complex networks.In addition,numerical simulation results are presented to illustrate the effectiveness of this method,showing that the synchronization speed is sensitively influenced by the adaptive law strength,the network size,and the network topological structure.
Dislocation Coupling-Induced Transition of Synchronization in Two-Layer Neuronal Networks
International Nuclear Information System (INIS)
The mutual coupling between neurons in a realistic neuronal system is much complex, and a two-layer neuronal network is designed to investigate the transition of electric activities of neurons. The Hindmarsh—Rose neuron model is used to describe the local dynamics of each neuron, and neurons in the two-layer networks are coupled in dislocated type. The coupling intensity between two-layer networks, and the coupling ratio (Pro), which defines the percentage involved in the coupling in each layer, are changed to observe the synchronization transition of collective behaviors in the two-layer networks. It is found that the two-layer networks of neurons becomes synchronized with increasing the coupling intensity and coupling ratio (Pro) beyond certain thresholds. An ordered wave in the first layer is useful to wake up the rest state in the second layer, or suppress the spatiotemporal state in the second layer under coupling by generating target wave or spiral waves. And the scheme of dislocation coupling can be used to suppress spatiotemporal chaos and excite quiescent neurons. (interdisciplinary physics and related areas of science and technology)
Master Synchronization in Physical-Layer Communications of Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Zheng Lin
2010-01-01
Full Text Available Synchronization in physical layer of wireless sensor networks is critical in restricting complexity of tag node and power consumption. Considering the master-to-tag communication (i.e., receiving signal from a master or anchor node by tag nodes, we propose a scheme on the basis of the principles of feedback control, to transfer the signal acquisition functionality from the tag receivers to the master nodes in a cluster. Furthermore, the algorithm of timing acquisition and phase adjustment do work in the master transmitter, and the tag nodes just need feedback results of the phase detection. The tag nodes do not require complicated clock or phase adjustment circuit any more or estimation in synchronization either. Thus, this master synchronization method reduces the complexity of tag nodes and power consumption. Due to the large random time delay in the wireless feedback loop, there exists the problem of stability and convergence in the acquisition. We analyze it and present a feasible scheme for the proposed master synchronization. In order to reduce acquisition time and cost in feedback, a two-step master acquisition algorithm is proposed. The acquisition performance under nonideal channel is analyzed, and further verified by simulations.
Torque vector control using neural network controller for synchronous reluctance motor
Energy Technology Data Exchange (ETDEWEB)
Feng, X. [Teco-Westinghouse Motor Co, R and D Center, Round Rock, TX (United States); Belmans, R.; Hameyer, K. [Katholieke Universiteit Leuven, Dic. ELEN, Dept. ESAT, Leuven-Heverlee (Belgium)
2000-08-01
This paper presents the torque vector control technique using a neural network controller for a synchronous reluctance motor. As the artificial neural network controller has the advantages of faster execution speed, harmonic ripple immunity and fault tolerance compared to a DSP-based controller, different multi-layer neural network controllers are designed and trained to produce a correct target vector when presented with the corresponding input vector. The trained result and calculated flops show that although the designed three layer controller with tansig, purelin and hard limit functions has more processing layers, the neuron number of each layer is less than that of other kinds of neural network controller, the requiring less flops and yielding faster execution and response. (orig.)
Institute of Scientific and Technical Information of China (English)
Li Wen-Lin; Li Shu-Feng; Li Gang
2012-01-01
The signal synchronization transmission of a spatiotemporal chaos network is investigated.The structure of the coupling function between connected nodes of the complex network and the value range of the linear term coefficient of the separated configuration in state equation of the node are obtained through constructing an appropriate Lyapunov function.Each node of the complex network is a laser spatiotemporal chaos model in which the phase-conjugate wave and the unilateral coupled map lattice are taken as a local function and a spatially extended system,respectively.The simulation results show the effectiveness of the signal synchronization transmission principle of the network.
International Nuclear Information System (INIS)
The signal synchronization transmission of a spatiotemporal chaos network is investigated. The structure of the coupling function between connected nodes of the complex network and the value range of the linear term coefficient of the separated configuration in state equation of the node are obtained through constructing an appropriate Lyapunov function. Each node of the complex network is a laser spatiotemporal chaos model in which the phase-conjugate wave and the unilateral coupled map lattice are taken as a local function and a spatially extended system, respectively. The simulation results show the effectiveness of the signal synchronization transmission principle of the network. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)
Partial synchronization in networks of non-linearly coupled oscillators: The Deserter Hubs Model
Freitas, Celso; Macau, Elbert; Pikovsky, Arkady
2015-04-01
We study the Deserter Hubs Model: a Kuramoto-like model of coupled identical phase oscillators on a network, where attractive and repulsive couplings are balanced dynamically due to nonlinearity of interactions. Under weak force, an oscillator tends to follow the phase of its neighbors, but if an oscillator is compelled to follow its peers by a sufficient large number of cohesive neighbors, then it actually starts to act in the opposite manner, i.e., in anti-phase with the majority. Analytic results yield that if the repulsion parameter is small enough in comparison with the degree of the maximum hub, then the full synchronization state is locally stable. Numerical experiments are performed to explore the model beyond this threshold, where the overall cohesion is lost. We report in detail partially synchronous dynamical regimes, like stationary phase-locking, multistability, periodic and chaotic states. Via statistical analysis of different network organizations like tree, scale-free, and random ones, we found a measure allowing one to predict relative abundance of partially synchronous stationary states in comparison to time-dependent ones.
