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).
Analysis of a phase synchronized functional network based on the rhythm of brain activities
Institute of Scientific and Technical Information of China (English)
Li Ling; Jin Zhen-Lan; Li Bin
2011-01-01
Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram (EEG) data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta (4-7 Hz),alpha (8-13 Hz) and beta (14-30 Hz) bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coefficient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm.
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> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D
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.
Directory of Open Access Journals (Sweden)
Wassim M. Haddad
2014-07-01
Full Text Available Advances in neuroscience have been closely linked to mathematical modeling beginning with the integrate-and-fire model of Lapicque and proceeding through the modeling of the action potential by Hodgkin and Huxley to the current era. The fundamental building block of the central nervous system, the neuron, may be thought of as a dynamic element that is “excitable”, and can generate a pulse or spike whenever the electrochemical potential across the cell membrane of the neuron exceeds a threshold. A key application of nonlinear dynamical systems theory to the neurosciences is to study phenomena of the central nervous system that exhibit nearly discontinuous transitions between macroscopic states. A very challenging and clinically important problem exhibiting this phenomenon is the induction of general anesthesia. In any specific patient, the transition from consciousness to unconsciousness as the concentration of anesthetic drugs increases is very sharp, resembling a thermodynamic phase transition. This paper focuses on multistability theory for continuous and discontinuous dynamical systems having a set of multiple isolated equilibria and/or a continuum of equilibria. Multistability is the property whereby the solutions of a dynamical system can alternate between two or more mutually exclusive Lyapunov stable and convergent equilibrium states under asymptotically slowly changing inputs or system parameters. In this paper, we extend the theory of multistability to continuous, discontinuous, and stochastic nonlinear dynamical systems. In particular, Lyapunov-based tests for multistability and synchronization of dynamical systems with continuously differentiable and absolutely continuous flows are established. The results are then applied to excitatory and inhibitory biological neuronal networks to explain the underlying mechanism of action for anesthesia and consciousness from a multistable dynamical system perspective, thereby providing a
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.
Network synchronization in hippocampal neurons.
Penn, Yaron; Segal, Menahem; Moses, Elisha
2016-03-22
Oscillatory activity is widespread in dynamic neuronal networks. The main paradigm for the origin of periodicity consists of specialized pacemaking elements that synchronize and drive the rest of the network; however, other models exist. Here, we studied the spontaneous emergence of synchronized periodic bursting in a network of cultured dissociated neurons from rat hippocampus and cortex. Surprisingly, about 60% of all active neurons were self-sustained oscillators when disconnected, each with its own natural frequency. The individual neuron's tendency to oscillate and the corresponding oscillation frequency are controlled by its excitability. The single neuron intrinsic oscillations were blocked by riluzole, and are thus dependent on persistent sodium leak currents. Upon a gradual retrieval of connectivity, the synchrony evolves: Loose synchrony appears already at weak connectivity, with the oscillators converging to one common oscillation frequency, yet shifted in phase across the population. Further strengthening of the connectivity causes a reduction in the mean phase shifts until zero-lag is achieved, manifested by synchronous periodic network bursts. Interestingly, the frequency of network bursting matches the average of the intrinsic frequencies. Overall, the network behaves like other universal systems, where order emerges spontaneously by entrainment of independent rhythmic units. Although simplified with respect to circuitry in the brain, our results attribute a basic functional role for intrinsic single neuron excitability mechanisms in driving the network's activity and dynamics, contributing to our understanding of developing neural circuits.
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.
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.
Bursting synchronization in clustered neuronal networks
Institute of Scientific and Technical Information of China (English)
Yu Hai-Tao; Wang Jiang; Deng Bin; Wei Xi-Le
2013-01-01
Neuronal networks in the brain exhibit the modular (clustered) property,i.e.,they are composed of certain subnetworks with differential internal and external connectivity.We investigate bursting synchronization in a clustered neuronal network.A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons,while on the spiking time scale,they behave asynchronously.This synchronization transition can be induced by the variations of inter-and intracoupling strengths,as well as the probability of random links between different subnetworks.Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain,we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network.Simulation results show a frequency locking tongue in the driving parameter plane,where bursting synchronization is maintained,even in the presence of external driving.Hence,effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.
Synchronization in Triangled Complex Networks
Institute of Scientific and Technical Information of China (English)
LU Xin-Biao; LI Xiang; WANG Xiao-Fan
2006-01-01
Using a tunable clustering coefficient model withoutchanging the degree distribution, we investigate the effect of clustering coefficient on synchronization of networks with both unweighted and weighted couplings. For several typical categories of complex networks, the more triangles are in the networks, the worse the synchronizability of the networks is.
Synchronization in an evolving network
Singh, R K
2015-01-01
In this work we study the dynamics of Kuramoto oscillators on a stochastically evolving network whose evolution is governed by the phases of the individual oscillators and degree distribution. Synchronization is achieved after a threshold connection density is reached. This cumulative effect of topology and dynamics has many real-world implications, where synchronization in a system emerges as a collective property of its components in a self-organizing manner. The synchronous state remains stable as long as the connection density remains above the threshold value, with additional links providing resilience against network fluctuations.
Synchronization on coupled dynamical networks
Institute of Scientific and Technical Information of China (English)
ZHENG Zhi-gang; FENG Xiao-qin; AO Bin; Michael C.Cross
2006-01-01
In this paper,partial synchronization (PaS) in networks of coupled chaotic oscillator systems and synchronization in sparsely coupled spatiotemporal systems are explored.For the PaS,we reveal that the existence of PaS patterns depends on the symmetry property of the network topology,while the emergence of the PaS pattern depends crucially on the stability of the corresponding solution.An analytical criterion in judging the stability of PaS state on a given network are proposed in terms Of a comparison between the Lyapunov exponent spectrum of the PaS manifold and that of the transversal manifold.The competition and selections of the PaS patterns induced by the presence of multiple topological symmetries of the network are studied in terms of the criterion.The phase diagram in distinguishing the synchronous and the asynchronous states is given.The criterion in judging PaS is further applied to the study of synchronization of two sparsely coupled spatiotemporal chaotic systems.Different synchronization regimes are distinguished.The present study reveals the intrinsic collective bifurcation of coupled dynamical systems prior to the emergence of global synchronization.
Synchronization in complex clustered networks
Institute of Scientific and Technical Information of China (English)
HUANG Liang; LAI Ying-Cheng; Kwangho PARK; WANG Xingang; LAI Choy Heng; Robert A. GATENBY
2007-01-01
Synchronization in complex networks has been an active area of research in recent years. While much effort has been devoted to networks with the small-world and scale-free topology, structurally they are often assumed to have a single, densely connected component. Recently it has also become apparent that many networks in social, biological, and tech-nological systems are clustered, as characterized by a number (or a hierarchy) of sparsely linked clusters, each with dense and complex internal connections. Synchronization is funda-mental to the dynamics and functions of complex clustered networks, but this problem has just begun to be addressed. This paper reviews some progress in this direction by focus-ing on the interplay between the clustered topology and net-work synchronizability. In particular, there are two parame-ters characterizing a clustered network: the intra-cluster and the inter-cluster link density. Our goal is to clarify the roles of these parameters in shaping network synchronizability. By using theoretical analysis and direct numerical simulations of oscillator networks, it is demonstrated that clustered net-works with random inter-cluster links are more synchroniz-able, and synchronization can be optimized when inter-cluster and intra-cluster links match. The latter result has one coun-terintuitive implication: more links, if placed improperly, can actually lead to destruction of synchronization, even though such links tend to decrease the average network distance. It is hoped that this review will help attract attention to the fun-damental problem of clustered structures/synchronization in network science.
Outer Synchronization of Complex Networks by Impulse
Institute of Scientific and Technical Information of China (English)
孙文; 燕子宗; 陈士华; 吕金虎
2011-01-01
This paper investigates outer synchronization of complex networks, especially, outer complete synchronization and outer anti-synchronization between the driving network and the response network. Employing the impulsive control method which is uncontinuous, simple, efficient, low-cost and easy to implement in practical applications, we obtain some sufficient conditions of outer complete synchronization and outer anti-synchronization between two complex networks. Numerical simulations demonstrate the effectiveness of the proposed impulsive control scheme.
Cluster synchronization in oscillatory networks
Belykh, Vladimir N.; Osipov, Grigory V.; Petrov, Valentin S.; Suykens, Johan A. K.; Vandewalle, Joos
2008-09-01
Synchronous behavior in networks of coupled oscillators is a commonly observed phenomenon attracting a growing interest in physics, biology, communication, and other fields of science and technology. Besides global synchronization, one can also observe splitting of the full network into several clusters of mutually synchronized oscillators. In this paper, we study the conditions for such cluster partitioning into ensembles for the case of identical chaotic systems. We focus mainly on the existence and the stability of unique unconditional clusters whose rise does not depend on the origin of the other clusters. Also, conditional clusters in arrays of globally nonsymmetrically coupled identical chaotic oscillators are investigated. The design problem of organizing clusters into a given configuration is 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.
Robustness of Optimal Synchronization in Real Networks
Ravoori, Bhargava; Cohen, Adam B.; Sun, Jie; Motter, Adilson E; Murphy, Thomas E.; Roy, Rajarshi
2011-01-01
Experimental studies of synchronization properties on networks with controlled connection topology can provide powerful insights into the physics of complex networks. Here, we report experimental results on the influence of connection topology on synchronization in fiber-optic networks of chaotic optoelectronic oscillators. We find that the recently predicted non-monotonic, cusp-like synchronization landscape manifests itself in the rate of convergence to the synchronous state. We also observ...
Delay synchronization of temporal Boolean networks
Wei, Qiang; Xie, Cheng-jun; Liang, Yi; Niu, Yu-jun; Lin, Da
2016-01-01
This paper investigates the delay synchronization between two temporal Boolean networks base on semi-tensor product method, which improve complete synchronization. Necessary and sufficient conditions for delay synchronization are drawn base on algebraic expression of temporal Boolean networks. A example is presented to show the effectiveness of theoretical analysis.
Enhancing synchronization based on complex gradient networks.
Wang, Xingang; Lai, Ying-Cheng; Lai, Choy Heng
2007-05-01
The ubiquity of scale-free networks in nature and technological applications and the finding that such networks may be more difficult to synchronize than homogeneous networks pose an interesting phenomenon for study in network science. We argue and demonstrate that, in the presence of some proper gradient fields, scale-free networks can be more synchronizable than homogeneous networks. The gradient structure can in fact arise naturally in any weighted and asymmetrical networks; based on this we propose a coupling scheme that permits effective synchronous dynamics on the network. The synchronization scheme is verified by eigenvalue analysis and by direct numerical simulations using networks of nonidentical chaotic oscillators. PMID:17677146
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
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...
Synchronized Data Aggregation for Wireless Sensor Network
DEFF Research Database (Denmark)
Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee
2014-01-01
Wireless Sensor Networks (WSNs) are used for monitoring and data collection purposes. A key challenge in effective data collection is to schedule and synchronize the activities of the nodes with global clock. This paper proposes the Synchronized Data Aggregation Algorithm (SDA) using spanning tree...... mechanism. It provides network-wide time synchronization for sensor network. In the initial stage algorithm established the hierarchical structure in the network and then perform the pair - wise synchronization. SDA aggregate data with a global time scale throughout the network. The aggregated packets...
[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.
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. PMID:24387542
An Ideal Assortative Network and Synchronization
Institute of Scientific and Technical Information of China (English)
DONG Cheng-Dong; LIU Zeng-Rong
2007-01-01
This paper proposes a novel complex network with assortative property based on multi-center networks. The average path length and clustering coefficient of the network are calculated, and the impact on the network topology is investigated. A simple dynamic system established on the proposed network is used to analyze how the assortative property of the network affects synchronization.
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.
Robustness of optimal synchronization in real networks.
Ravoori, Bhargava; Cohen, Adam B; Sun, Jie; Motter, Adilson E; Murphy, Thomas E; Roy, Rajarshi
2011-07-15
Experimental studies can provide powerful insights into the physics of complex networks. Here, we report experimental results on the influence of connection topology on synchronization in fiber-optic networks of chaotic optoelectronic oscillators. We find that the recently predicted nonmonotonic, cusplike synchronization landscape manifests itself in the rate of convergence to the synchronous state. We also observe that networks with the same number of nodes, same number of links, and identical eigenvalues of the coupling matrix can exhibit fundamentally different approaches to synchronization. This previously unnoticed difference is determined by the degeneracy of associated eigenvectors in the presence of noise and mismatches encountered in real-world conditions. PMID:21838362
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
Institute of Scientific and Technical Information of China (English)
Sun Wen; Chen Zhong; Chen Shi-Hua
2012-01-01
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.
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
Minati, Ludovico; Chiesa, Pietro; Tabarelli, Davide; D'Incerti, Ludovico; Jovicich, Jorge
2015-03-01
In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D2), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.
Fitness for synchronization of network motifs
DEFF Research Database (Denmark)
Vega, Y.M.; Vázquez-Prada, M.; Pacheco, A.F.;
2004-01-01
We study the synchronization of Kuramoto's oscillators in small parts of networks known as motifs. We first report on the system dynamics for the case of a scale-free network and show the existence of a non-trivial critical point. We compute the probability that network motifs synchronize, and fi...... that the fitness for synchronization correlates well with motifs interconnectedness and structural complexity. Possible implications for present debates about network evolution in biological and other systems are discussed. © 2004 Elsevier B.V. All rights reserved....
Probing scale interaction in brain dynamics through synchronization.
Barardi, Alessandro; Malagarriga, Daniel; Sancristobal, Belén; Garcia-Ojalvo, Jordi; Pons, Antonio J
2014-10-01
The mammalian brain operates in multiple spatial scales simultaneously, ranging from the microscopic scale of single neurons through the mesoscopic scale of cortical columns, to the macroscopic scale of brain areas. These levels of description are associated with distinct temporal scales, ranging from milliseconds in the case of neurons to tens of seconds in the case of brain areas. Here, we examine theoretically how these spatial and temporal scales interact in the functioning brain, by considering the coupled behaviour of two mesoscopic neural masses (NMs) that communicate with each other through a microscopic neuronal network (NN). We use the synchronization between the two NM models as a tool to probe the interaction between the mesoscopic scales of those neural populations and the microscopic scale of the mediating NN. The two NM oscillators are taken to operate in a low-frequency regime with different peak frequencies (and distinct dynamical behaviour). The microscopic neuronal population, in turn, is described by a network of several thousand excitatory and inhibitory spiking neurons operating in a synchronous irregular regime, in which the individual neurons fire very sparsely but collectively give rise to a well-defined rhythm in the gamma range. Our results show that this NN, which operates at a fast temporal scale, is indeed sufficient to mediate coupling between the two mesoscopic oscillators, which evolve dynamically at a slower scale. We also establish how this synchronization depends on the topological properties of the microscopic NN, on its size and on its oscillation frequency.
Robustness of Optimal Synchronization in Real Networks
Ravoori, Bhargava; Sun, Jie; Motter, Adilson E; Murphy, Thomas E; Roy, Rajarshi; 10.1103/PhysRevLett.107.034102
2011-01-01
Experimental studies of synchronization properties on networks with controlled connection topology can provide powerful insights into the physics of complex networks. Here, we report experimental results on the influence of connection topology on synchronization in fiber-optic networks of chaotic optoelectronic oscillators. We find that the recently predicted non-monotonic, cusp-like synchronization landscape manifests itself in the rate of convergence to the synchronous state. We also observe that networks with the same number of nodes, same number of links, and identical eigenvalues of the coupling matrix can exhibit fundamentally different approaches to synchronization. This previously unnoticed difference is determined by the degeneracy of associated eigenvectors in the presence of noise and mismatches encountered in real-world conditions.
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
Optimal synchronization of directed complex networks
Skardal, Per Sebastian; Taylor, Dane; Sun, Jie
2016-09-01
We study optimal synchronization of networks of coupled phase oscillators. We extend previous theory for optimizing the synchronization properties of undirected networks to the important case of directed networks. We derive a generalized synchrony alignment function that encodes the interplay between the network structure and the oscillators' natural frequencies and serves as an objective measure for the network's degree of synchronization. Using the generalized synchrony alignment function, we show that a network's synchronization properties can be systematically optimized. This framework also allows us to study the properties of synchrony-optimized networks, and in particular, investigate the role of directed network properties such as nodal in- and out-degrees. For instance, we find that in optimally rewired networks, the heterogeneity of the in-degree distribution roughly matches the heterogeneity of the natural frequency distribution, but no such relationship emerges for out-degrees. We also observe that a network's synchronization properties are promoted by a strong correlation between the nodal in-degrees and the natural frequencies of oscillators, whereas the relationship between the nodal out-degrees and the natural frequencies has comparatively little effect. This result is supported by our theory, which indicates that synchronization is promoted by a strong alignment of the natural frequencies with the left singular vectors corresponding to the largest singular values of the Laplacian matrix.