Partial synchronization in networks of non-linearly coupled oscillators: The Deserter Hubs Model
Energy Technology Data Exchange (ETDEWEB)
Freitas, Celso, E-mail: cbnfreitas@gmail.com; Macau, Elbert, E-mail: elbert.macau@inpe.br [Associate Laboratory for Computing and Applied Mathematics - LAC, Brazilian National Institute for Space Research - INPE (Brazil); Pikovsky, Arkady, E-mail: pikovsky@uni-potsdam.de [Department of Physics and Astronomy, University of Potsdam, Germany and Department of Control Theory, Nizhni Novgorod State University, Gagarin Av. 23, 606950, Nizhni Novgorod (Russian Federation)
2015-04-15
We study the Deserter Hubs Model: a Kuramoto-like model of coupled identical phase oscillators on a network, where attractive and repulsive couplings are balanced dynamically due to nonlinearity of interactions. Under weak force, an oscillator tends to follow the phase of its neighbors, but if an oscillator is compelled to follow its peers by a sufficient large number of cohesive neighbors, then it actually starts to act in the opposite manner, i.e., in anti-phase with the majority. Analytic results yield that if the repulsion parameter is small enough in comparison with the degree of the maximum hub, then the full synchronization state is locally stable. Numerical experiments are performed to explore the model beyond this threshold, where the overall cohesion is lost. We report in detail partially synchronous dynamical regimes, like stationary phase-locking, multistability, periodic and chaotic states. Via statistical analysis of different network organizations like tree, scale-free, and random ones, we found a measure allowing one to predict relative abundance of partially synchronous stationary states in comparison to time-dependent ones.
Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling.
Directory of Open Access Journals (Sweden)
Qingyun Wang
Full Text Available This paper investigates the dependence of synchronization transitions of bursting oscillations on the information transmission delay over scale-free neuronal networks with attractive and repulsive coupling. It is shown that for both types of coupling, the delay always plays a subtle role in either promoting or impairing synchronization. In particular, depending on the inherent oscillation period of individual neurons, regions of irregular and regular propagating excitatory fronts appear intermittently as the delay increases. These delay-induced synchronization transitions are manifested as well-expressed minima in the measure for spatiotemporal synchrony. For attractive coupling, the minima appear at every integer multiple of the average oscillation period, while for the repulsive coupling, they appear at every odd multiple of the half of the average oscillation period. The obtained results are robust to the variations of the dynamics of individual neurons, the system size, and the neuronal firing type. Hence, they can be used to characterize attractively or repulsively coupled scale-free neuronal networks with delays.
Partial synchronization in networks of non-linearly coupled oscillators: The Deserter Hubs Model
International Nuclear Information System (INIS)
We study the Deserter Hubs Model: a Kuramoto-like model of coupled identical phase oscillators on a network, where attractive and repulsive couplings are balanced dynamically due to nonlinearity of interactions. Under weak force, an oscillator tends to follow the phase of its neighbors, but if an oscillator is compelled to follow its peers by a sufficient large number of cohesive neighbors, then it actually starts to act in the opposite manner, i.e., in anti-phase with the majority. Analytic results yield that if the repulsion parameter is small enough in comparison with the degree of the maximum hub, then the full synchronization state is locally stable. Numerical experiments are performed to explore the model beyond this threshold, where the overall cohesion is lost. We report in detail partially synchronous dynamical regimes, like stationary phase-locking, multistability, periodic and chaotic states. Via statistical analysis of different network organizations like tree, scale-free, and random ones, we found a measure allowing one to predict relative abundance of partially synchronous stationary states in comparison to time-dependent ones
Structural Brain Network: What is the Effect of LiFE Optimization of Whole Brain Tractography?
Qi, Shouliang; Meesters, Stephan; Nicolay, Klaas; ter Haar Romeny, Bart M.; Ossenblok, Pauly
2016-01-01
Structural brain networks constructed based on diffusion-weighted MRI (dMRI) have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE) method, and study the differences between the networks with and without LiFE optimization. For a co...