Synchronization of Boolean Networks with Different Update Schemes.
Zhang, Hao; Wang, Xingyuan; Lin, Xiaohui
2014-01-01
In this paper, the synchronizations of Boolean networks with different update schemes (synchronized Boolean networks and asynchronous Boolean networks) are investigated. All nodes in Boolean network are represented in terms of semi-tensor product. First, we give the concept of inner synchronization and observe that all nodes in a Boolean network are synchronized with each other. Second, we investigate the outer synchronization between a driving Boolean network and a corresponding response Boolean network. We provide not only the concept of traditional complete synchronization, but also the anti-synchronization and get the anti-synchronization in simulation. Third, we extend the outer synchronization to asynchronous Boolean network and get the complete synchronization between an asynchronous Boolean network and a response Boolean network. Consequently, theorems for synchronization of Boolean networks and asynchronous Boolean networks are derived. Examples are provided to show the correctness of our theorems.
Time Synchronization for Mobile Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Ying Guo
2013-01-01
Full Text Available Time synchronization is very crucial for the implementation of energy constricted underwater wireless sensor networks (UWSN. The purpose of this paper is to present a time synchronization algorithm which is suitable to UWSN. Although several time synchronization protocols have been developed, most of them tend to break down when implemented on mobile underwater sensor networks. In this paper, we analyze the effect of node mobility, and propose a Mobile Counteracted Time Synchronization approach, called “Mc-Sync”, which is a novel time synchronization scheme for mobile underwater acoustic sensor networks. It makes use of two mobile reference nodes to counteract the effect of node mobility. We also analyze and design the optimized trajectories of the two mobile reference nodes in underwater environment. We show through analysis and simulation that Mc-Sync provides much better performance than existing schemes.
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
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
Experimental network synchronization via plastic optical fiber
Arellano-Delgado, A.; López-Gutiérrez, R. M.; Cruz-Hernández, C.; Posadas-Castillo, C.; Cardoza-Avendaño, L.; Serrano-Guerrero, H.
2013-03-01
In this paper, network synchronization of coupled Chua's circuits in star configuration is experimentally studied. In particular, plastic optical fiber (POF) is used in the network like communication channels among chaotic nodes to achieve synchronization. The master signal is sent to multiple slaves through a fiber optical coupler with corresponding electrical/optical and optical/electrical stages. An application to encrypted chaotic communication to transmit analogical signal and image messages to multiple receivers is also given.
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...
Davison, Elizabeth; Dey, Biswadip; Leonard, Naomi
Mathematical studies of synchronization in networks of neuronal oscillators offer insight into neuronal ensemble behavior in the brain. Systematic means to understand how network structure and external input affect synchronization in network models have the potential to improve methods for treating synchronization-related neurological disorders such as epilepsy and Parkinson's disease. To elucidate the complex relationships between network structure, external input, and synchronization, we investigate synchronous firing patterns in arbitrary networks of neuronal oscillators coupled through gap junctions with heterogeneous external inputs. We first apply a passivity-based Lyapunov analysis to undirected networks of homogeneous FitzHugh-Nagumo (FN) oscillators with homogeneous inputs and derive a sufficient condition on coupling strength that guarantees complete synchronization. In biologically relevant regimes, we employ Gronwall's inequality to obtain a bound tighter than those previously reported. We extend both analyses to a homogeneous FN network with heterogeneous inputs and show how cluster synchronization emerges under conditions on the symmetry of the coupling matrix and external inputs. Our results can be generalized to any network of semi-passive oscillators.
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.
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.
Synchronization in Oscillator Networks with Nonlinear Coupling
Institute of Scientific and Technical Information of China (English)
ZHANG Jian-Bao; LIU Zeng-Rong; LI Ying
2008-01-01
Synchronization in coupled oscillator networks has attracted much attention from many fields of science and engineering. In this paper, it is firstly proved that the oscillator network with nonlinear coupling is also eventually dissipative under the hypothesis of eventual dissipation of the uncoupled oscillators. And the dynamics of the network is analyzed in its absorbing domain by combining two methods developed recently. Sufficient conditions for synchronization in the oscillator networks with nonlinear coupling are obtained. The two methods are combined effectively and the results embody the respective merits of the two methods. Numerical simulations confirm the validity of the results.
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.
Synchronization of mobile chaotic oscillator networks
Fujiwara, Naoya; Kurths, Jürgen; Díaz-Guilera, Albert
2016-09-01
We study synchronization of systems in which agents holding chaotic oscillators move in a two-dimensional plane and interact with nearby ones forming a time dependent network. Due to the uncertainty in observing other agents' states, we assume that the interaction contains a certain amount of noise that turns out to be relevant for chaotic dynamics. We find that a synchronization transition takes place by changing a control parameter. But this transition depends on the relative dynamic scale of motion and interaction. When the topology change is slow, we observe an intermittent switching between laminar and burst states close to the transition due to small noise. This novel type of synchronization transition and intermittency can happen even when complete synchronization is linearly stable in the absence of noise. We show that the linear stability of the synchronized state is not a sufficient condition for its stability due to strong fluctuations of the transverse Lyapunov exponent associated with a slow network topology change. Since this effect can be observed within the linearized dynamics, we can expect such an effect in the temporal networks with noisy chaotic oscillators, irrespective of the details of the oscillator dynamics. When the topology change is fast, a linearized approximation describes well the dynamics towards synchrony. These results imply that the fluctuations of the finite-time transverse Lyapunov exponent should also be taken into account to estimate synchronization of the mobile contact networks.
Onset of synchronization in complex gradient networks.
Wang, Xingang; Huang, Liang; Guan, Shuguang; Lai, Ying-Cheng; Lai, Choy Heng
2008-09-01
Recently, it has been found that the synchronizability of a scale-free network can be enhanced by introducing some proper gradient in the coupling. This result has been obtained by using eigenvalue-spectrum analysis under the assumption of identical node dynamics. Here we obtain an analytic formula for the onset of synchronization by incorporating the Kuramoto model on gradient scale-free networks. Our result provides quantitative support for the enhancement of synchronization in such networks, further justifying their ubiquity in natural and in technological systems. PMID:19045491
Transient Synchronization in Complex Neuronal Networks
Costa, Luciano da Fontoura
2008-01-01
Transient synchronization in complex neuronal networks as a consequence of activation-conserved dynamics induced by having sources placed at specific neurons is investigated. The basic integrate-and-fire neuron is adopted, and the dynamics is estimated computationally so as to obtain the activation at each node along each instant of time. The dynamics is implemented so as to conserve the total activation entering the system, which is a distinctive feature of the current work. The synchronization of the activation of the network is then quantified along time in terms of its normalized instantaneous entropy. The potential of such concepts and measurements is explored with respect to 6 theoretical models, as well as for the neuronal network of \\emph{C. elegans}. A series of interesting results are obtained and discussed, including the fact that all models led to a transient period of synchronization, whose specific features depend heavily on the topological features of the networks.
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.
Forced synchronization of autonomous dynamical Boolean networks.
Rivera-Durón, R R; Campos-Cantón, E; Campos-Cantón, I; Gauthier, Daniel J
2015-08-01
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.
Paths to synchronization on complex networks.
Gómez-Gardeñes, Jesús; Moreno, Yamir; Arenas, Alex
2007-01-19
The understanding of emergent collective phenomena in natural and social systems has driven the interest of scientists from different disciplines during decades. Among these phenomena, the synchronization of a set of interacting individuals or units has been intensively studied because of its ubiquity in the natural world. In this Letter, we show how for fixed coupling strengths local patterns of synchronization emerge differently in homogeneous and heterogeneous complex networks, driving the process towards a certain global synchronization degree following different paths. The dependence of the dynamics on the coupling strength and on the topology is unveiled. This study provides a new perspective and tools to understand this emerging phenomena. PMID:17358685
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.
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.
Stochastic synchronization of genetic oscillator networks
Directory of Open Access Journals (Sweden)
Chen Luonan
2007-01-01
Full Text Available Abstract Background The study of synchronization among genetic oscillators is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. Genetic networks are intrinsically noisy due to natural random intra- and inter-cellular fluctuations. Therefore, it is important to study the effects of noise perturbation on the synchronous dynamics of genetic oscillators. From the synthetic biology viewpoint, it is also important to implement biological systems that minimizing the negative influence of the perturbations. Results In this paper, based on systems biology approach, we provide a general theoretical result on the synchronization of genetic oscillators with stochastic perturbations. By exploiting the specific properties of many genetic oscillator models, we provide an easy-verified sufficient condition for the stochastic synchronization of coupled genetic oscillators, based on the Lur'e system approach in control theory. A design principle for minimizing the influence of noise is also presented. To demonstrate the effectiveness of our theoretical results, a population of coupled repressillators is adopted as a numerical example. Conclusion In summary, we present an efficient theoretical method for analyzing the synchronization of genetic oscillator networks, which is helpful for understanding and testing the synchronization phenomena in biological organisms. Besides, the results are actually applicable to general oscillator networks.
Synchronization of Asynchronous Switched Boolean Network.
Zhang, Hao; Wang, Xingyuan; Lin, Xiaohui
2015-01-01
In this paper, the complete synchronizations for asynchronous switched Boolean network with free Boolean sequence controllers and close-loop controllers are studied. First, the basic asynchronous switched Boolean network model is provided. With the method of semi-tensor product, the Boolean dynamics is translated into linear representation. Second, necessary and sufficient conditions for ASBN synchronization with free Boolean sequence control and close-loop control are derived, respectively. Third, some illustrative examples are provided to show the efficiency of the proposed methods.
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.
Fu, Chenbo; Lin, Weijie; Huang, Liang; Wang, Xingang
2014-05-01
Synchronization transition in networks of nonlocally coupled chaotic oscillators is investigated. It is found that in reaching the state of global synchronization the networks can stay in various states of partial synchronization. The stability of the partial synchronization states is analyzed by the method of eigenvalue analysis, in which the important roles of the network topological symmetry on synchronization transition are identified. Moreover, for networks possessing multiple topological symmetries, it is found that the synchronization transition can be divided into different stages, with each stage characterized by a unique synchronous pattern of the oscillators. Synchronization transitions in networks of nonsymmetric topology and nonidentical oscillators are also investigated, where the partial synchronization states, although unstable, are found to be still playing important roles in the transitions.
The mechanism of synchronization in feed-forward neuronal networks
Energy Technology Data Exchange (ETDEWEB)
Goedeke, S; Diesmann, M [Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg (Germany)], E-mail: diesmann@brain.riken.jp
2008-01-15
Synchronization in feed-forward subnetworks of the brain has been proposed to explain the precisely timed spike patterns observed in experiments. While the attractor dynamics of these networks is now well understood, the underlying single neuron mechanisms remain unexplained. Previous attempts have captured the effects of the highly fluctuating membrane potential by relating spike intensity f(U) to the instantaneous voltage U generated by the input. This article shows that f is high during the rise and low during the decay of U(t), demonstrating that the U-dot-dependence of f, not refractoriness, is essential for synchronization. Moreover, the bifurcation scenario is quantitatively described by a simple f(U,U-dot) relationship. These findings suggest f(U,U-dot) as the relevant model class for the investigation of neural synchronization phenomena in a noisy environment.
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.
Anti-synchronization Between Coupled Networks with Two Active Forms*
Institute of Scientific and Technical Information of China (English)
WU Yong-Qing; SUN Wei-Gang; LI Shan-Shan
2011-01-01
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.
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
Directory of Open Access Journals (Sweden)
Andrey Dovzhenok
Full Text Available Suppression of excessively synchronous beta-band oscillatory activity in the brain is believed to suppress hypokinetic motor symptoms of Parkinson's disease. Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS. This type of synchrony control was shown to destabilize the synchronized state in networks of simple model oscillators as well as in networks of coupled model neurons. However, the dynamics of the neural activity in Parkinson's disease exhibits complex intermittent synchronous patterns, far from the idealized synchronous dynamics used to study the delayed feedback stimulation. This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. We employ a computational model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics. When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action. However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony. As network parameters are changed from the range which produces complete synchrony to those favoring less synchronous dynamics, desynchronizing delayed feedback may gradually turn into synchronizing stimulation. This suggests that delayed feedback DBS in Parkinson's disease may boost rather than suppress synchronization and is unlikely to be clinically successful. The study also indicates that delayed feedback stimulation may not necessarily exhibit a desynchronization effect when acting on a physiologically realistic partially synchronous dynamics, and provides an example of how to estimate the stimulation effect.
Dovzhenok, Andrey; Park, Choongseok; Worth, Robert M; Rubchinsky, Leonid L
2013-01-01
Suppression of excessively synchronous beta-band oscillatory activity in the brain is believed to suppress hypokinetic motor symptoms of Parkinson's disease. Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). This type of synchrony control was shown to destabilize the synchronized state in networks of simple model oscillators as well as in networks of coupled model neurons. However, the dynamics of the neural activity in Parkinson's disease exhibits complex intermittent synchronous patterns, far from the idealized synchronous dynamics used to study the delayed feedback stimulation. This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. We employ a computational model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics. When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action. However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony. As network parameters are changed from the range which produces complete synchrony to those favoring less synchronous dynamics, desynchronizing delayed feedback may gradually turn into synchronizing stimulation. This suggests that delayed feedback DBS in Parkinson's disease may boost rather than suppress synchronization and is unlikely to be clinically successful. The study also indicates that delayed feedback stimulation may not necessarily exhibit a desynchronization effect when acting on a physiologically realistic partially synchronous dynamics, and provides an example of how to estimate the stimulation effect.
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.
New Approach to Cluster Synchronization in Complex Dynamical Networks
Institute of Scientific and Technical Information of China (English)
LU Xin-Biao; QIN Bu-Zhi; LU Xin-Yu
2009-01-01
In this paper, a distributed control strategy is proposed to make a complex dynamical network achieve cluster synchronization, which means that nodes in the same group achieve the same synchronization state, while nodes in different groups achieve different synchronization states. The local and global stability of the cluster synchronization state are analyzed. Moreover, simulation results verify the effectiveness of the new approach
Synchronization between uncertain nonidentical networks with quantum chaotic behavior
Li, Wenlin; Li, Chong; Song, Heshan
2016-11-01
Synchronization between uncertain nonidentical networks with quantum chaotic behavior is researched. The identification laws of unknown parameters in state equations of network nodes, the adaptive laws of configuration matrix elements and outer coupling strengths are determined based on Lyapunov theorem. The conditions of realizing synchronization between uncertain nonidentical networks are discussed and obtained. Further, Jaynes-Cummings model in physics are taken as the nodes of two networks and simulation results show that the synchronization performance between networks is very stable.
Preliminary OFDM based acoustic communication for underwater sensor networks synchronization
Pallarés Valls, Oriol; Sarriá Gandul, David; Viñolo Monzoncillo, Carlos; Río Fernandez, Joaquín del; Manuel Lázaro, Antonio
2013-01-01
This work presents a first approach to wireless underwater sensor networks UWSN time synchronization, using OFDM (Orthogonal Frequency Division Multiplexing) acoustic communication and time reference served by a synchronization protocol. This synchronization and type of modulation allows getting a low drift clock on each sensor, on a high efficiency underwater communication network. Peer Reviewed
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.
The hippocampus: hub of brain network communication for memory.
F.P. Battaglia; K. Benchenane; A. Sirota; C.M.A. Pennartz; S.I. Wiener
2011-01-01
A complex brain network, centered on the hippocampus, supports episodic memories throughout their lifetimes. Classically, upon memory encoding during active behavior, hippocampal activity is dominated by theta oscillations (6-10Hz). During inactivity, hippocampal neurons burst synchronously, constit
Network structure, topology, and dynamics in generalized models of synchronization
Lerman, Kristina; Ghosh, Rumi
2012-08-01
Network structure is a product of both its topology and interactions between its nodes. We explore this claim using the paradigm of distributed synchronization in a network of coupled oscillators. As the network evolves to a global steady state, nodes synchronize in stages, revealing the network's underlying community structure. Traditional models of synchronization assume that interactions between nodes are mediated by a conservative process similar to diffusion. However, social and biological processes are often nonconservative. We propose a model of synchronization in a network of oscillators coupled via nonconservative processes. We study the dynamics of synchronization of a synthetic and real-world networks and show that the traditional and nonconservative models of synchronization reveal different structures within the same network.
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.
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.
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.
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...
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...
FPGA implementation of motifs-based neuronal network and synchronization analysis
Deng, Bin; Zhu, Zechen; Yang, Shuangming; Wei, Xile; Wang, Jiang; Yu, Haitao
2016-06-01
Motifs in complex networks play a crucial role in determining the brain functions. In this paper, 13 kinds of motifs are implemented with Field Programmable Gate Array (FPGA) to investigate the relationships between the networks properties and motifs properties. We use discretization method and pipelined architecture to construct various motifs with Hindmarsh-Rose (HR) neuron as the node model. We also build a small-world network based on these motifs and conduct the synchronization analysis of motifs as well as the constructed network. We find that the synchronization properties of motif determine that of motif-based small-world network, which demonstrates effectiveness of our proposed hardware simulation platform. By imitation of some vital nuclei in the brain to generate normal discharges, our proposed FPGA-based artificial neuronal networks have the potential to replace the injured nuclei to complete the brain function in the treatment of Parkinson's disease and epilepsy.
Network-complement transitions, symmetries, and cluster synchronization
Nishikawa, Takashi; Motter, Adilson E.
2016-09-01
Synchronization in networks of coupled oscillators is known to be largely determined by the spectral and symmetry properties of the interaction network. Here, we leverage this relation to study a class of networks for which the threshold coupling strength for global synchronization is the lowest among all networks with the same number of nodes and links. These networks, defined as being uniform, complete, and multi-partite (UCM), appear at each of an infinite sequence of network-complement transitions in a larger class of networks characterized by having near-optimal thresholds for global synchronization. We show that the distinct symmetry structure of the UCM networks, which by design are optimized for global synchronizability, often leads to formation of clusters of synchronous oscillators, and that such states can coexist with the state of global synchronization.
Optimization of noise-induced synchronization of oscillator networks
Kawamura, Yoji; Nakao, Hiroya
2016-09-01
We investigate common-noise-induced synchronization between two identical networks of coupled phase oscillators exhibiting fully locked collective oscillations. Using the collective phase description method for fully locked oscillators, we demonstrate that two noninteracting networks of coupled phase oscillators can exhibit in-phase synchronization between the networks when driven by weak common noise. We derive the Lyapunov exponent characterizing the relaxation time for synchronization and develop a method of obtaining the optimal input pattern of common noise to achieve fast synchronization. We illustrate the theory using three representative networks with heterogeneous, global, and local coupling. The theoretical results are validated by direct numerical simulations.
Impulsive Cluster Synchronization in Community Network with Nonidentical Nodes
Institute of Scientific and Technical Information of China (English)
邓丽萍; 吴召艳
2012-01-01
In this paper,cluster synchronization in community network with nonidentical nodes and impulsive effects is investigated.Community networks with two kinds of topological structure are investigated.Positive weighted network is considered first and external pinning controllers are designed for achieving cluster synchronization.Cooperative and competitive network under some assumptions is investigated as well and can achieve cluster synchronization with only impulsive controllers.Based on the stability analysis of impulsive differential equation and the Lyapunov stability theory,several simple and useful synchronization criteria are derived.Finally,numerical simulations are provided to verify the effectiveness of the derived results.
Feedback Controller Design for the Synchronization of Boolean Control Networks.
Liu, Yang; Sun, Liangjie; Lu, Jianquan; Liang, Jinling
2016-09-01
This brief investigates the partial and complete synchronization of two Boolean control networks (BCNs). Necessary and sufficient conditions for partial and complete synchronization are established by the algebraic representations of logical dynamics. An algorithm is obtained to construct the feedback controller that guarantees the synchronization of master and slave BCNs. Two biological examples are provided to illustrate the effectiveness of the obtained 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.
Introduction to Focus Issue: Patterns of Network Synchronization
Abrams, Daniel M; Motter, Adilson E
2016-01-01
The study of synchronization of coupled systems is currently undergoing a major surge fueled by recent discoveries of new forms of collective dynamics and the development of techniques to characterize a myriad of new patterns of network synchronization. This includes chimera states, phenomena determined by symmetry, remote synchronization, and asymmetry-induced synchronization. This Focus Issue presents a selection of contributions at the forefront of these developments, to which this introduction is intended to offer an up-to-date foundation.
Generalized synchronization in mutually coupled oscillators and complex networks.
Moskalenko, Olga I; Koronovskii, Alexey A; Hramov, Alexander E; Boccaletti, Stefano
2012-09-01
We introduce a concept of generalized synchronization, able to encompass the setting of collective synchronized behavior for mutually coupled systems and networking systems featuring complex topologies in their connections. The onset of the synchronous regime is confirmed by the dependence of the system's Lyapunov exponents on the coupling parameter. The presence of a generalized synchronization regime is verified by means of the nearest neighbor method.
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.
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...
Stimulus-dependent synchronization in delayed-coupled neuronal networks.
Esfahani, Zahra G; Gollo, Leonardo L; Valizadeh, Alireza
2016-03-22
Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand.
Synchronization, quantum correlations and entanglement in oscillator networks.
Manzano, Gonzalo; Galve, Fernando; Giorgi, Gian Luca; Hernández-García, Emilio; Zambrini, Roberta
2013-01-01
Synchronization is one of the paradigmatic phenomena in the study of complex systems. It has been explored theoretically and experimentally mostly to understand natural phenomena, but also in view of technological applications. Although several mechanisms and conditions for synchronous behavior in spatially extended systems and networks have been identified, the emergence of this phenomenon has been largely unexplored in quantum systems until very recently. Here we discuss synchronization in quantum networks of different harmonic oscillators relaxing towards a stationary state, being essential the form of dissipation. By local tuning of one of the oscillators, we establish the conditions for synchronous dynamics, in the whole network or in a motif. Beyond the classical regime we show that synchronization between (even unlinked) nodes witnesses the presence of quantum correlations and entanglement. Furthermore, synchronization and entanglement can be induced between two different oscillators if properly linked to a random network.
Synchronization in Complex Networks of Nonlinear Dynamical Systems
Wu, Chai Wah
2007-01-01
This book brings together two emerging research areas: synchronization in coupled nonlinear systems and complex networks, and study conditions under which a complex network of dynamical systems synchronizes. While there are many texts that study synchronization in chaotic systems or properties of complex networks, there are few texts that consider the intersection of these two very active and interdisciplinary research areas. The main theme of this book is that synchronization conditions can be related to graph theoretical properties of the underlying coupling topology. The book introduces ide
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...
Robust impulsive synchronization of linear discrete dynamical networks
Institute of Scientific and Technical Information of China (English)
Yonghong LONG; Min WU; Bin LIU
2005-01-01
This paper aims to study robust impulsive synchronization problem for uncertain linear discrete dynamical network.For the discrete dynamical networks with unknown but bounded linear coupling,by introducing the concept of uniformly positive definite matrix functions,some robust impulsive controllers are designed,which ensure that the state of a discrete dynamical network globally asymptotically synchronizes with an arbitrarily assigned state of an isolate node of the network.This paper also investigates the synchronization problem where the network coupling functions are uncertain but bounded nonlinear functions.Finally,two examples are simulated to illustrate our results.
Adaptive Synchronization in Small-World Dynamical Networks
Institute of Scientific and Technical Information of China (English)
ZOU Yan-li; ZHU Jie; LUO Xiao-shu
2007-01-01
Adaptive synchronization in NW small-world dynamical networks was studied. Firstly, an adaptive synchronization method is presented and explained. Then, it is applied to two different classes of dynamical networks,one is a class-B network, small-world connected R(o)ssler oscillators, the other is a class-A network, small-world connected Chua's circuits. The simulation verifies the validity of the presented method. It also shows that the adaptive synchronization method is robust to the variations of the node systems parameters. So the presented method can be used in networks whose node systems have unknown or time-varying parameters.
Time synchronization in ad-hoc wireless sensor networks
Sharma, Nishant
2013-06-01
Advances in micro-electronics and developments in the various technologies have given birth to this era of wireless sensor networks. A sensor network is the one which provides information about the surrounding environment by sensing it and clock synchronization in wireless sensor networks plays a vital role to maintain the integrity of entire network. In this paper two major low energy consumption clock synchronization algorithms, Reference Broadcast Synchronization (RBS) and Timing-Sync Protocol for Sensor Networks (TPSN) are simulated, which result in high level of accuracy, reliability, handles substantially greater node densities, supports mobility, and hence perform well under all possible conditions.
Network of phase-locking oscillators and a possible model for neural synchronization
Piqueira, José Roberto C.
2011-09-01
In order to model the synchronization of brain signals, a three-node fully-connected network is presented. The nodes are considered to be voltage control oscillator neurons (VCON) allowing to conjecture about how the whole process depends on synaptic gains, free-running frequencies and delays. The VCON, represented by phase-locked loops (PLL), are fully-connected and, as a consequence, an asymptotically stable synchronous state appears. Here, an expression for the synchronous state frequency is derived and the parameter dependence of its stability is discussed. Numerical simulations are performed providing conditions for the use of the derived formulae. Model differential equations are hard to be analytically treated, but some simplifying assumptions combined with simulations provide an alternative formulation for the long-term behavior of the fully-connected VCON network. Regarding this kind of network as models for brain frequency signal processing, with each PLL representing a neuron (VCON), conditions for their synchronization are proposed, considering the different bands of brain activity signals and relating them to synaptic gains, delays and free-running frequencies. For the delta waves, the synchronous state depends strongly on the delays. However, for alpha, beta and theta waves, the free-running individual frequencies determine the synchronous state.
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...
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.
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 ...
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 and Bifurcation of General Complex Dynamical Networks
Institute of Scientific and Technical Information of China (English)
SUN Wei-Gang; XU Cong-Xiang; LI Chang-Pin; FANG Jin-Qing
2007-01-01
In the present paper, synchronization and bifurcation of general complex dynamical networks are investigated. We mainly focus on networks with a somewhat general coupling matrix, i.e., the sum of each row equals a nonzero constant u. We derive a result that the networks can reach a new synchronous state, which is not the asymptotic limit set determined by the node equation. At the synchronous state, the networks appear bifurcation if we regard the constant u as a bifurcation parameter. Numerical examples are given to illustrate our derived conclusions.
Stochastic synchronization for time-varying complex dynamical networks
Institute of Scientific and Technical Information of China (English)
Guo Xiao-Yong; Li Jun-Min
2012-01-01
This paper studies the stochastic synchronization problem for time-varying complex dynamical networks. This model is totally different from some existing network models. Based on the Lyapunov stability theory, inequality techniques, and the properties of the Weiner process, some controllers and adaptive laws are designed to ensure achieving stochastic synchronization of a complex dynamical network model. A sufficient synchronization condition is given to ensure that the proposed network model is mean-square stable. Theoretical analysis and numerical simulation fully verify the main results.
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.
The Cable and Wireless approach to network synchronization
Calvert, Robert D.
1990-01-01
The philosophy adopted by Cable and Wireless for the synchronization of its world-wide network is presented. The architectures of some clock systems already deployed and how network synchronization had been implemented at selected locations are discussed. This includes some innovative designs as the network spans both first and third world countries with a combination of North Amercan and European hierarchy equipment. Different parts of the global network are linked together by a combination of terrestrial microwave, submarine cable and satellite technology. The impact of synchronization on Intelsat Intermediate Data Rate (IDR) operation and the restoration of submarine cable systems are addressed.
Pattern formation and firing synchronization in networks of map neurons
Energy Technology Data Exchange (ETDEWEB)
Wang Qingyun [State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Aerospace Engineering, College of Engineering, Peking University, Beijing 100871 (China); Duan Zhisheng [State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Aerospace Engineering, College of Engineering, Peking University, Beijing 100871 (China); Huang Lin [State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Aerospace Engineering, College of Engineering, Peking University, Beijing 100871 (China); Chen Guanrong [Department of Electronic Engineering, City University of Hong Kong, Hong Kong (China); Lu Qishao [School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083 (China)
2007-10-15
Patterns and collective phenomena such as firing synchronization are studied in networks of nonhomogeneous oscillatory neurons and mixtures of oscillatory and excitable neurons, with dynamics of each neuron described by a two-dimensional (2D) Rulkov map neuron. It is shown that as the coupling strength is increased, typical patterns emerge spatially, which propagate through the networks in the form of beautiful target waves or parallel ones depending on the size of networks. Furthermore, we investigate the transitions of firing synchronization characterized by the rate of firing when the coupling strength is increased. It is found that there exists an intermediate coupling strength; firing synchronization is minimal simultaneously irrespective of the size of networks. For further increasing the coupling strength, synchronization is enhanced. Since noise is inevitable in real neurons, we also investigate the effects of white noise on firing synchronization for different networks. For the networks of oscillatory neurons, it is shown that firing synchronization decreases when the noise level increases. For the missed networks, firing synchronization is robust under the noise conditions considered in this paper. Results presented in this paper should prove to be valuable for understanding the properties of collective dynamics in real neuronal networks.
Synchronization properties of heterogeneous neuronal networks with mixed excitability type.
Leone, Michael J; Schurter, Brandon N; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G
2015-03-01
We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.
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.
Synchronization speed of identical oscillators on community networks
Institute of Scientific and Technical Information of China (English)
Zhang Zhi; Fu Zhong-Qian; Yan Gang
2009-01-01
Synchronizability of complex oscillators networks has attracted much research interest in recent years. In contrast,in this paper we investigate numerically the synchronization speed, rather than the synchronizability or synchronization stability, of identical oscillators on complex networks with communities. A new weighted community network model is employed here, in which the community strength could be tunable by one parameter δ. The results showed that the synchronization speed of identical oscillators on community networks could reach a maximal value when δ is around 0.1.We argue that this is induced by the competition between the community partition and the scale-free property of the networks. Moreover, we have given the corresponding analysis through the second least eigenvalue λ2 of the Laplacian matrix of the network which supports the previous result that the synchronization speed is determined by the value of λ2.
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.
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
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.
Synchronization Analysis of Master-Slave Probabilistic Boolean Networks.
Lu, Jianquan; Zhong, Jie; Li, Lulu; Ho, Daniel W C; Cao, Jinde
2015-01-01
In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then we investigate synchronization with probability one. By resorting to new approach called semi-tensor product (STP), the master-slave PBNs are expressed in equivalent algebraic forms. Based on the algebraic form, some necessary and sufficient criteria are derived to guarantee synchronization with probability one. Further, we study the synchronization of master-slave PBNs in probability. Synchronization in probability implies that for any initial states, the master BN can be synchronized by the slave BN with certain probability, while synchronization with probability one implies that master BN can be synchronized by the slave BN with probability one. Based on the equivalent algebraic form, some efficient conditions are derived to guarantee synchronization in probability. Finally, several numerical examples are presented to show the effectiveness of the main results.
Bandwidth Efficient Hybrid Synchronization for Wireless Sensor Network
DEFF Research Database (Denmark)
Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee
2015-01-01
Data collection and transmission are the fundamental operations of Wireless Sensor Networks (WSNs). A key challenge in effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Bandwidth Efficient Hybrid...... Synchronization Data Aggregation Algorithm (BESDA) using spanning tree mechanism (SPT). It uses static sink and mobile nodes in the network. BESDA considers the synchronization of a local clock of node with global clock of the network. In the initial stage algorithm established the hierarchical structure...... in the network and then perform the pair-wise synchronization. With the mobility of node, the structure frequently changes causing an increase in energy consumption. To mitigate the problem BESDA aggregate data with the notion of a global timescale throughout the network and schedule based time-division multiple...