Abdurahman, Abdujelil; Jiang, Haijun; Rahman, Kaysar
2015-12-01
This paper deals with the problem of function projective synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying delays. Based on the theory of differential equations with discontinuous right-hand side, some novel criteria are obtained to realize the function projective synchronization of addressed networks by combining open loop control and linear feedback control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the considered memristor-based Cohen-Grossberg neural network. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness of the obtained results. PMID:26557930
Chen, Jianchun; Ding, Shaojie; Li, Hui; He, Guolong; Zhang, Xuejuan
2014-09-01
This paper studies the combined effect of transmission delay and channel fluctuations on population behaviors of an excitatory Erdös-Rényi neuronal network. First, it is found that the network reaches a perfect spatial temporal coherence at a suitable membrane size. Such a coherence resonance is stimulus-free and is array-enhanced. Second, the presence of transmission delay can induce intermittent changes of the population dynamics. Besides, two resonant peaks of the population firing rate are observed as delay changes: one is at τd≈7ms for all membrane areas, which reflects the resonance between the delayed interaction and the intrinsic period of channel kinetics; the other occurs when the transmission delay equals to the mean inter-spike intervals of the population firings in the absence of delay, which reflects the resonance between the delayed interaction and the firing period of the non-delayed system. Third, concerning the impact of network topology and population size, it is found that decreasing the connection probability does not change the range of transmission delay but broadens the range of synaptic coupling that supports population neurons to generate action potentials synchronously and temporally coherently. Furthermore, there exists a critical connection probability that distinguishes the population dynamics into an asynchronous and synchronous state. All the results we obtained are based on networks of size N = 500, which are shown to be robust to further increasing the population size. PMID:25273211
Synchronization in scale-free networks: The role of finite-size effects
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.
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....
Synchronization New 3D Chaotic System Using Brain Emotional Learning Based Intelligent Controller
Directory of Open Access Journals (Sweden)
Masoud Taleb Ziabari
2015-01-01
Full Text Available One of the most important phenomena of some systems is chaos which is caused by nonlinear dynamics. In this paper, the new 3 dimensional chaotic system is firstly investigated and then utilizing an intelligent controller which based on brain emotional learning (BELBIC, this new chaotic system is synchronized. The BELBIC consists of reward signal which accept positive values. Improper selection of the parameters causes an improper behavior which may cause serious problems such as instability of system. It is needed to optimize these parameters. Genetic Algorithm (GA, Cuckoo Optimization Algorithm (COA, Particle Swarm Optimization Algorithm (PSO and Imperialist Competitive Algorithm (ICA are used to compute the optimal parameters for the reward signal of BELBIC. These algorithms can select appropriate and optimal values for the parameters. These minimize the Cost Function, so the optimal values for the parameters will be founded. Selected cost function is defined to minimizing the least square errors. Cost function enforce the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method.
Han, Baohui
2016-01-01
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...
Bai H; Xu J; Yang H; Jin B; Lou Y; Wu D.; Han B.
2016-01-01
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 patient...
Noise in Naming Games, partial synchronization and community detection in social networks
Zhang, Weituo
2010-01-01
The Naming Games (NG) are agent-based models for agreement dynamics, peer pressure and herding in social networks, and protocol selection in autonomous ad-hoc sensor networks. By introducing a small noise term to the NG, the resulting Markov Chain model called Noisy Naming Games (NNG) are ergodic, in which all partial consensus states are recurrent. By using Gibbs-Markov equivalence we show how to get the NNG's stationary distribution in terms of the local specification of a related Markov Random Field (MRF). By ordering the partially-synchronized states according to their Gibbs energy, taken here to be a good measure of social tension, this method offers an enhanced method for community-detection in social interaction data. We show how the lowest Gibbs energy multi-name states separate and display the hidden community structures within a social network.
Sánchez, Alejandro D.; Izús, Gonzalo G.
2016-05-01
We study the stochastic dynamics of two electrically coupled networks of excitable FitzHugh-Nagumo cells, each of them phase-repulsively linked to form a ring able to develop noise-sustained structures. All cells are submitted to Gaussian white noises with common intensity η, while each network is forced with opposite phase by an adiabatic subthreshold harmonic signal. In terms of the nonequilibrium potential of a four-cell reduced model we have interpreted the dynamics, explained the observed activation and synchronization of the structures, and calculated the optimal η level as a function of coupling strength between networks. The values obtained from the reduced model coincide in order of magnitude with those arising from numerical simulations of the full system.
Driving-based generalized synchronization in two-layer networks via pinning control
Ning, Di; Wu, Xiaoqun; Lu, Jun-an; Lü, Jinhu
2015-11-01
Synchronization of complex networks has been extensively investigated in various fields. In the real world, one network is usually affected by another one but coexists in harmony with it, which can be regarded as another kind of synchronization—generalized synchronization (GS). In this paper, the GS in two-layer complex networks with unidirectional inter-layer coupling via pinning control is investigated based on the auxiliary-system approach. Specifically, for two-layer networks under study, one is considered as the drive network and the other is the response one. According to the auxiliary-system approach, output from the drive layer is designed as input for the response one, and an identical duplication of the response layer is constructed, which is driven by the same driving signals. A sufficient condition for achieving GS via pinning control is presented. Numerical simulations are further provided to illustrate the correctness of the theoretical results. It is also revealed that the least number of pinned nodes needed for achieving GS decreases with the increasing density of the response layer. In addition, it is found that when the intra-layer coupling strength of the response network is large, nodes with larger degrees should be selected to pin first for the purpose of achieving GS. However, when the coupling strength is small, it is more preferable to pin nodes with smaller degrees. This work provides engineers with a convenient approach to realize harmonious coexistence of various complex systems, which can further facilitate the selection of pinned systems and reduce control cost.