Global synchronization of general delayed complex networks with stochastic disturbances
Institute of Scientific and Technical Information of China (English)
Tu Li-Lan
2011-01-01
In this paper, global synchronization of general delayed complex networks with stochastic disturbances, which is a zero-mean real scalar Wiener process, is investigated. The networks under consideration are continuous-time networks with time-varying delay. Based on the stochastic Lyapunov stability theory, It(O)'s differential rule and the linear matrix inequality (LMI) optimization technique, several delay-dependent synchronous criteria are established, which guarantee the asymptotical mean-square synchronization of drive networks and response networks with stochastic disturbances. The criteria are expressed in terms of LMI, which can be easily solved using the Matlab LMI Control Toolbox. Finally, two examples show the effectiveness and feasibility of the proposed synchronous conditions.
Synchronization failure caused by interplay between noise and network heterogeneity
Kobayashi, Y.; Kori, H.
2016-09-01
We investigate synchronization in complex networks of noisy phase oscillators. We find that, while too weak a coupling is not sufficient for the whole system to synchronize, too strong a coupling induces a nontrivial type of phase slip among oscillators, resulting in synchronization failure. Thus, an intermediate coupling range for synchronization exists, which becomes narrower when the network is more heterogeneous. Analyses of two noisy oscillators reveal that nontrivial phase slip is a generic phenomenon when noise is present and coupling is strong. Therefore, the low synchronizability of heterogeneous networks can be understood as a result of the difference in effective coupling strength among oscillators with different degrees; oscillators with high degrees tend to undergo phase slip while those with low degrees have weak coupling strengths that are insufficient for synchronization.
Nonlocal Mechanism for Synchronization of Time Delay Networks
Kanter, Ido; Kopelowitz, Evi; Vardi, Roni; Zigzag, Meital; Cohen, Dana; Kinzel, Wolfgang
2011-11-01
We present the interplay between synchronization of networks with heterogeneous delays and the greatest common divisor (GCD) of loops composing the network. We distinguish between two types of networks; (I) chaotic networks and (II) population dynamic networks with periodic activity driven by external stimuli. For type (I), in the weak chaos region, the units of a chaotic network characterized by GCD=1 are in a chaotic zero-lag synchronization, whereas for GCD>1, the network splits into GCD-clusters in which clustered units are in zero-lag synchronization. These results are supported by simulations of chaotic systems, self-consistent and mixing arguments, as well as analytical solutions of Bernoulli maps. Type (II) is exemplified by simulations of Hodgkin Huxley population dynamic networks with unidirectional connectivity, synaptic noise and distribution of delays within neurons belonging to a node and between connecting nodes. For a stimulus to one node, the network splits into GCD-clusters in which cluster neurons are in zero-lag synchronization. For complex external stimuli, the network splits into clusters equal to the greatest common divisor of loops composing the network (spatial) and the periodicity of the external stimuli (temporal). The results suggest that neural information processing may take place in the transient to synchronization and imply a much shorter time scale for the inference of a perceptual entity.
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.
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.
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.
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
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.
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.
Secure and self-stabilizing clock synchronization in sensor networks
Hoepman, J.H.; Larsson, A.; Schiller, E.M.; Tsigas, P.
2007-01-01
In sensor networks, correct clocks have arbitrary starting offsets and nondeterministic fluctuating skews. We consider an adversary that aims at tampering with the clock synchronization by intercepting messages, replaying intercepted messages (after the adversary's choice of delay), and capturing no
Protecting Clock Synchronization: Adversary Detection through Network Monitoring
Directory of Open Access Journals (Sweden)
Elena Lisova
2016-01-01
Full Text Available Nowadays, industrial networks are often used for safety-critical applications with real-time requirements. Such applications usually have a time-triggered nature with message scheduling as a core property. Scheduling requires nodes to share the same notion of time, that is, to be synchronized. Therefore, clock synchronization is a fundamental asset in real-time networks. However, since typical standards for clock synchronization, for example, IEEE 1588, do not provide the required level of security, it raises the question of clock synchronization protection. In this paper, we identify a way to break synchronization based on the IEEE 1588 standard, by conducting a man-in-the-middle (MIM attack followed by a delay attack. A MIM attack can be accomplished through, for example, Address Resolution Protocol (ARP poisoning. Using the AVISPA tool, we evaluate the potential to perform a delay attack using ARP poisoning and analyze its consequences showing both that the attack can, indeed, break clock synchronization and that some design choices, such as a relaxed synchronization condition mode, delay bounding, and using knowledge of environmental conditions, can make the network more robust/resilient against these kinds of attacks. Lastly, a Configuration Agent is proposed to monitor and detect anomalies introduced by an adversary performing attacks targeting clock synchronization.
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...
Controlled synchronization of complex network with different kinds of nodes
Institute of Scientific and Technical Information of China (English)
Zhengquan YANG; Zhongxin LIU; Zengqiang CHEN; Zhuzhi YUAN
2008-01-01
In this paper, a new dynamical network model is introduced, in which the nodes of the network are different. It is shown that by the designed controllers, the state of the network can exponentially synchronize onto a homogeneous stationary state. Some criteria are derived and some examples are presented. The numerical simulations coincide with theoretical analysis.
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
Synchronization of an uncertain chaotic system via recurrent neural networks
Institute of Scientific and Technical Information of China (English)
谭文; 王耀南
2005-01-01
Incorporating distributed recurrent networks with high-order connections between neurons, the identification and synchronization problem of an unknown chaotic system in the presence of unmodelled dynamics is investigated. Based on the Lyapunov stability theory, the weights learning algorithm for the recurrent high-order neural network model is presented. Also, analytical results concerning the stability properties of the scheme are obtained. Then adaptive control law for eliminating synchronization error of uncertain chaotic plant is developed via Lyapunov methodology.The proposed scheme is applied to model and synchronize an unknown Rossler system.
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)
Training brain networks and states.
Tang, Yi-Yuan; Posner, Michael I
2014-07-01
Brain training refers to practices that alter the brain in a way that improves cognition, and performance in domains beyond those involved in the training. We argue that brain training includes network training through repetitive practice that exercises specific brain networks and state training, which changes the brain state in a way that influences many networks. This opinion article considers two widely used methods - working memory training (WMT) and meditation training (MT) - to demonstrate the similarities and differences between network and state training. These two forms of training involve different areas of the brain and different forms of generalization. We propose a distinction between network and state training methods to improve understanding of the most effective brain training.
Cost and effect of pinning control for network synchronization
Institute of Scientific and Technical Information of China (English)
Li Rong; Duan Zhi-Sheng; Chen Guan-Rong
2009-01-01
The problem of pinning control for the synchronization of complex dynamical networks is discussed in this paper. A cost function of the controlled network is defined by the feedback gain and the coupling strength of the network. An interesting result is that a lower cost is achieved by using the control scheme of pinning nodes with smaller degrees. Some strict mathematical analyses are presented for achieving a lower cost in the synchronization of different star-shaped various star-shaped networks are performed for verification and illustration.
Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay
Energy Technology Data Exchange (ETDEWEB)
Tang, Longkun, E-mail: tomlk@hqu.edu.cn, E-mail: xqwu@whu.edu.cn [School of Mathematical Science, Huaqiao University, Quanzhou 362021 (China); Wu, Xiaoqun, E-mail: tomlk@hqu.edu.cn, E-mail: xqwu@whu.edu.cn; Lu, Jun-an, E-mail: jalu@whu.edu.cn [School of Mathematics and Statistics, Wuhan University, Wuhan 430072 (China); Lü, Jinhu, E-mail: jhlu@iss.ac.cn [LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Faculty of Engineering, King Abdulaziz University, Jeddah (Saudi Arabia)
2015-03-15
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.
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.
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.
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.
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)
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.
Decentralized adaptive synchronization of an uncertain complex delayed dynamical network
Institute of Scientific and Technical Information of China (English)
Weisong ZHONG; Jun ZHAO; Georgi M.DIMIROVSKI
2009-01-01
In this paper,we investigate the locally and globally adaptive synchronization problem for an uncertain complex dynamical network with time-varying coupling delays based on the decentralized control.The coupling terms here are bounded by high-order polynomials with known gains that are ubiquitous in a large class of complex dynamical networks.We generalize the usual technology of searching for an appropriate coordinates transformation to change the network dynamics into a series of decoupled lower-dimensional systems.Several adaptive synchronization criteria are derived by constructing the Lyapunov-Krasovskii functional and Barbalat lemma,and the proposed criteria are simple in form and convenient for the practical engineering design.Numerical simulations illustrated by a nearest-neighbor coupling network verify the effectiveness of the proposed synchronization scheme.
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.
Different types of synchronization in coupled network based chaotic circuits
Srinivasan, K.; Chandrasekar, V. K.; Gladwin Pradeep, R.; Murali, K.; Lakshmanan, M.
2016-10-01
We propose a simple and new unified method to achieve lag, complete and anticipatory synchronizations in coupled nonlinear systems. It can be considered as an alternative to the subsystem and intentional parameter mismatch methods. This novel method is illustrated in a unidirectionally coupled RC phase shift network based Chua's circuit. Employing feedback coupling, different types of chaos synchronization are observed experimentally and numerically in coupled identical chaotic oscillators without using time delay. With a simple switch in the experimental set up we observe different kinds of synchronization. We also analyze the coupled system with numerical simulations.
Synchronization of spatiotemporal chaos in complex networks via backstepping
Institute of Scientific and Technical Information of China (English)
Chai Yuan; Lü Ling; Chen Li-Qun
2012-01-01
A backstepping approach is proposed for the synchronization of chain networks of multi-spatiotemporal chaotic systems with topologically equivalent structures.The synchronization of multi-spatiotemporal chaotic systems is implemented by adding the control only to a terminal node,and the controller is designed via a corresponding update law.The control law is applied to spatiotemporal Gray-Scott systems.Numerical results demonstrate the effectiveness and the feasibility of the proposed approach.
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...
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.
Adaptive impulsive cluster synchronization in community network with nonidentical nodes
Gong, Xiaoli; Gan, Luyining; Wu, Zhaoyan
2016-07-01
In this paper, cluster synchronization in community network with nonidentical nodes is investigated. Through introducing proper adaptive strategy into impulsive control scheme, adaptive impulsive controllers are designed for achieving the cluster synchronization. In this adaptive impulsive control scheme, for any given networks, the impulsive gains can adjust themselves to proper values according to the proposed adaptive strategy when the impulsive intervals are fixed. The impulsive instants can be estimated by solving a sequence of maximum value problems when the impulsive gains are fixed. Both community networks without and with coupling delay are considered. Based on the Lyapunov function method and mathematical analysis technique, two synchronization criteria are derived. Several numerical examples are performed to verify the effectiveness of the derived theoretical results.
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.
Anticipated synchronization in neuronal network motifs
Matias, F. S.; Gollo, L. L.; Carelli, P. V.; Copelli, M.; Mirasso, C. R.
2013-01-01
Two identical dynamical systems coupled unidirectionally (in a so called master-slave configuration) exhibit anticipated synchronization (AS) if the one which receives the coupling (the slave) also receives a negative delayed self-feedback. In oscillatory neuronal systems AS is characterized by a phase-locking with negative time delay τ between the spikes of the master and of the slave (slave fires before the master), while in the usual delayed synchronization (DS) regime τ is positive (slave fires after the master). A 3-neuron motif in which the slave self-feedback is replaced by a feedback loop mediated by an interneuron can exhibits both AS and DS regimes. Here we show that AS is robust in the presence of noise in a 3 Hodgkin-Huxley type neuronal motif. We also show that AS is stable for large values of τ in a chain of connected slaves-interneurons.
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.
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
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.
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...
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.
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.
The effect of apolipoprotein E4 on synchronous neural interactions in brain cultures.
Christopoulos, Vassilios; Georgopoulos, Angeliki; Georgopoulos, Apostolos P
2015-06-01
In a previous study, we assessed the synchronous neural interactions (SNI) in a developing neural network in brain cultures on multielectrode arrays (Christopoulos et al. in J Neural Eng 9:046008, 2012). Here, we report on the effects of apolipoprotein E4 (apoE4) on these neural interactions. We carried out six experiments (five using rodent brain cultures and one using neuroblastoma cultures) in which we recorded local field potentials (LFP) from 59 sites for several days in vitro under the following conditions. In one experiment, we added to the culture media triglyceride (TG)-rich lipoproteins from a human subject with the apoE4/4 genotype, whereas in the other experiments, we added recombinant human apoE4. We found that SNI in the apoE4-treated cultures had higher coefficient of SNI variation, as compared to control cultures. These findings further document the role of SNI as a fundamental aspect of the dynamic organization of neural networks (Langheim et al. in Proc Natl Acad Sci USA 103:455-459, 2006. doi: 10.1073/pnas.0509623102 ; Georgopoulos et al. in J Neural Eng 4:349-355, 2007) and extend the effect of apoE4 on SNI (Leuthold et al. in Exp Brain Res 226:525-536, 2013) across different brain species (human, rodents), apoE source (TG-rich lipoproteins, recombinant), neural signals (MEG, LFP), and brain network (intact brain, developing brain in vitro). To our knowledge, this is the first study of the effects of apoE4 on neural network function in vitro.
Synchronization in a Random Length Ring Network for SDN-Controlled Optical TDM Switching
DEFF Research Database (Denmark)
Kamchevska, Valerija; Cristofori, Valentina; Da Ros, Francesco;
2016-01-01
. In addition, we propose a novel synchronization algorithm that enables automatic synchronization of software defined networking controlled all-optical TDM switching nodes connected in a ring network. Besides providing synchronization, the algorithm also can facilitate dynamic slot size change and failure...... multiplexing transmission and switching of data bursts when using the proposed algorithm to provide synchronization....
Concurrent enhancement of percolation and synchronization in adaptive networks
Eom, Young-Ho; Caldarelli, Guido
2015-01-01
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.
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.
Isochronal synchronization in networks and chaos-based TDMA communication
Grzybowski, J. M. V.; Macau, E. E. N.; Yoneyama, T.
2014-06-01
Pairs of delay-coupled chaotic systems were shown to be able to achieve isochronal synchronization under bidirectional coupling and self-feedback. Such identical-in-time behavior was demonstrated to be stable under a set of conditions and to support simultaneous bidirectional communication between pairs of chaotic oscillators coupled with time-delay. More recently, it was shown that isochronal synchronization can emerge in networks with several hundreds of oscillators, which allows its exploitation for communication in distributed systems. In this paper, we introduce a conceptual framework for the application of isochronal synchronization to TDMA communication in networks of delay-coupled chaotic oscillators. On the basis of the stable and identical-in-time behavior of delay-coupled chaotic systems, the chaotic dynamics of distributed oscillators is used to support and sustain coordinate communication among nodes over the network. On the basis of the unique features of chaotic systems in isochronal synchronization, the chaotic signals are used to timestamp clock readings at the physical layer such that logical clock synchronization among the nodes (a prerequisite for TDMA) can be exploited using the same basic structure. The result is a standalone network communication scheme that can be advantageously applied in the context of ad-hoc networks or alike, especially short-ranged ones that yield low values of time-delay. As explored to its depths in practical implementations, this conceptual framework is argued to have potential to provide gain in simplicity, security and efficiency in communication schemes for autonomous/standalone network applications.
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.
Robust adaptive synchronization of chaotic neural networks by slide technique
Institute of Scientific and Technical Information of China (English)
Lou Xu-Yang; Cui Bao-Tong
2008-01-01
In this paper,we focus on the robust adaptive synchronization between two coupled chaotic neural networks with all the parameters unknown and time-varying delay.In order to increase the robustness of the two coupled neural networks,the key idea is that a sliding-mode-type controller is employed.Moreover,without the estimate values of the network unknown parameters taken as an updating object,a new updating object is introduced in the constructing of controller.Using the proposed controller,without any requirements for the boundedness,monotonicity and differentiability of activation functions,and symmetry of connections,the two coupled chaotic neural networks can achieve global robust synchronization no matter what their initial states are.Finally,the numerical simulation validates the effectiveness and feasibility of the proposed technique.
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.
Shuttle-run synchronization in mobile ad hoc networks
Ma, Sheng-Fei; Bi, Hong-Jie; Zou, Yong; Liu, Zong-Hua; Guan, Shu-Guang
2015-06-01
In this work, we study the collective dynamics of phase oscillators in a mobile ad hoc network whose topology changes dynamically. As the network size or the communication radius of individual oscillators increases, the topology of the ad hoc network first undergoes percolation, forming a giant cluster, and then gradually achieves global connectivity. It is shown that oscillator mobility generally enhances the coherence in such networks. Interestingly, we find a new type of phase synchronization/clustering, in which the phases of the oscillators are distributed in a certain narrow range, while the instantaneous frequencies change signs frequently, leading to shuttle-run-like motion of the oscillators in phase space. We conduct a theoretical analysis to explain the mechanism of this synchronization and obtain the critical transition point.