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.
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. PMID:26621123
Server Cache Synchronization Protocol (SCSP): component for directory-enabled networks
Costa Requena, Jose; Kantola, Raimo
1999-11-01
This paper describes and analyses a solution to the problem of data synchronization and replication for distributed entities such as directories in IP communication networks. We discuss the role of directories in the developing IP communications service infrastructure. The data replication solution we have implemented is based on the protocol specifications for the Internet, titled `Server Cache Synchronization Protocol' (SCSP). We review the requirements of using and maintaining data that is shared among many applications while the data resides in different physical locations. We give a brief description of the SCSP and discuss its implementation. We point out some possible applications for the protocol in a mixed IP/ISDN network. We also review some alternative approaches to directory services. In conclusion we propose the SCSP as a component for directory enabled networks--a concept emphasizing the key role of directories in the merging communications infrastructure. New emerging services manage large amounts of data. To facilitate the data management it is distributed over different locations following directory structures where the information is close to the customer location. The main goal is to achieve a global service accessible from everywhere, independently of the location where the user is accessing the service.
Resolving structural variability in network models and the brain.
Directory of Open Access Journals (Sweden)
Florian Klimm
2014-03-01
Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful
RIDT/Malta Neuroscience Network (MNN) Brain Campaign 2016
Kenely, Wilfred; Malta Neuroscience Network (MNN); University of Malta Research Trust (RIDT)
2015-01-01
The University of Malta Research Trust (RIDT), in collaboration with the Malta Neuroscience Network (MNN), has chosen The Brain as its main campaign for 2016. The campaign will have two parallel strands - one strand promoting brain awareness and the other a fund-raising campaign for research in brain disorders. The main objective of the fi rst strand is for the public to understand the brain and its functions and is intended to bring together scientists and the communi...
Wei, Xiang; Chen, Shihua; Lu, Jun-an; Ning, Di
2016-06-01
This paper proposes an approach to identify the topological structure and unknown system parameters of a weighted complex dynamical network with delay and noise perturbation. Based on the Barbalat-type invariance principle for stochastic differential equations, an effective adaptive feedback technique with an updated law is developed to realize generalized outer synchronization. The unknown topological structure and parameters are identified simultaneously through the established technique. The weight configuration matrix was found to be unnecessarily symmetric. Numerical examples are examined to illustrate the effectiveness of the analytical results.
Synchronous Hybrid Event- and Time-Driven Consensus in Multiagent Networks With Time Delays.
Xiao, Feng; Chen, Tongwen; Gao, Huijun
2016-05-01
This paper studies the delay robustness of a class of synchronous hybrid event- and time-driven consensus protocols in undirected networks. These protocols can ensure the system performance at reduced data-sampling rates. We consider three types of time delays in feedbacks, including one common time delay, multiple time-invariant delays, and multiple time-varying delays; and by sampled-data control techniques, we characterize the maximum allowable time delay and the event-detecting period for solving the average consensus problem in terms of the algebraic structure of interaction topologies. Simulations are given to show the effectiveness of theoretical results. PMID:26259227
Wen, Shiping; Zeng, Zhigang; Huang, Tingwen; Meng, Qinggang; Yao, Wei
2015-07-01
This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption. PMID:25594985
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.
Functional brain networks associated with eating behaviors in obesity
Bo-yong Park; Jongbum Seo; Hyunjin Park
2016-01-01
Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from...
Li, Xiao-Jian; Yang, Guang-Hong
2016-01-01
This paper is concerned with the problem of synchronization control of complex dynamical networks (CDN) subject to nonlinear couplings and uncertainties. An fuzzy logical system-based adaptive distributed controller is designed to achieve the synchronization. The asymptotic convergence of synchronization errors is analyzed by combining algebraic graph theory and Lyapunov theory. In contrast to the existing results, the proposed synchronization control method is applicable for the CDN with system uncertainties and unknown topology. Especially, the considered uncertainties are allowed to occur in the node local dynamics as well as in the interconnections of different nodes. In addition, it is shown that a unified controller design framework is derived for the CDN with or without coupling delays. Finally, simulations on a Chua's circuit network are provided to validate the effectiveness of the theoretical results. PMID:25720020
Hyper-connectivity of functional networks for brain disease diagnosis.
Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang
2016-08-01
Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help
International Nuclear Information System (INIS)
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 μs. 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
Macroscopic networks in the human brain: mapping connectivity in healthy and damaged brains
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 possibil
International Nuclear Information System (INIS)
This paper deals with the cluster exponential synchronization of a class of complex networks with hybrid coupling and time-varying delay. Through constructing an appropriate Lyapunov—Krasovskii functional and applying the theory of the Kronecker product of matrices and the linear matrix inequality (LMI) technique, several novel sufficient conditions for cluster exponential synchronization are obtained. These cluster exponential synchronization conditions adopt the bounds of both time delay and its derivative, which are less conservative. Finally, the numerical simulations are performed to show the effectiveness of the theoretical results. (general)
Botmart, T.; Niamsup, P.
2012-01-01
We investigate the local exponential synchronization for complex dynamical networks with interval time-varying delays in the dynamical nodes and the switched coupling term simultaneously. The constraint on the derivative of the time-varying delay is not required which allows the time delay to be a fast time-varying function. By using common unitary matrix for different subnetworks, the problem of synchronization is transformed into the stability analysis of some linear switched delay systems....
Complex brain networks: From topological communities to clustered dynamics
Indian Academy of Sciences (India)
Lucia Zemanová; Gorka Zamora-López; Changsong Zhou; Jürgen Kurths
2008-06-01
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 activities. We investigate synchronisation dynamics on the corticocortical network of the cat by modelling each node of the network (cortical area) with a subnetwork of interacting excitable neurons. We find that this network of networks displays clustered synchronisation behaviour and the dynamical clusters closely coincide with the topological community structures observed in the anatomical network. The correlation between the firing rate of the areas and the areal intensity is additionally examined. Our results provide insights into the relationship between the global organisation and the functional specialisation of the brain cortex.
Directory of Open Access Journals (Sweden)
Ji-De Huang
2012-06-01
Full Text Available Wireless Sensor Network (WSN technology is currently being employed in the field of civil engineering, especially in the areas of structural monitoring and construction safety. When used in construction site safety, time-synchronization within the sensor network is required to obtain important correlations between events, such as the prevention of equipment collisions. Here, we develop a highly efficient time synchronization method for an ultra-precise localization system to prevent collision accidents at construction sites. Without interfering with the operation of the entire sensor network, the developed protocol can locally synchronize several nodes within 10 ms. By measuring the time uncertainty information through the time stamping, wireless sensor nodes can be synchronized with only two transmitted packages from the beacon or specific nodes. Based on this proposed approach, 0.8 micro-second synchronization error was experimentally achieved on an IEEE 802.15.4 platform. Employing the developed method, the localization system was implemented and experimentally examined to have accuracy within several centimeters in the range of 2 m. This localization system can be used to identify the location of different construction equipment. This work demonstrates a simple and efficient approach to obtain high accuracy, low communication overhead, and robust synchronization for most WSN platforms to be used in automated monitoring at construction sites.
EEG-based research on brain functional networks in cognition.
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. PMID:26405867
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 brain ↔ brain 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 (brain ↔ brain 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
Directory of Open Access Journals (Sweden)
Esra Ruzgar
2013-04-01
Full Text Available Graph coloring is a widely used technique for allocation of time and frequency slots to nodes, for formingclusters, for constructing independent sets and dominating sets on wireless ad hoc and sensor networks. Agood coloring approach should produce low color count as possible. Besides, since the nodes of a wirelessad hoc and sensor network operate with limited bandwidth, energy and computing resources, the coloringshould be computed with few message passing and computational steps. In this paper, we provide aperformance evaluation of distributed synchronous greedy graph coloring algorithms on ad hoc and sensornetworks. We provide both theoretical and practical evaluations of distributed largest first and thedistributed version of Brelaz’s algorithm. We showed that although distributed version of Brelaz’salgorithm produces less color count, its resource consumption is worse than distributed largest firstalgorithm.
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.
Effect of the topology and delayed interactions in neuronal networks synchronization.
Pérez, Toni; Garcia, Guadalupe C; Eguíluz, Víctor M; Vicente, Raúl; Pipa, Gordon; Mirasso, Claudio
2011-01-01
As important as the intrinsic properties of an individual nervous cell stands the network of neurons in which it is embedded and by virtue of which it acquires great part of its responsiveness and functionality. In this study we have explored how the topological properties and conduction delays of several classes of neural networks affect the capacity of their constituent cells to establish well-defined temporal relations among firing of their action potentials. This ability of a population of neurons to produce and maintain a millisecond-precise coordinated firing (either evoked by external stimuli or internally generated) is central to neural codes exploiting precise spike timing for the representation and communication of information. Our results, based on extensive simulations of conductance-based type of neurons in an oscillatory regime, indicate that only certain topologies of networks allow for a coordinated firing at a local and long-range scale simultaneously. Besides network architecture, axonal conduction delays are also observed to be another important factor in the generation of coherent spiking. We report that such communication latencies not only set the phase difference between the oscillatory activity of remote neural populations but determine whether the interconnected cells can set in any coherent firing at all. In this context, we have also investigated how the balance between the network synchronizing effects and the dispersive drift caused by inhomogeneities in natural firing frequencies across neurons is resolved. Finally, we show that the observed roles of conduction delays and frequency dispersion are not particular to canonical networks but experimentally measured anatomical networks such as the macaque cortical network can display the same type of behavior. PMID:21637767
Liu, Y; Z. Wang; Liang, J.; Liu, X.