Secure and self-stabilizing clock synchronization in sensor networks
Hoepman, J.H.; Larsson, A.; Schiller, E.M.; Tsigas, P.
2011-01-01
In sensor networks, correct clocks have arbitrary starting offsets and nondeterministic fluctuating skews. We consider an adversary that aims at tampering with the clock synchronization by intercepting messages, replaying intercepted messages (after the adversary’s choice of delay), and capturing no
Explosive synchronization of complex networks with different chaotic oscillators
Institute of Scientific and Technical Information of China (English)
Zhao Jun-Chan
2013-01-01
Recent studies have shown that explosive synchronization transitions can be observed in networks of phase oscillators [Gómez-Garde(n)es 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,Sendi(n)a-Nadal I,Gómez-Garde(n)es 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 Rtssler 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.
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.
Synchronization in a Novel Local-World Dynamical Network Model
Directory of Open Access Journals (Sweden)
Jianeng Tang
2014-01-01
Full Text Available Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes.
Graph partitions and cluster synchronization in networks of oscillators
Schaub, Michael T.; O'Clery, Neave; Billeh, Yazan N.; Delvenne, Jean-Charles; Lambiotte, Renaud; Barahona, Mauricio
2016-09-01
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 graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators.
Cluster-based Multihop Synchronization Scheme for Femtocell Network
Directory of Open Access Journals (Sweden)
Aisha H. Abdalla
2012-10-01
Full Text Available ABSTRACT: Femtocell technology has been drawing considerable attention as a cost-effective means of improving cellular coverage and capacity. It is connected to the core network through an IP backhaul and can only use timing protocols such as IEEE1588 or Network Time Protocol (NTP. Furthermore, the femtocell is installed indoor, and cannot use a GPS antenna for time synchronization. High-precision crystal oscillators can solve the timing problem, but they are often too expensive for consumer grade devices. Therefore, femtocell Base Station (fBS synchronization is one of the principle technical trends in femtocell deployment. Since fBSand macrocell Base Station (mBS network operates on the same frequency under a licensed spectrum, fBS network can interfere with the macrocell network. In addition, fBSs can also interfere with each other if multiple units are in close proximity. Furthermore, in a flat fBS structured network using IEEE 1588 synchronization algorithm and fBS-fBS synchronization scheme creates offset and frequency error which results inaccurate synchronization. In order to reduce offset and frequency error (skew, this paper proposed a cluster-based multihop synchronization scheme to achieve precise in fBS neighbor nodes. The proposed scheme is able to reduce the offset and skew significantly.ABSTRAK: Teknologi Femtocell telah menjadi tumpuan sebagai alat yang kos-efektif dalam memperbaiki liputan mudahalih dan kapasiti. Ia menghubungkan jaringan teras melalui IP backhaul dan hanya boleh menggunakan protokol masa seperti IEEE1588 atau Protokol Jaringan Masa (NTP. Seterusnya, femtocell dipasang di dalam, dan tidak boleh menggunakan antena GPS untuk sinkronisasi masa. Osilator Kristal yang tinggi kejituannya boleh menyelesaikan masalah masa, tetapi ianya mahal bagi gred peranti consumer. Oleh itu, sinkronisasi Stesen Asas femtocell (fBS adalah salah satu tren teknikal prinsip dalam deployment femtocell. Memandangkan fBS dan jaringan
Synchronization of chaos using radial basis functions neural networks
Institute of Scientific and Technical Information of China (English)
Ren Haipeng; Liu Ding
2007-01-01
The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.
Public channel cryptography by synchronization of neural networks and chaotic maps.
Mislovaty, Rachel; Klein, Einat; Kanter, Ido; Kinzel, Wolfgang
2003-09-12
Two different kinds of synchronization have been applied to cryptography: synchronization of chaotic maps by one common external signal and synchronization of neural networks by mutual learning. By combining these two mechanisms, where the external signal to the chaotic maps is synchronized by the nets, we construct a hybrid network which allows a secure generation of secret encryption keys over a public channel. The security with respect to attacks, recently proposed by Shamir et al., is increased by chaotic synchronization.
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...
Event-based exponential synchronization of complex networks.
Zhou, Bo; Liao, Xiaofeng; Huang, Tingwen
2016-10-01
In this paper, we consider exponential synchronization of complex networks. The information diffusions between nodes are driven by properly defined events. By employing the M-matrix theory, algebraic graph theory and the Lyapunov method, two kinds of distributed event-triggering laws are designed, which avoid continuous communications between nodes. Then, several criteria that ensure the event-based exponential synchronization are presented, and the exponential convergence rates are obtained as well. Furthermore, we prove that Zeno behavior of the event-triggering laws can be excluded before synchronization being achieved, that is, the lower bounds of inter-event times are strictly positive. Finally, a simulation example is provided to illustrate the effectiveness of theoretical analysis. PMID:27668021
Frequency control in synchronized networks of inhibitory neurons
Chow, C C; Ritt, J; Kopell, N; Chow, Carson C.; White, John A.; Ritt, Jason; Kopell, Nancy
1998-01-01
We analyze the control of frequency for a synchronized inhibitory neuronal network. The analysis is done for a reduced membrane model with a biophysically-based synaptic influence. We argue that such a reduced model can quantitatively capture the frequency behavior of a larger class of neuronal models. We show that in different parameter regimes, the network frequency depends in different ways on the intrinsic and synaptic time constants. Only in one portion of the parameter space, called `phasic', is the network period proportional to the synaptic decay time. These results are discussed in connection with previous work of the authors, which showed that for mildly heterogeneous networks, the synchrony breaks down, but coherence is preserved much more for systems in the phasic regime than in the other regimes. These results imply that for mildly heterogeneous networks, the existence of a coherent rhythm implies a linear dependence of the network period on synaptic decay time, and a much weaker dependence on th...
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.
Bordier, Cécile; Macaluso, Emiliano
2015-09-01
Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time-series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time-series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task-dependent changes of connectivity). We present a fully data-driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time-windows was used to identify if/when any area showed stimulus/task-related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time-series (stimulus/task-related networks). Finally, for each network, a second clustering step grouped together all the time-windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task-related activity at specific time-points during the fMRI time-series. We label these configurations: "brain modes" (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli.
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...
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...
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.
Directory of Open Access Journals (Sweden)
Jean-Pascal Pfister
2010-07-01
Full Text Available Highly synchronized neural networks can be the source of various pathologies such as Parkinson's disease or essential tremor. Therefore, it is crucial to better understand the dynamics of such networks and the conditions under which a high level of synchronization can be observed. One of the key factors that influences the level of synchronization is the type of learning rule that governs synaptic plasticity. Most of the existing work on synchronization in recurrent network with synaptic plasticity are based on numerical simulations and there is a clear lack of a theoretical framework for studying the effects of various synaptic plasticity rules. In this paper we derive analytically the conditions for Spike-Timing Dependent Plasticity (STDP to lead a network into a synchronized or a desynchronized state. We also show that under appropriate conditions bistability occurs in recurrent networks governed by STDP. Indeed, a pathological regime with strong connections and therefore strong synchronized activity, as well as a physiological regime with weaker connections and lower levels of synchronization are found to coexist. Furthermore, we show that with appropriate stimulation, the network dynamics can be pushed to the low synchronization stable state. This type of therapeutical stimulation is very different from the existing High-Frequency stimulation for Deep-Brain Stimulation since once the stimulation is stopped the network stays in the low synchronization regime.
Kim, Sang-Yoon; Lim, Woochang
2015-11-01
We consider a clustered network with small-world subnetworks of inhibitory fast spiking interneurons and investigate the effect of intermodular connection on the emergence of fast sparsely synchronized rhythms by varying both the intermodular coupling strength Jinter and the average number of intermodular links per interneuron Msyn(inter ). In contrast to the case of nonclustered networks, two kinds of sparsely synchronized states such as modular and global synchronization are found. For the case of modular sparse synchronization, the population behavior reveals the modular structure, because the intramodular dynamics of subnetworks make some mismatching. On the other hand, in the case of global sparse synchronization, the population behavior is globally identical, independently of the cluster structure, because the intramodular dynamics of subnetworks make perfect matching. We introduce a realistic cross-correlation modularity measure, representing the matching degree between the instantaneous subpopulation spike rates of the subnetworks, and examine whether the sparse synchronization is global or modular. Depending on its magnitude, the intermodular coupling strength Jinter seems to play "dual" roles for the pacing between spikes in each subnetwork. For large Jinter, due to strong inhibition it plays a destructive role to "spoil" the pacing between spikes, while for small Jinter it plays a constructive role to "favor" the pacing between spikes. Through competition between the constructive and the destructive roles of Jinter, there exists an intermediate optimal Jinter at which the pacing degree between spikes becomes maximal. In contrast, the average number of intermodular links per interneuron Msyn(inter ) seems to play a role just to favor the pacing between spikes. With increasing Msyn(inter ), the pacing degree between spikes increases monotonically thanks to the increase in the degree of effectiveness of global communication between spikes. Furthermore, we
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
Coutanche, Marc N; Thompson-Schill, Sharon L
2013-01-01
The fluctuations in a brain region's activation levels over a functional magnetic resonance imaging (fMRI) time-course are used in functional connectivity (FC) to identify networks with synchronous responses. It is increasingly recognized that multi-voxel activity patterns contain information that cannot be extracted from univariate activation levels. Here we present a novel analysis method that quantifies regions' synchrony in multi-voxel activity pattern discriminability, rather than univariate activation, across a timeseries. We introduce a measure of multi-voxel pattern discriminability at each time-point, which is then used to identify regions that share synchronous time-courses of condition-specific multi-voxel information. This method has the sensitivity and access to distributed information that multi-voxel pattern analysis enjoys, allowing it to be applied to data from conditions not separable by univariate responses. We demonstrate this by analyzing data collected while people viewed four different types of man-made objects (typically not separable by univariate analyses) using both FC and informational connectivity (IC) methods. IC reveals networks of object-processing regions that are not detectable using FC. The IC results support prior findings and hypotheses about object processing. This new method allows investigators to ask questions that are not addressable through typical FC, just as multi-voxel pattern analysis (MVPA) has added new research avenues to those addressable with the general linear model (GLM).
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
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.
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.
Synchronization of networked Jahn-Teller systems in SQUIDs
Gül, Yusuf
2016-06-01
We consider the nonlinear effects in a Jahn-Teller (JT) system of two coupled resonators interacting simultaneously with a flux qubit using coupled SQUIDs. A two-frequency description of JT system that inherits the networked structure of both nonlinear Josephson junctions and harmonic oscillators is employed to describe the synchronous structures in a multifrequency scheme. Eigenvalue spectrum is used to show the switch between the effective single mode and two mode configuration in terms of frequency difference. The Rabi supersplitting is investigated by the spectral response of JT systems in different coupling regimes. Second-order coherence functions are employed to investigate antibunching effects in resonator mode. Synchronous structure between correlations of privileged mode and qubit is obtained in localization-delocalization and photon blockade regime controlled by the population imbalance.
Amplification of asynchronous inhibition-mediated synchronization by feedback in recurrent networks.
Directory of Open Access Journals (Sweden)
Sashi Marella
2010-02-01
Full Text Available Synchronization of 30-80 Hz oscillatory activity of the principle neurons in the olfactory bulb (mitral cells is believed to be important for odor discrimination. Previous theoretical studies of these fast rhythms in other brain areas have proposed that principle neuron synchrony can be mediated by short-latency, rapidly decaying inhibition. This phasic inhibition provides a narrow time window for the principle neurons to fire, thus promoting synchrony. However, in the olfactory bulb, the inhibitory granule cells produce long lasting, small amplitude, asynchronous and aperiodic inhibitory input and thus the narrow time window that is required to synchronize spiking does not exist. Instead, it has been suggested that correlated output of the granule cells could serve to synchronize uncoupled mitral cells through a mechanism called "stochastic synchronization", wherein the synchronization arises through correlation of inputs to two neural oscillators. Almost all work on synchrony due to correlations presumes that the correlation is imposed and fixed. Building on theory and experiments that we and others have developed, we show that increased synchrony in the mitral cells could produce an increase in granule cell activity for those granule cells that share a synchronous group of mitral cells. Common granule cell input increases the input correlation to the mitral cells and hence their synchrony by providing a positive feedback loop in correlation. Thus we demonstrate the emergence and temporal evolution of input correlation in recurrent networks with feedback. We explore several theoretical models of this idea, ranging from spiking models to an analytically tractable model.
Coullaut-Valera, R; Arbaiza, I; Bajo, R; Arrúe, R; López, M E; Coullaut-Valera, J; Correas, A; López-Sanz, D; Maestu, F; Papo, D
2014-02-01
Drug abusers typically consume not just one but several types of drugs, starting from alcohol and marijuana consumption, and then dramatically lapsing into addiction to harder drugs, such as cocaine, heroin, or amphetamine. The brain of drug abusers presents various structural and neurophysiological abnormalities, some of which may predate drug consumption onset. However, how these changes translate into modifications in functional brain connectivity is still poorly understood. To characterize functional connectivity patterns, we recorded Electroencephalogram (EEG) activity from 21 detoxified drug abusers and 20 age-matched control subjects performing a simple counting task and at rest activity. To evaluate the cortical brain connectivity network we applied the Synchronization Likelihood algorithm. The results showed that drug abusers had higher synchronization levels at low frequencies, mainly in the θ band (4-8 Hz) between frontal and posterior cortical regions. During the counting task, patients showed increased synchronization in the β (14-35 Hz), and γ (35-45 Hz) frequency bands, in fronto-posterior and interhemispheric temporal regions. Taken together 'slow-down' at rest and task-related 'over-exertion' could indicate that the brain of drug abusers is suffering from a premature form of ageing. Future studies will clarify whether this condition can be reversed following prolonged periods of abstinence.
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.
Decentralized Network-level Synchronization in Mobile Ad Hoc Networks
Voulgaris, Spyros; Dobson, Matthew; Steen, van Maarten
2016-01-01
Energy is the scarcest resource in ad hoc wireless networks, particularly in wireless sensor networks requiring a long lifetime. Intermittently switching the radio on and off is widely adopted as the most effective way to keep energy consumption low. This, however, prevents the very goal of communic
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.
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.
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...
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...
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.
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.
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 ...
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.
Comparison of Synchronization Ability of Four Types of Regular Coupled Networks
Institute of Scientific and Technical Information of China (English)
WANG Hai-Xia; LU Qi-Shao; SHI Xia
2012-01-01
We investigate the synchronization ability of four types of regular coupled networks. By introducing the proper error variables and Lyapunov functions, we turn the stability of synchronization manifold into that of null solution of error equations, further, into the negative definiteness of some symmetric matrices, thus we get the sufficient synchronization stability conditions. To test the valid of the results, we take the Chua's circuit as an example. Although the theoretical synchronization thresholds appear to be very conservative, they provide new insights about the influence of topology and scale of networks on synchronization, and that the theoretical results and our numerical simulations are consistent.
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...... bridge the gap between Ethernet and mobile network domains creating a comprehensive architectural analysis....... 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...
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;
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...
Synchronization in a network of delay coupled maps with stochastically switching topologies
Nag, Mayurakshi; Poria, Swarup
2016-10-01
The synchronization behavior of delay coupled chaotic smooth unimodal maps over a ring network with stochastic switching of links at every time step is reported in this paper. It is observed that spatiotemporal synchronization never appears for nearest neighbor connections; however, stochastic switching of connections with homogeneous delay $(\\tau)$ is capable of synchronizing the network to homogeneous steady state or periodic orbit or synchronized chaotically oscillating state depending on the delay parameter, stochasticity parameter and map parameters. Linear stability analysis of the synchronized state is done analytically for unit delay and the value of the critical coupling strength, at which the onset of synchronization occurs is determined analytically. The logistic map $rx(1-x)$ (a smooth unimodal map) is chosen for numerical simulation purpose. Synchronized steady state or synchronized period-2 orbit is stabilized for delay $\\tau=1$. On the other hand for delay $\\tau=2$ the network is stabilized to the fixed point of the local map. Numerical simulation results are in good agreement with the analytically obtained linear stability analysis results. Another interesting observation is the existence of synchronized chaos in the network for delay $\\tau>2$. Calculating synchronization error and plotting time series data and Poincare first return map the existence of synchronized chaos is confirmed. The results hold good for other smooth unimodal maps also.
Energy Technology Data Exchange (ETDEWEB)
Hao Yinghang [School of Physics, Ludong University, Yantai 264025 (China); Gong, Yubing, E-mail: gongyubing09@hotmail.co [School of Physics, Ludong University, Yantai 264025 (China); Wang Li; Ma Xiaoguang; Yang Chuanlu [School of Physics, Ludong University, Yantai 264025 (China)
2011-04-15
Research highlights: Single synchronization transition for gap-junctional coupling. Multiple synchronization transitions for chemical synaptic coupling. Gap junctions and chemical synapses have different impacts on synchronization transition. Chemical synapses may play a dominant role in neurons' information processing. - Abstract: In this paper, we have studied time delay- and coupling strength-induced synchronization transitions in scale-free modified Hodgkin-Huxley (MHH) neuron networks with gap-junctions and chemical synaptic coupling. It is shown that the synchronization transitions are much different for these two coupling types. For gap-junctions, the neurons exhibit a single synchronization transition with time delay and coupling strength, while for chemical synapses, there are multiple synchronization transitions with time delay, and the synchronization transition with coupling strength is dependent on the time delay lengths. For short delays we observe a single synchronization transition, whereas for long delays the neurons exhibit multiple synchronization transitions as the coupling strength is varied. These results show that gap junctions and chemical synapses have different impacts on the pattern formation and synchronization transitions of the scale-free MHH neuronal networks, and chemical synapses, compared to gap junctions, may play a dominant and more active function in the firing activity of the networks. These findings would be helpful for further understanding the roles of gap junctions and chemical synapses in the firing dynamics of neuronal networks.
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.
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...
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.
Global synchronization of Chua's chaotic delay network by using linear matrix inequality
Institute of Scientific and Technical Information of China (English)
Li Zhi; Shi Song-Jiao
2004-01-01
Global synchronization of Chua's chaotic dynamical networks with coupling delays is investigated in this paper.Unlike other approaches, where only local results were obtained, the network is found to be not linearized in this paper.Instead, the global synchronization is obtained by using the linear matrix inequality theory. Moreover, some quite simple linear-state-error feedback controllers for global synchronization are derived, which can be easily constructed based on the minimum eigenvalue of the coupling matrix. A simulation of Chua's chaotic network with global coupling delays in nodes is finally given, which is used to verify the theoretical results of the proposed global synchronization scheme.
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.
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.
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...
An Approach to Analyse Phase Synchronization in Oscillator Networks with Weak Coupling
Institute of Scientific and Technical Information of China (English)
ZHANG Jian-Bao; LIU Zeng-Rong; LI Ying
2007-01-01
We study phase synchronization in oscillator networks through phase reduced method. The dynamics of networks is reduced to phase equations by this method. Analysing the phase equations through the master stability function method, one obtains that the oscillators with identical frequency can be in-phase synchronized by weak balanced coupling. Sim.ila.rly, the problem of frequency synchronization of oscillators with different frequencies is transformed to the existence of a locally asymptotically stable equilibrium of the phase error system.
Organization of anti-phase synchronization pattern in neural networks: what are the key factors?
Directory of Open Access Journals (Sweden)
Dong eLi
2011-12-01
Full Text Available Anti-phase oscillation has been widely observed in cortical neuralnetwork. Elucidating the mechanism underlying the organization ofanti-phase pattern is of significance for better understanding morecomplicated pattern formations in brain networks. In dynamicalsystems theory, the organization of anti-phase oscillation patternhas usually been considered to relate to time-delay in coupling.This is consistent to conduction delays in real neural networks inthe brain due to finite propagation velocity of action potentials.However, other structural factors in cortical neural network, suchas modular organization (connection density and the coupling types(excitatory or inhibitory, could also play an important role. Inthis work, we investigate the anti-phase oscillation patternorganized on a two-module network of either neuronal cell model orneural mass model, and analyze the impact of the conduction delaytimes, the connection densities, and coupling types. Our resultsshow that delay times and coupling types can play key roles in thisorganization. The connection densities may have an influence on thestability if an anti-phase pattern exists due to the other factors.Furthermore, we show that anti-phase synchronization of slowoscillations can be achieved with small delay times if there isinteraction between slow and fast oscillations. These results aresignificant for further understanding more realistic spatiotemporaldynamics of cortico-cortical communications.
H∞-Based Pinning Synchronization of General Complex Dynamical Networks with Coupling Delays
Directory of Open Access Journals (Sweden)
Bowen Du
2013-01-01
Full Text Available This paper investigates the synchronization of complex dynamical networks with coupling delays and external disturbances by applying local feedback injections to a small fraction of nodes in the whole network. Based on H∞ control theory, some delay-independent and -dependent synchronization criteria with a prescribed H∞ disturbances attenuation index are derived for such controlled networks in terms of linear matrix inequalities (LMIs, which guarantee that by placing a small number of feedback controllers on some nodes, the whole network can be pinned to reach network synchronization. A simulation example is included to validate the theoretical results.
Synchronous state in a fully connected phase-locked loop network
Directory of Open Access Journals (Sweden)
J. R. C. Piqueira
2006-01-01
work, an estimation is analytically obtained for the synchronous state in a generic N-node network. Numerical experiments complete the analysis of the fully connected network relating free-running frequencies, node gains, and propagation delays.
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.
Robust fixed-time synchronization of delayed Cohen-Grossberg neural networks.
Wan, Ying; Cao, Jinde; Wen, Guanghui; Yu, Wenwu
2016-01-01
The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results.
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
Institute of Scientific and Technical Information of China (English)
Yang Xinsong; Cao Jinde
2012-01-01
In this article,we consider the global chaotic synchronization of general coupled neural networks,in which subsystems have both discrete and distributed delays.Stochastic perturbations between subsystems are also considered.On the basis of two simple adaptive pinning feedback control schemes,Lyapunov functional method,and stochastic analysis approach,several sufficient conditions are developed to guarantee global synchronization of the coupled neural networks with two kinds of delay couplings,even if only partial states of the nodes are coupled.The outer-coupling matrices may be symmetric or asymmetric.Unlike existing results that an isolate node is introduced as the pinning target,we pin to help the network realizing synchronization without introducing any isolate node when the network is not synchronized.As a by product,sufficient conditions under which the network realizes synchronization without control are derived.Numerical simulations confirm the effectiveness of the obtained results.
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.
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.
Synchronization and Bifurcation Analysis in Coupled Networks of Discrete-Time Systems
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Synchronization and bifurcation analysis in coupled networks of discrete-time systems are investigated in the present paper. We mainly focus on some special coupling matrix, i.e., the sum of each row equals a nonzero constant u and the network connection is directed. A result that the network can reach a new synchronous state, which is not the asymptotic limit set determined by the node state equation, is derived. It is interesting that the network exhibits bifurcation if we regard the constant u as a bifurcation parameter at the synchronous state. Numerical simulations are given to show the efficiency of our derived conclusions.
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.
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.
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
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.
Exponential synchronization of memristive Cohen-Grossberg neural networks with mixed delays.
Yang, Xinsong; Cao, Jinde; Yu, Wenwu
2014-06-01
This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen-Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen-Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results.
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
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.
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...
Effect of externality in multiplex networks on one-layer synchronization
Jiang, Xin; Li, Meng; Zheng, Zhiming; Ma, Yifang; Ma, Lili
2015-06-01
Synchronization phenomenon plays an important role in the dynamic process on complex networks. In this letter, we consider synchronization under multiplex structures and explore the effect of external parts on the multilayered Kuramoto model with positive correlation between frequencies and degree. We show that large and strong mixing parts of the external layer may put off synchronization while the coupling strength of external layers has an accelerating, but limited, influence on synchronization. In particular, an explosive synchronization can be obtained by weakening the co-evolution coupling strength. The transition tends to be smooth when the co-evolution coupling strength increases. Our findings indicate that externality from multiplex networks, especially the mixing parts, and the co-evolution coupling strength should be of importance for synchronization on one certain layer.
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.
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 ...
Synchronization-based computation through networks of coupled oscillators
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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.
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Bo Liu
2013-01-01
Full Text Available This paper investigates the adaptive synchronization of complex dynamical networks satisfying the local Lipschitz condition with switching topology. Based on differential inclusion and nonsmooth analysis, it is proved that all nodes can converge to the synchronous state, even though only one node is informed by the synchronous state via introducing decentralized adaptive strategies to the coupling strengths and feedback gains. Finally, some numerical simulations are worked out to illustrate the analytical results.
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.
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
Zhang, Wei; Li, Chuandong; Huang, Tingwen; Xiao, Mingqing
2015-11-01
In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper.
Zhuo, Zhao; Zhang, Jie; Fu, Zhongqian
2011-01-01
In the past decade, synchronization on complex networks has attracted increasing attentions from various research disciplines. Most previous works, however, focus only on the dynamic behaviors of synchronization process in the stable region, i.e., global synchronization. In this letter, we demonstrate that synchronization process on complex networks can efficiently reveal the substructures of networks when the coupling strength of chaotic oscillators is under the lower boundary of stable region. Both analytic and numerical results show that the nodes belonging to the same component in the hierarchical network are tightly clustered according to the Euclidean distances between the state vectors of the corresponding oscillators, and different levels of hierarchy can be systematically unfolded by gradually tuning the coupling strength. When the coupling strengths exceed the upper boundary of stable region, the hierarchy of the network cannot be recognized by this approach. Extensive simulations suggest that our m...
Gan, Qintao; Lv, Tianshi; Fu, Zhenhua
2016-04-01
In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.
Institute of Scientific and Technical Information of China (English)
金运国; 钟守铭; 安娜
2015-01-01
Although function projective synchronization in complex dynamical networks has been extensively studied in the literature, few papers deal with the problem between two different complex networks with correlated random disturbances. In this paper, we present some novel techniques to analyze the problem of synchronization. A probability approach is introduced to obtain an almost sure synchronization criterion. We also present some efficient approaches to analyze the problem of exponential synchronization. For the problem of synchronization in some complex networks, our approaches not only can replace the LaSalle-type theorem but also allow improvements of existing results in the literature. Finally, some numerical examples are provided to demonstrate the effectiveness of the proposed approaches.
Cai, Zuowei; Huang, Lihong; Zhang, Lingling
2015-05-01
This paper investigates the problem of exponential synchronization of time-varying delayed neural networks with discontinuous neuron activations. Under the extended Filippov differential inclusion framework, by designing discontinuous state-feedback controller and using some analytic techniques, new testable algebraic criteria are obtained to realize two different kinds of global exponential synchronization of the drive-response system. Moreover, we give the estimated rate of exponential synchronization which depends on the delays and system parameters. The obtained results extend some previous works on synchronization of delayed neural networks not only with continuous activations but also with discontinuous activations. Finally, numerical examples are provided to show the correctness of our analysis via computer simulations. Our method and theoretical results have a leading significance in the design of synchronized neural network circuits involving discontinuous factors and time-varying delays.
Simple models of human brain functional networks.
Vértes, Petra E; Alexander-Bloch, Aaron F; Gogtay, Nitin; Giedd, Jay N; Rapoport, Judith L; Bullmore, Edward T
2012-04-10
Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.
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...
Brain networks shaping religious belief.
Kapogiannis, Dimitrios; Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P; Grafman, Jordan Henry
2014-02-01
We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal
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
Directory of Open Access Journals (Sweden)
Qiuxiang Bian
2013-01-01
Full Text Available 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.
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.
Bit Level Synchronized MAC Protocol for Multireader RFID Networks
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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
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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.
Scale-Free Brain Functional Networks
Eguíluz, Victor M.; Chialvo, Dante R.; Cecchi, Guillermo A.; Baliki, Marwan; Apkarian, A. Vania
2005-01-01
Functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that (a)the distribution of functional connections, and the probability of finding a link versus distance are both scale-free, (b)the characteristic path length is small and comparable with those of equivalent random networks, and (c)the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small-world networks, reflect important functional information about brain states.
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
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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
Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen
2015-01-01
Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence.
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.
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.
Lü, Ling; Chen, Liansong; Bai, Suyuan; Li, Gang
2016-10-01
We propose a novel scheme to achieve synchronization tracking of uncertain discrete network with spatiotemporal chaos behaviors. In this work, the traditional method of sliding mode control is firstly modified for researching conveniently the synchronization tracking of uncertain discrete network. Further, the network sliding mode surface and control input are designed, and their effectiveness are analyzed. At the same time, we also design the adaptive law to identify availably the uncertain configuration coefficient of the network sliding mode surface. Finally, an example about the small-world network is considered to illustrate the application and effectiveness of the proposed scheme.
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.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper, we investigate coherence resonance (CR) and noise-induced synchronization in Hindmarsh-Rose (HR) neural network with three different types of topologies: regular, random, and small-world. It is found that the additive noise can induce CR in HR neural network with different topologies and its coherence is optimized by a proper noise level. It is also found that as coupling strength increases the plateau in the measure of coherence curve becomes broadened and the effects of network topology is more pronounced simultaneously. Moreover, we find that increasing tie probability p of the network topology leads to an enhancement of noise-induced synchronization in HR neurons network.
Synchronization of unidirectional time delay chaotic networks and the greatest common divisor
Kanter, I; Englert, A; Geissler, F; Kinzel, W; 10.1209/0295-5075/93/60003
2011-01-01
We present the interplay between synchronization of unidirectional coupled chaotic nodes with heterogeneous delays and the greatest common divisor (GCD) of loops composing the oriented graph. In the weak chaos region and for GCD=1 the network is in chaotic zero-lag synchronization, whereas for GCD=m>1 synchronization of m-sublattices emerges. Complete synchronization can be achieved when all chaotic nodes are influenced by an identical set of delays and in particular for the limiting case of homogeneous delays. Results are supported by simulations of chaotic systems, self-consistent and mixing arguments, as well as analytical solutions of Bernoulli maps.
Synchronization of unidirectional time delay chaotic networks and the greatest common divisor
Kanter, I.; Zigzag, M.; Englert, A.; Geissler, F.; Kinzel, W.
2011-03-01
We present the interplay between synchronization of unidirectional coupled chaotic nodes with heterogeneous delays and the greatest common divisor (GCD) of loops composing the oriented graph. In the weak-chaos region and for GCD=1 the network is in chaotic zero-lag synchronization, whereas for GCD=m>1 synchronization of m-sublattices emerges. Complete synchronization can be achieved when all chaotic nodes are influenced by an identical set of delays and in particular for the limiting case of homogeneous delays. Results are supported by simulations of chaotic systems, self-consistent and mixing arguments, as well as analytical solutions of Bernoulli maps.
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.
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.
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.
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.
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.
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.
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. PMID:27575164
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.
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...
Synchronization criteria for coupled Hopfield neural networks with time-varying delays
Institute of Scientific and Technical Information of China (English)
M.J. Park; O.M. Kwon; Ju H. Park; S.M. Lee; E.J. Cha
2011-01-01
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.
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
Projective synchronization of a complex network with different fractional order chaos nodes
Institute of Scientific and Technical Information of China (English)
wang Ming-Jun; wang Xing-Yuan; Niu Yu-Jun
2011-01-01
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 system, Liu system and Coullet system are chosen as examples to show the effectiveness of the scheme.
Projective lag synchronization in drive-response dynamical networks via hybrid feedback control
Al-Mahbashi, Ghada; Noorani, Mohd Salmi Md; Bakar, Sakhinah Abu
2015-09-01
This paper investigates projective lag synchronization (PLS) behavior in drive-response dynamical networks (DRDNs) model with non-identical reference node. Based on Lyapunov stability theory and hybrid feedback control method the problem of PLS with mismatch terms is solved. 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. Moreover, the numerical simulations results demonstrate the validity of the proposed method.
Synchronization of time-delay chaotic systems on small-world networks with delayed coupling
Institute of Scientific and Technical Information of China (English)
Qi Wei; Wang Ying-Hai
2009-01-01
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.
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.