2013-01-01
This is the post-print version of the Article. The official published version can be accessed from the links below - Copyright @ 2013 IEEE. In this paper, the synchronization problem is studied for an array of N identical delayed neutral-type neural networks with Markovian jumping parameters. The coupled networks involve both the mode-dependent discrete-time delays and the mode-dependent unbounded distributed time delays. All the network parameters including the coupling matrix are also de...
International Nuclear Information System (INIS)
In this Letter, time-delay has been introduced in to split the networks, upon which a model of complex dynamical networks with multi-links has been constructed. Moreover, based on Lyapunov stability theory and some hypotheses, we achieve synchronization between two complex networks with different structures by designing effective controllers. The validity of the results was proved through numerical simulations of this Letter.
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.
Development of large-scale functional brain networks in children.
Supekar, Kaustubh; Musen, Mark; Menon, Vinod
2009-07-01
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. PMID:19621066
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.
A synchronization method for wireless acquisition systems, application to brain computer interfaces.
Foerster, M; Bonnet, S; van Langhenhove, A; Porcherot, J; Charvet, G
2013-01-01
A synchronization method for wireless acquisition systems has been developed and implemented on a wireless ECoG recording implant and on a wireless EEG recording helmet. The presented algorithm and hardware implementation allow the precise synchronization of several data streams from several sensor nodes for applications where timing is critical like in event-related potential (ERP) studies. The proposed method has been successfully applied to obtain visual evoked potentials and compared with a reference biosignal amplifier. The control over the exact sampling frequency allows reducing synchronization errors that will otherwise accumulate during a recording. The method is scalable to several sensor nodes communicating with a shared base station. PMID:24109816
International Nuclear Information System (INIS)
We consider the impulsive effect on the exponential synchronization of neural networks with leakage delay under the sampled-data feedback control. We use an appropriate Lyapunov—Krasovskii functional combined with the input delay approach and some inequality techniques to derive sufficient conditions that ensure the exponential synchronization of the delayed neural network. The conditions are formulated in terms of the leakage delay, the sampling period, and the exponential convergence rate. Numerical examples are given to demonstrate the usefulness and the effectiveness of the results. (general)
Brain network modules of meaningful and meaningless objects
Rizkallah, J.; Benquet, P.; Wendling, F; Khalil, M; Mheich, A; Dufor, O.; Hassan, M
2016-01-01
Network modularity is a key feature for efficient information processing in the human brain. This information processing is however dynamic and networks can reconfigure at very short time period, few hundreds of millisecond. This requires neuroimaging techniques with sufficient time resolution. Here we use the dense electroencephalography, EEG, source connectivity methods to identify cortical networks with excellent time resolution, in the order of millisecond. We identify functional networks...
Three-dimensional network of Drosophila brain hemisphere
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...
Small-World Propensity and Weighted Brain Networks
Muldoon, Sarah Feldt; Bridgeford, Eric W.; Bassett, Danielle S.
2016-02-01
Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.
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. PMID:21596060
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.
A permutation testing framework to compare groups of brain networks
Directory of Open Access Journals (Sweden)
Sean L Simpson
2013-11-01
Full Text Available Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.
Modeling carbachol-induced hippocampal network synchronization using hidden Markov models
Dragomir, Andrei; Akay, Yasemin M.; Akay, Metin
2010-10-01
In this work we studied the neural state transitions undergone by the hippocampal neural network using a hidden Markov model (HMM) framework. We first employed a measure based on the Lempel-Ziv (LZ) estimator to characterize the changes in the hippocampal oscillation patterns in terms of their complexity. These oscillations correspond to different modes of hippocampal network synchronization induced by the cholinergic agonist carbachol in the CA1 region of mice hippocampus. HMMs are then used to model the dynamics of the LZ-derived complexity signals as first-order Markov chains. Consequently, the signals corresponding to our oscillation recordings can be segmented into a sequence of statistically discriminated hidden states. The segmentation is used for detecting transitions in neural synchronization modes in data recorded from wild-type and triple transgenic mice models (3xTG) of Alzheimer's disease (AD). Our data suggest that transition from low-frequency (delta range) continuous oscillation mode into high-frequency (theta range) oscillation, exhibiting repeated burst-type patterns, occurs always through a mode resembling a mixture of the two patterns, continuous with burst. The relatively random patterns of oscillation during this mode may reflect the fact that the neuronal network undergoes re-organization. Further insight into the time durations of these modes (retrieved via the HMM segmentation of the LZ-derived signals) reveals that the mixed mode lasts significantly longer (p < 10-4) in 3xTG AD mice. These findings, coupled with the documented cholinergic neurotransmission deficits in the 3xTG mice model, may be highly relevant for the case of AD.