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
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
Institute of Scientific and Technical Information of China (English)
LUO Yabo; ZHENG Li; ZOU Shiwei; ZHAO Liang
2006-01-01
Based on the analysis on the previous research in virtual manufacturing and virtual enterprises, this paper provides a novel architecture of networked manufacturing system around the cooperative design. The key technologies for synchronous cooperative design in networked manufacturing platform, such as the cooperative mechanism, cooperative rules, control authority conveyed, cooperative efficiency, are detailed, with which a synchronous cooperative design system is developed. Due to the cooperative efficiency is the major bottleneck of the synchronous cooperative design over Internet, this research details the test and experiment to demonstrate the practicality of the system. Finally the advantages of the system are illustrated compared with current software tools.
Kada, Hisashi; Teramae, Jun-Nosuke; Tokuda, Isao T.
2016-01-01
Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons. The previous studies, however, have not considered highly non-random features of the synaptic connectivity, namely, bidirectional connections between cortical neurons are more common than expected by chance and strengths of synapses are positively correlated between pre- and postsynaptic neurons. The positive correlation of synaptic connections may destabilize asynchronous activity of networks with the long-tailed synaptic distribution and induce pathological synchronized firing among neurons. It remains unclear how the cortical network avoids such pathological synchronization. Here, we demonstrate that introduction of the correlated connections indeed gives rise to synchronized firings in a cortical network model with the long-tailed distribution. By using a simplified feed-forward network model of spiking neurons, we clarify the underlying mechanism of the synchronization. We then show that the synchronization can be efficiently suppressed by highly heterogeneous distribution, typically a lognormal distribution, of inhibitory-to-excitatory connection strengths in a recurrent network model of cortical neurons. PMID:27803659
Network Assemblies in the Functional Brain
Sepulcre, Jorge; Sabuncu, Mert R.; Johnson, Keith A.
2012-01-01
Purpose of review This review focuses on recent advances in functional connectivity MRI and renewed interest in knowing the large-scale functional network assemblies in the brain. We also consider some methodological aspects of graph theoretical analysis. Recent findings Network science applied to neuroscience is quickly growing in recent years. The characterization of the functional connectomes in normal and pathological brain conditions is now a priority for researchers in the neuropsychiatric field and current findings have provided new insights regarding the pivotal role of network epicenters and specific configurations of the functional networks in the brain. Summary Functional connectivity and its analytical tools are providing organization of the functional brain that will be key for the understanding of pathologies in neurology. PMID:22766721
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 ...
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.
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
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.
Punetha, Nirmal; Ramaswamy, Ramakrishna; Atay, Fatihcan M
2015-04-01
We study synchronization in bipartite networks of phase oscillators with general nonlinear coupling and distributed time delays. Phase-locked solutions are shown to arise, where the oscillators in each partition are perfectly synchronized among themselves but can have a phase difference with the other partition, with the phase difference necessarily being either zero or π radians. Analytical conditions for the stability of both types of solutions are obtained and solution branches are explicitly calculated, revealing that the network can have several coexisting stable solutions. With increasing value of the mean delay, the system exhibits hysteresis, phase flips, final state sensitivity, and an extreme form of multistability where the numbers of stable in-phase and antiphase synchronous solutions with distinct frequencies grow without bound. The theory is applied to networks of Landau-Stuart and Rössler oscillators and shown to accurately predict both in-phase and antiphase synchronous behavior in appropriate parameter ranges.
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.
Stability and synchronization analysis of inertial memristive neural networks with time delays.
Rakkiyappan, R; Premalatha, S; Chandrasekar, A; Cao, Jinde
2016-10-01
This paper is concerned with the problem of stability and pinning synchronization of a class of inertial memristive neural networks with time delay. In contrast to general inertial neural networks, inertial memristive neural networks is applied to exhibit the synchronization and stability behaviors due to the physical properties of memristors and the differential inclusion theory. By choosing an appropriate variable transmission, the original system can be transformed into first order differential equations. Then, several sufficient conditions for the stability of inertial memristive neural networks by using matrix measure and Halanay inequality are derived. These obtained criteria are capable of reducing computational burden in the theoretical part. In addition, the evaluation is done on pinning synchronization for an array of linearly coupled inertial memristive neural networks, to derive the condition using matrix measure strategy. Finally, the two numerical simulations are presented to show the effectiveness of acquired theoretical results. PMID:27668022
Stability and synchronization analysis of inertial memristive neural networks with time delays.
Rakkiyappan, R; Premalatha, S; Chandrasekar, A; Cao, Jinde
2016-10-01
This paper is concerned with the problem of stability and pinning synchronization of a class of inertial memristive neural networks with time delay. In contrast to general inertial neural networks, inertial memristive neural networks is applied to exhibit the synchronization and stability behaviors due to the physical properties of memristors and the differential inclusion theory. By choosing an appropriate variable transmission, the original system can be transformed into first order differential equations. Then, several sufficient conditions for the stability of inertial memristive neural networks by using matrix measure and Halanay inequality are derived. These obtained criteria are capable of reducing computational burden in the theoretical part. In addition, the evaluation is done on pinning synchronization for an array of linearly coupled inertial memristive neural networks, to derive the condition using matrix measure strategy. Finally, the two numerical simulations are presented to show the effectiveness of acquired theoretical results.
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...
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...
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 ...
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
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.
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.
Zhang, Guodong; Shen, Yi
2014-07-01
This paper investigates the exponential synchronization of coupled memristor-based chaotic neural networks with both time-varying delays and general activation functions. And here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new criteria ensuring exponential synchronization of two memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the new proposed results here are very easy to verify and also complement, extend the earlier publications. Numerical simulations on the chaotic systems are presented to illustrate the effectiveness of the theoretical results.
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.
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.
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
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.
Wille, Carolin; Lehnert, Judith; Schöll, Eckehard
2014-09-01
We investigate the combined effects of distributed delay and the balance between excitatory and inhibitory nodes on the stability of synchronous oscillations in a network of coupled Stuart-Landau oscillators. To this end a symmetric network model is proposed for which the stability can be investigated analytically. It is found that beyond a critical inhibition ratio, synchronization tends to be unstable. However, increasing distributional widths can counteract this trend, leading to multiple resynchronization transitions at relatively high inhibition ratios. The extended applicability of the results is confirmed by numerical studies on asymmetrically perturbed network topologies. All investigations are performed on two distribution types, a uniform distribution and a Γ distribution.
Dynamic synchronization of a time-evolving optical network of chaotic oscillators.
Cohen, Adam B; Ravoori, Bhargava; Sorrentino, Francesco; Murphy, Thomas E; Ott, Edward; Roy, Rajarshi
2010-12-01
We present and experimentally demonstrate a technique for achieving and maintaining a global state of identical synchrony of an arbitrary network of chaotic oscillators even when the coupling strengths are unknown and time-varying. At each node an adaptive synchronization algorithm dynamically estimates the current strength of the net coupling signal to that node. We experimentally demonstrate this scheme in a network of three bidirectionally coupled chaotic optoelectronic feedback loops and we present numerical simulations showing its application in larger networks. The stability of the synchronous state for arbitrary coupling topologies is analyzed via a master stability function approach.
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
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.
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.
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.
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
Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators.
Nakano, H; Saito, T
2004-09-01
This paper studies a pulse-coupled network consisting of simple chaotic spiking oscillators (CSOs). If a unit oscillator and its neighbor(s) have (almost) the same parameter values, they exhibit in-phase synchronization of chaos. As the parameter values differ, they exhibit asynchronous phenomena. Based on such behavior, some synchronous groups appear partially in the network. Typical phenomena are verified in the laboratory via a simple test circuit. These phenomena can be evaluated numerically by using an effective mapping procedure. We then apply the proposed network to image segmentation. Using a lattice pulse-coupled network via grouping synchronous phenomena, the input image data can be segmented into some sub-regions.
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.
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.
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.
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.
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.
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.
Dynamic range in the C. elegans brain network
Antonopoulos, Chris G.
2016-01-01
We study external electrical perturbations and their responses in the brain dynamic network of the Caenorhabditis elegans soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a realistic experiment where one stimulates externally specific parts of the brain and studies the persistent neural activity triggered in other cortical regions. In this work, we perturb groups of neurons that form communities, identified by the walktrap community detection method, by trains of stereotypical electrical Poissonian impulses and study the propagation of neural activity to other communities by measuring the corresponding dynamic ranges and Steven law exponents. We show that when one perturbs specific communities, keeping the rest unperturbed, the external stimulations are able to propagate to some of them but not to all. There are also perturbations that do not trigger any response. We found that this depends on the initially perturbed community. Finally, we relate our findings for the former cases with low neural synchronization, self-criticality, and large information flow capacity, and interpret them as the ability of the brain network to respond to external perturbations when it works at criticality and its information flow capacity becomes maximal.
Synchronization of Nonlinear Oscillators Over Networks with Dynamic Links
De Persis, Claudio
2015-01-01
In this paper we investigate the problem of synchronization of homogeneous nonlinear oscillators coupled by dynamic links. The output of the nonlinear oscillators is the input to the dynamic links, while the output of these dynamics links is the quantity available to the distributed controllers at t
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....
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
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.
Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
Directory of Open Access Journals (Sweden)
Huaiqin Wu
2013-01-01
Full Text Available This paper investigates the complete periodic synchronization of memristor-based neural networks with time-varying delays. Firstly, under the framework of Filippov solutions, by using M-matrix theory and the Mawhin-like coincidence theorem in set-valued analysis, the existence of the periodic solution for the network system is proved. Secondly, complete periodic synchronization is considered for memristor-based neural networks. According to the state-dependent switching feature of the memristor, the error system is divided into four cases. Adaptive controller is designed such that the considered model can realize global asymptotical synchronization. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.
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.
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...
Local synchronization of chaotic neural networks with sampled-data and saturating actuators.
Wu, Zheng-Guang; Shi, Peng; Su, Hongye; Chu, Jian
2014-12-01
This paper investigates the problem of local synchronization of chaotic neural networks with sampled-data and actuator saturation. A new time-dependent Lyapunov functional is proposed for the synchronization error systems. The advantage of the constructed Lyapunov functional lies in the fact that it is positive definite at sampling times but not necessarily between sampling times, and makes full use of the available information about the actual sampling pattern. A local stability condition of the synchronization error systems is derived, based on which a sampled-data controller with respect to the actuator saturation is designed to ensure that the master neural networks and slave neural networks are locally asymptotically synchronous. Two optimization problems are provided to compute the desired sampled-data controller with the aim of enlarging the set of admissible initial conditions or the admissible sampling upper bound ensuring the local synchronization of the considered chaotic neural networks. A numerical example is used to demonstrate the effectiveness of the proposed design technique.
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
Synchronicity from Synchronized Chaos
Directory of Open Access Journals (Sweden)
Gregory S. Duane
2015-03-01
Full Text Available The synchronization of loosely-coupled chaotic oscillators, a phenomenon investigated intensively for the last two decades, may realize the philosophical concept of “synchronicity”—the commonplace notion that related eventsmysteriously occur at the same time. When extended to continuous media and/or large discrete arrays, and when general (non-identical correspondences are considered between states, intermittent synchronous relationships indeed become ubiquitous. Meaningful synchronicity follows naturally if meaningful events are identified with coherent structures, defined by internal synchronization between remote degrees of freedom; a condition that has been posited as necessary for synchronizability with an external system. The important case of synchronization between mind and matter is realized if mind is analogized to a computer model, synchronizing with a sporadically observed system, as in meteorological data assimilation. Evidence for the ubiquity of synchronization is reviewed along with recent proposals that: (1 synchronization of different models of the same objective process may be an expeditious route to improved computational modeling and may also describe the functioning of conscious brains; and (2 the nonlocality in quantum phenomena implied by Bell’s theorem may be explained in a variety of deterministic (hidden variable interpretations if the quantum world resides on a generalized synchronization “manifold”.
Resting network plasticity following brain injury.
Directory of Open Access Journals (Sweden)
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.
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
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
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:1neural networks. Finally, three numerical examples are presented to show the effectiveness of our proposed theoretical results.
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.
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.
Impact of Loss Synchronization on Reliable High Speed Networks: A Model Based Simulation
Directory of Open Access Journals (Sweden)
Suman Kumar
2014-01-01
Full Text Available Contemporary nature of network evolution demands for simulation models which are flexible, scalable, and easily implementable. In this paper, we propose a fluid based model for performance analysis of reliable high speed networks. In particular, this paper aims to study the dynamic relationship between congestion control algorithms and queue management schemes, in order to develop a better understanding of the causal linkages between the two. We propose a loss synchronization module which is user configurable. We validate our model through simulations under controlled settings. Also, we present a performance analysis to provide insights into two important issues concerning 10 Gbps high speed networks: (i impact of bottleneck buffer size on the performance of 10 Gbps high speed network and (ii impact of level of loss synchronization on link utilization-fairness tradeoffs. The practical impact of the proposed work is to provide design guidelines along with a powerful simulation tool to protocol designers and network developers.
Institute of Scientific and Technical Information of China (English)
P. Balasubramaniam; M. Kalpana; R. Rakkiyappan
2012-01-01
Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs).Each cell in an FCNN contains fuzzy operating abilities.The entire network is governed by cellular computing laws.The design of FCNNs is based on fuzzy local rules.In this paper,a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated.Mixed delays include discrete time-varying delays and unbounded distributed delays.A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network.By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs.The controller can be easily obtained by solving the derived LMIs.A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.
Consciousness, cognition and brain networks: New perspectives.
Aldana, E M; Valverde, J L; Fábregas, N
2016-10-01
A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks.
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.
Average Synchronization and Temporal Order in a Noisy Neuronal Network with Coupling Delay
Institute of Scientific and Technical Information of China (English)
WANG Qing-Yun; DUAN Zhi-Sheng; LU Qi-Shao
2007-01-01
Average synchronization and temporal order characterized by the rate of firing are studied in a spatially extended network system with the coupling time delay, which is locally modelled by a two-dimensional Rulkov map neuron.It is shown that there exists an optimal noise level, where average synchronization and temporal order are maximum irrespective of the coupling time delay. Furthermore, it is found that temporal order is weakened when the coupling time delay appears. However, the coupling time delay has a twofold effect on average synchronization,one associated with its increase, the other with its decrease. This clearly manifests that random perturbations and time delay play a complementary role in synchronization and temporal order.
Achuthan, Srisairam; Canavier, Carmen C
2009-04-22
Networks of model neurons were constructed and their activity was predicted using an iterated map based solely on the phase-resetting curves (PRCs). The predictions were quite accurate provided that the resetting to simultaneous inputs was calculated using the sum of the simultaneously active conductances, obviating the need for weak coupling assumptions. Fully synchronous activity was observed only when the slope of the PRC at a phase of zero, corresponding to spike initiation, was positive. A novel stability criterion was developed and tested for all-to-all networks of identical, identically connected neurons. When the PRC generated using N-1 simultaneously active inputs becomes too steep, the fully synchronous mode loses stability in a network of N model neurons. Therefore, the stability of synchrony can be lost by increasing the slope of this PRC either by increasing the network size or the strength of the individual synapses. Existence and stability criteria were also developed and tested for the splay mode in which neurons fire sequentially. Finally, N/M synchronous subclusters of M neurons were predicted using the intersection of parameters that supported both between-cluster splay and within-cluster synchrony. Surprisingly, the splay mode between clusters could enforce synchrony on subclusters that were incapable of synchronizing themselves. These results can be used to gain insights into the activity of networks of biological neurons whose PRCs can be measured.
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
Structural covariance networks in the mouse brain.
Pagani, Marco; Bifone, Angelo; Gozzi, Alessandro
2016-04-01
The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks.
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.
Changes in cognitive state alter human functional brain networks
Directory of Open Access Journals (Sweden)
Malaak Nasser Moussa
2011-08-01
Full Text Available The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network (DMN. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation. Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.
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
Directory of Open Access Journals (Sweden)
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.
Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile
2015-02-01
Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.
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...