Real-time synchronization of wireless sensor network by 1-PPS signal
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.
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.
Molchanova, Svetlana M; Huupponen, Johanna; Lauri, Sari E; Taira, Tomi
2016-08-01
Direct electrical coupling between neurons through gap junctions is prominent during development, when synaptic connectivity is scarce, providing the additional intercellular connectivity. However, functional studies of gap junctions are hampered by the unspecificity of pharmacological tools available. Here we have investigated gap-junctional coupling between CA3 pyramidal cells in neonatal hippocampus and its contribution to early network activity. Four different gap junction inhibitors, including the general blocker carbenoxolone, decreased the frequency of network activity bursts in CA3 area of hippocampus of P3-6 rats, suggesting the involvement of electrical connections in the generation of spontaneous network activity. In CA3 pyramidal cells, spikelets evoked by local stimulation of stratum oriens, were inhibited by carbenoxolone, but not by inhibitors of glutamatergic and GABAergic synaptic transmission, signifying the presence of electrical connectivity through axo-axonic gap junctions. Carbenoxolone also decreased the success rate of firing antidromic action potentials in response to stimulation, and changed the pattern of spontaneous action potential firing of CA3 pyramidal cells. Altogether, these data suggest that electrical coupling of CA3 pyramidal cells contribute to the generation of the early network events in neonatal hippocampus by modulating their firing pattern and synchronization. PMID:26926429
Network-dependent modulation of brain activity during sleep
Watanabe, T.; Kan, S.; Koike, T.; Misaki, M; Konishi, S.; Miyauchi, S; Miyahsita, Y.; Masuda, N.
2014-01-01
Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy mod...
The Efficiency of a Small-World Functional Brain Network
Institute of Scientific and Technical Information of China (English)
ZHAO Qing-Bai; ZHANG Xiao-Fei; SUI Dan-Ni; ZHOU Zhi-Jin; CHEN Qi-Cai; TANG Yi-Yuan
2012-01-01
We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task.Functional brain networks are constructed by multichannel eventrelated potential data,in which the electrodes are the nodes and the functional connectivities between them are the edges.The results show that the correlation between small-world measures and reaction time is task-specific,such that in global imagery,there is a positive correlation between the clustering coefficient and reaction time,while in local imagery the average path length is positively correlated with the reaction time.This suggests that the efficiency of a functional brain network is task-dependent.%We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task. Functional brain networks are constructed by multichannel event-related potential data, in which the electrodes are the nodes and the functional connectivities between them are the edges. The results show that the correlation between small-world measures and reaction time is task-specific, such that in global imagery, there is a positive correlation between the clustering coefficient and reaction time, while in local imagery the average path length is positively correlated with the reaction time. This suggests that the efficiency of a functional brain network is task-dependent.
BRAIN TUMOR CLASSIFICATION USING NEURAL NETWORK BASED METHODS
Kalyani A. Bhawar*, Prof. Nitin K. Bhil
2016-01-01
MRI (Magnetic resonance Imaging) brain neoplasm pictures Classification may be a troublesome tasks due to the variance and complexity of tumors. This paper presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of 3 stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the options connected with tomography pictures victimization d...
Network Organization of the Huntingtin Proteomic Interactome in Mammalian Brain
Shirasaki, Dyna I; Greiner, Erin R.; Al-Ramahi, Ismael; Gray, Michelle; Boontheung, Pinmanee; Geschwind, Daniel H.; Botas, Juan; Coppola, Giovanni; Horvath, Steve; Loo, Joseph A.; Yang, X. William
2012-01-01
We used affinity-purification mass spectrometry to identify 747 candidate proteins that are complexed with Huntingtin (Htt) in distinct brain regions and ages in Huntington’s disease (HD) and wildtype mouse brains. To gain a systems-level view of the Htt interactome, we applied Weighted Gene Correlation Network Analysis (WGCNA) to the entire proteomic dataset to unveil a verifiable rank of Htt-correlated proteins and a network of Htt-interacting protein modules, with each module highlighting ...
Estimation of brain network ictogenicity predicts outcome from epilepsy surgery
Goodfellow, M.; Rummel, C.; Abela, E.; M. P. Richardson; Schindler, K.; Terry, J.R.
2016-01-01
Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal...
Functional brain networks associated with eating behaviors in obesity
Park, Bo-yong; Seo, Jongbum; Park, Hyunjin
2016-01-01
Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores. PMID:27030024
Functional brain networks associated with eating behaviors in obesity.
Park, Bo-Yong; Seo, Jongbum; Park, Hyunjin
2016-01-01
Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores. PMID:27030024
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.
The influence of noise on synchronous dynamics in a diluted neural network
International Nuclear Information System (INIS)
We study the influence of noise on the dynamics of a simple model of excitatory leaky integrate – and – fire neurons in a diluted network. The stochastic process amounts to a random walk with boundaries acting on the external current, whose average value plays the role of a control parameter identifying different dynamical phases. Above a given threshold value one observes a gaussian statistics of synchronous firing events, that changes to an asymmetric long-tail distribution below threshold. For uncorrelated noise the distribution below threshold exhibits an exponential tail for large rare events, while for strongly correlated noise the long-tail turns to a power-law. This interesting dynamical scenario is shown to persist also when short-term plasticity is introduced in the model. Synchronous firing events change to population bursts and the model with plasticity is shown to reproduce quantitatively what observed in in vitro experiments. We also discuss the persistence of this scenario in the thermodynamic limit
Pflugradt, Maik; Mann, Steffen; Tigges, Timo; Görnig, Matthias; Orglmeister, Reinhold
2016-02-01
Wearable home-monitoring devices acquiring various biosignals such as the electrocardiogram, photoplethysmogram, electromyogram, respirational activity and movements have become popular in many fields of research, medical diagnostics and commercial applications. Especially ambulatory settings introduce still unsolved challenges to the development of sensor hardware and smart signal processing approaches. This work gives a detailed insight into a novel wireless body sensor network and addresses critical aspects such as signal quality, synchronicity among multiple devices as well as the system's overall capabilities and limitations in cardiovascular monitoring. An early sign of typical cardiovascular diseases is often shown by disturbed autonomic regulations such as orthostatic intolerance. In that context, blood pressure measurements play an important role to observe abnormalities like hypo- or hypertensions. Non-invasive and unobtrusive blood pressure monitoring still poses a significant challenge, promoting alternative approaches including pulse wave velocity considerations. In the scope of this work, the presented hardware is applied to demonstrate the continuous extraction of multi modal parameters like pulse arrival time within a preliminary clinical study. A Schellong test to diagnose orthostatic hypotension which is typically based on blood pressure cuff measurements has been conducted, serving as an application that might significantly benefit from novel multi-modal measurement principles. It is further shown that the system's synchronicity is as precise as 30 μs and that the integrated analog preprocessing circuits and additional accelerometer data provide significant advantages in ambulatory measurement environments. PMID:26479338
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.
Survey of timing/synchronization of operating wideband digital communications networks
Mitchell, R. L.
1978-01-01
In order to benefit from experience gained from the synchronization of operational wideband digital networks, a survey was made of three such systems: Data Transmission Company, Western Union Telegraph Company, and the Computer Communications Group of the Trans-Canada Telephone System. The focus of the survey was on deployment and operational experience from a practical (as opposed to theoretical) viewpoint. The objective was to provide a report on the results of deployment how the systems performed, and wherein the performance differed from that predicted or intended in the design. It also attempted to determine how the various system designers would use the benefit of hindsight if they could design those same systems today.
Institute of Scientific and Technical Information of China (English)
郑方; 武健; 宋战江
2000-01-01
The previously proposed syllable-synchronous network search (SSNS) algorithm plays a very important role in the word decoding of the continuous Chinese speech recognition and achieves satisfying performance. Several related key factors that may affect the overall word decoding effect are carefully studied in this paper, including the perfecting of the vocabulary, the big-discount Turing re-estimating of the N-Gram probabilities, and the managing of the searching path buffers. Based on these discussions, corresponding approaches to improving the SSNS algorithm are proposed. Compared with the previous version of SSNS algorithm, the new version decreases the Chinese character error rate (CCER) in the word decoding by 42.1% across a database consisting of a large number of testing sentences (syllable strings).
A distributed Synchronous reservation multiple access control protocol for mobile Ad hoc networks
Institute of Scientific and Technical Information of China (English)
ZHANG Yanling; SUN Xianpu; LI Jiandong
2007-01-01
This study proposes a new multiple access control protocol named distributed synchronous reservation multiple access control protocol.in which the hidden and exposed terminal problems are solved,and the quality of service(QoS)requirements for real-time traffic are guaranteed.The protocol is founded on time division multiplex address and a different type of traffic is assigned to difierent priority,according to which a node should compete for and reserve the free slots in a different method.Moreover,there is a reservation acknowledgement process before data transmit in each reserved slot,so that the intruded terminal problem is solved.The throughput and average packets drop probability of this protocol are analyzed and simulated in a fully connected network.the results of which indicate that this protocol is efficient enough to support the real-time traffic.and it is more suitable to MANETs.
Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks
Vértes, Petra E.; Alexander-Bloch, Aaron; Bullmore, Edward T
2014-01-01
Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be...
Synchronous wearable wireless body sensor network composed of autonomous textile nodes.
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
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.
Synchronization in Scale Free networks: The role of finite size effects
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...
Optimal Brain Surgeon on Artificial Neural Networks in
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine; Høgsberg, Jan Becker
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...