Directory of Open Access Journals (Sweden)
Dahlia Sam
2014-01-01
Full Text Available VANET is a type of ad hoc network in which the moving vehicles act as nodes. There has been lot of research for using VANETs in many applications. One of the main applications is the use of VANETs to improve driving safety. In any safety related applications, the vehicular nodes have to constantly communicate with each other and the roadside equipments. For e.g., the roadside units sense real time information about road conditions, road blocks or animals straying on the road and passes the message to the approaching vehicles. The alert message enables the driver to take timely decisions in preventing accidents or delay. However there are two issues in the above system. One problem is that VANETs are subject to frequent network disconnections especially in low traffic areas. Due to this some events in the road may go undetected while the detected events may not be transmitted on time. The second issue it with maintaining a synchronized clock within the network. Only then the messages communicated between the nodes will be meaningful. To overcome the above issues and make the system more reliable we propose to include roadside wireless sensor nodes along with the vehicular nodes in the network. The roadside wireless sensor nodes can be deployed at fixed distances and communicate wirelessly with the vehicular nodes. They play an important role in keeping the network connected and guarantee message transmission. We also propose a Hybrid Clock Synchronization (HCS algorithm to synchronize the clocks of all the nodes. This integrated network which is also time synchronized is called the Hybrid VANET (H-VANET. The proposed H-VANET was simulated and tested using GrooveNet. On comparing our system with the conventional VANET implementing RBS for synchronization, it was seen that our model has better performance and reliability.
Georgopoulos, Apostolos P.; Karageorgiou, Elissaios; Leuthold, Arthur C.; Lewis, Scott M.; Lynch, Joshua K.; Alonso, Aurelio A.; Aslam, Zaheer; Carpenter, Adam F.; Georgopoulos, Angeliki; Hemmy, Laura S.; Koutlas, Ioannis G.; Langheim, Frederick J. P.; Riley McCarten, J.; McPherson, Susan E.; Pardo, José V.; Pardo, Patricia J.; Parry, Gareth J.; Rottunda, Susan J.; Segal, Barbara M.; Sponheim, Scott R.; Stanwyck, John J.; Stephane, Massoud; Westermeyer, Joseph J.
2007-12-01
We report on a test to assess the dynamic brain function at high temporal resolution using magnetoencephalography (MEG). The essence of the test is the measurement of the dynamic synchronous neural interactions, an essential aspect of the brain function. MEG signals were recorded from 248 axial gradiometers while 142 human subjects fixated a spot of light for 45-60 s. After fitting an autoregressive integrative moving average (ARIMA) model and taking the stationary residuals, all pairwise, zero-lag, partial cross-correlations (PCCij0) and their z-transforms (zij0) between i and j sensors were calculated, providing estimates of the strength and sign (positive, negative) of direct synchronous coupling at 1 ms temporal resolution. We found that subsets of zij0 successfully classified individual subjects to their respective groups (multiple sclerosis, Alzheimer's disease, schizophrenia, Sjögren's syndrome, chronic alcoholism, facial pain, healthy controls) and gave excellent external cross-validation results. Contribution by the authors: Designed research (APG); acquired data (AAA, IGK, FJPL, ACL, SML, JJS); analyzed data (APG, EK, ACL, JKL); wrote the paper (APG, EK, ACL, SML); contributed subjects (AAA, ZA, AFC, AG, LSH, IGK, FJPL, SML, JRM, SEM, JVP, PJP, GJP, SJR, BMS, SRS, MS, JJS, JJW); discussed results (All); contributed equally (ZA, AFC, AG, LSH, FJPL, JRM, SEM, JVP, PJP, GJP, SJR, BMS, SRS, MS, JJS, JJW).
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.
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
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.
Li, Huaqing; Liao, Xiaofeng; Chen, Guo; Hill, David J; Dong, Zhaoyang; Huang, Tingwen
2015-06-01
This paper presents a new framework for synchronization of complex network by introducing a mechanism of event-triggering distributed sampling information. A kind of event which avoids continuous communication between neighboring nodes is designed to drive the controller update of each node. The advantage of the event-triggering strategy is the significant decrease of the number of controller updates for synchronization task of complex networks involving embedded microprocessors with limited on-board resources. To describe the system's ability reaching synchronization, a concept about generalized algebraic connectivity is introduced for strongly connected networks and then extended to the strongly connected components of the directed network containing a directed spanning tree. Two sufficient conditions are presented to reveal the underlying relationships of corresponding parameters to reach global synchronization based on algebraic graph, matrix theory and Lyapunov control method. A positive lower bound for inter-event times is derived to guarantee the absence of Zeno behavior. Finally, a numerical simulation example is provided to demonstrate the theoretical results.
Pinning Synchronization of Delayed Neural Networks with Nonlinear Inner-Coupling
Directory of Open Access Journals (Sweden)
Yangling Wang
2011-01-01
Full Text Available Without assuming the symmetry and irreducibility of the outer-coupling weight configuration matrices, we investigate the pinning synchronization of delayed neural networks with nonlinear inner-coupling. Some delay-dependent controlled stability criteria in terms of linear matrix inequality (LMI are obtained. An example is presented to show the application of the criteria obtained in this paper.
Wang, Qi; Gong, Yubing; Wu, Yanan
2015-11-01
Introducing adaptive coupling in delayed neuronal networks and regulating the dissipative parameter (DP) of adaptive coupling by noise, we study the effect of fluctuations of the changing rate of adaptive coupling on the synchronization of the neuronal networks. It is found that time delay can induce synchronization transitions for intermediate DP values, and the synchronization transitions become strongest when DP is optimal. As the intensity of DP noise is varied, the neurons can also exhibit synchronization transitions, and the phenomenon is delay-dependent and is enhanced for certain time delays. Moreover, the synchronization transitions change with the change of DP and become strongest when DP is optimal. These results show that randomly changing adaptive coupling can considerably change the synchronization of the neuronal networks, and hence could play a crucial role in the information processing and transmission in neural systems.
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...
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 ...
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.
Sensor Network Localization by Eigenvector Synchronization Over the Euclidean Group.
Cucuringu, Mihai; Lipman, Yaron; Singer, Amit
2012-07-01
We present a new approach to localization of sensors from noisy measurements of a subset of their Euclidean distances. Our algorithm starts by finding, embedding, and aligning uniquely realizable subsets of neighboring sensors called patches. In the noise-free case, each patch agrees with its global positioning up to an unknown rigid motion of translation, rotation, and possibly reflection. The reflections and rotations are estimated using the recently developed eigenvector synchronization algorithm, while the translations are estimated by solving an overdetermined linear system. The algorithm is scalable as the number of nodes increases and can be implemented in a distributed fashion. Extensive numerical experiments show that it compares favorably to other existing algorithms in terms of robustness to noise, sparse connectivity, and running time. While our approach is applicable to higher dimensions, in the current article, we focus on the two-dimensional case.
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
Functional brain networks in schizophrenia: a review
Directory of Open Access Journals (Sweden)
Vince D Calhoun
2009-08-01
Full Text Available Functional magnetic resonance imaging (fMRI has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their
Timme, Marc; Geisel, Theo; Wolf, Fred
2006-03-01
We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general nonstandard solution to the multioperator problem. Subsequently, we derive a class of neuronal rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate-and-fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distributions of the stability operators. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e., finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity. Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.
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.
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.
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
Directory of Open Access Journals (Sweden)
Xiaojian Hu
2015-01-01
Full Text Available 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.
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.
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.
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.
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.
Tang, Ze; Park, Ju H.; Lee, Tae H.
2016-10-01
This paper is devoted to the cluster synchronization issue of nonlinearly coupled Lur'e networks under the distributed adaptive pinning control strategy. The time-varying delayed networks consisted of identical and nonidentical Lur'e systems are discussed respectively by applying the edge-based pinning control scheme. In each cluster, the edges belonging to the spanning tree are pinned. In view of the nonlinearly couplings of the networks, for the first time, an efficient distributed nonlinearly adaptive update law based on the local information of the dynamical behaviors of node is proposed. Sufficient criteria for the achievement of cluster synchronization are derived based on S-procedure, Kronecker product and Lyapunov stability theory. Additionally, some illustrative examples are provided to demonstrate the effectiveness of the theoretical results.
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
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...
Physical layer one-time-pad data encryption through synchronized semiconductor laser networks
Argyris, Apostolos; Pikasis, Evangelos; Syvridis, Dimitris
2016-02-01
Semiconductor lasers (SL) have been proven to be a key device in the generation of ultrafast true random bit streams. Their potential to emit chaotic signals under conditions with desirable statistics, establish them as a low cost solution to cover various needs, from large volume key generation to real-time encrypted communications. Usually, only undemanding post-processing is needed to convert the acquired analog timeseries to digital sequences that pass all established tests of randomness. A novel architecture that can generate and exploit these true random sequences is through a fiber network in which the nodes are semiconductor lasers that are coupled and synchronized to central hub laser. In this work we show experimentally that laser nodes in such a star network topology can synchronize with each other through complex broadband signals that are the seed to true random bit sequences (TRBS) generated at several Gb/s. The potential for each node to access real-time generated and synchronized with the rest of the nodes random bit streams, through the fiber optic network, allows to implement an one-time-pad encryption protocol that mixes the synchronized true random bit sequence with real data at Gb/s rates. Forward-error correction methods are used to reduce the errors in the TRBS and the final error rate at the data decoding level. An appropriate selection in the sampling methodology and properties, as well as in the physical properties of the chaotic seed signal through which network locks in synchronization, allows an error free performance.
Three-dimensional network of Drosophila brain hemisphere
Mizutani, Ryuta; Saiga, Rino; 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-d...
Speed Tracking of Field Oriented Control Permanent Magnet Synchronous Motor Using Neural Network
Directory of Open Access Journals (Sweden)
Wahyu Mulyo Utomo
2014-05-01
Full Text Available The field oriented control theory and space vector pulse width modulation technique make a permanent magnet synchronous motor can achieve the performance as well as a DC motor. However, due to the nonlinearity of the permanent magnet synchronous motor drive characteristics, it is difficult to control by using conventional proportional-integral-derivative controller. By this reason in this paper an online neural network controller for the permanent magnet synchronous motor is proposed. The controller is designed to tracks variations of speed references and also during load disturbance. The effectiveness of the proposed method is verified by develop simulation model in MATLAB-simulink program. The simulation results show that the proposed controller can reduce the overshoot, settling time and rise time. It can be concluded that the performance of the controller is improved.
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.
Resonance, synchronization, and lexical redundancy in the expanding dynamics of brain stem neurons
Mandell, Arnold J.; Selz, Karen A.
1993-11-01
Interspike interval patterns of brain stem neurons that project directly or indirectly to much of the neocortex interactively influence electroencephalographically-defined states of consciousness and modulate patterns of temporal-spatial coherence, `binding,' in cortical field potential oscillations. Neurochemical classes of brain stem neurons manifest discriminable dynamical characteristics apart from the statistics of their firing rates. These sequences of interspike intervals are not well described by either harmonic functions or the Poisson statistics of renewal processes. We cast these patterns within the context of information bearing processes by using moment partitions and symbolic dynamics. We describe the expanding behavior of model and real brain stem neurons in relationship to states of resonance (the presence of complex singularities in the power spectrum with amplitudes related to the persistence of unstable fixed points in the nonexponential decay of correlations), synchronization (how closely the measure of maximal entropy comes to equaling the Sinai- Ruelle-Bowen area measure), and lexical redundancy (as repetitions of symbol subsequences).
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
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.
Institute of Scientific and Technical Information of China (English)
Wang Shu-Guo; Zheng Song
2013-01-01
We investigate the problem of function projective synchronization (FPS) in drive-response dynamical networks with non-identical nodes.An adaptive controller is proposed for the FPS of complex dynamical networks with uncertain parameters and disturbance.Not only are the unknown parameters of the networks estimated by the adaptive laws obtained from the Lyapunov stability theory and Taylor expansions,but the unknown bounded disturbances are also simultaneously conquered by the proposed control.Finally,a numerical simulation is provided to illustrate the feasibility and effectiveness of the obtained result.
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.
Synchronization control of memristor-based recurrent neural networks with perturbations.
Wang, Weiping; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian
2014-05-01
In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results.
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...
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.
Siebenhühner, Felix; Wang, Sheng H; Palva, J Matias; Palva, Satu
2016-01-01
Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions. DOI: http://dx.doi.org/10.7554/eLife.13451.001 PMID:27669146
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.
Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie
2016-10-01
Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.
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
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.
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.
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.
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.
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.
Noise-induced synchronous stochastic oscillations in small scale cultured heart-cell networks
Institute of Scientific and Technical Information of China (English)
Yuan Lan; Liu Zhi-Qiang; Zhang Hui-Min; Ding Xue-Li; Yang Ming-Hao; Gu Hua-Guang; Ren Wei
2011-01-01
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 sub-threshold 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.
Adaptive synchronization in delay-coupled networks of Stuart-Landau oscillators.
Selivanov, Anton A; Lehnert, Judith; Dahms, Thomas; Hövel, Philipp; Fradkov, Alexander L; Schöll, Eckehard
2012-01-01
We consider networks of delay-coupled Stuart-Landau oscillators. In these systems, the coupling phase has been found to be a crucial control parameter. By proper choice of this parameter one can switch between different synchronous oscillatory states of the network. Applying the speed-gradient method, we derive an adaptive algorithm for an automatic adjustment of the coupling phase such that a desired state can be selected from an otherwise multistable regime. We propose goal functions based on both the difference of the oscillators and a generalized order parameter and demonstrate that the speed-gradient method allows one to find appropriate coupling phases with which different states of synchronization, e.g., in-phase oscillation, splay, or various cluster states, can be selected.
Directory of Open Access Journals (Sweden)
Cao Jinde
2011-01-01
Full Text Available Abstract In this paper, an integral sliding mode control approach is presented to investigate synchronization of nonidentical chaotic neural networks with discrete and distributed time-varying delays as well as leakage delay. By considering a proper sliding surface and constructing Lyapunov-Krasovskii functional, as well as employing a combination of the free-weighting matrix method, Newton-Leibniz formulation and inequality technique, a sliding mode controller is designed to achieve the asymptotical synchronization of the addressed nonidentical neural networks. Moreover, a sliding mode control law is also synthesized to guarantee the reachability of the specified sliding surface. The provided conditions are expressed in terms of linear matrix inequalities, and are dependent on the discrete and distributed time delays as well as leakage delay. A simulation example is given to verify the theoretical results.
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.
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.
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
Synchronized Multimedia Streaming on the iPhone Platform with Network Coding
DEFF Research Database (Denmark)
Vingelmann, Peter; Fitzek, Frank; Pedersen, Morten Videbæk;
2011-01-01
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....
Akrami, Amin; Ghaderi, Mohammad; Ghadi, Saeed
2010-01-01
Energizing the power transformers usually results in flowing very high inrush currents. This harmful current can be minimized using controlled switching and considering the value of residual flux. But nowadays, developing the ground power networks results in high increment of ferroresonance phenomenon occurrence due to the line' capacitance reactance and nonlinear inductive reactance of power transformer's core. In this study, these transient phenomena and their cause have studied synchronously.
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
Directory of Open Access Journals (Sweden)
Dong Chao-Yi
2012-03-01
Full Text Available Abstract Background Synchronized bursting activity (SBA is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i In a network with an excitatory ratio (ER of 80-90%, its connective ratio (CR was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30% optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA.
The effect of distributed time-delays on the synchronization of neuronal networks
Kachhvah, Ajay Deep
2016-01-01
Here we investigate the synchronization of networks of FitzHugh-Nagumo neurons coupled in scale-free, small-world and random topologies, in the presence of distributed time delays in the coupling of neurons. We explore how the synchronization transition is affected when the time delays in the interactions between pairs of interacting neurons are non-uniform. We find that the presence of distributed time-delays does not change the behavior of the synchronization transition significantly, vis-a-vis networks with constant time-delay, where the value of the constant time-delay is the mean of the distributed delays. We also notice that a normal distribution of delays gives rise to a transition at marginally lower coupling strengths, vis-a-vis uniformly distributed delays. These trends hold across classes of networks and for varying standard deviations of the delay distribution, indicating the generality of these results. So we conclude that distributed delays, which may be typically expected in real-world situatio...
Picallo, Clara B.; Riecke, Hermann
2011-03-01
Motivated by recent observations in neuronal systems we investigate all-to-all networks of nonidentical oscillators with adaptive coupling. The adaptation models spike-timing-dependent plasticity in which the sum of the weights of all incoming links is conserved. We find multiple phase-locked states that fall into two classes: near-synchronized states and splay states. Among the near-synchronized states are states that oscillate with a frequency that depends only very weakly on the coupling strength and is essentially given by the frequency of one of the oscillators, which is, however, neither the fastest nor the slowest oscillator. In sufficiently large networks the adaptive coupling is found to develop effective network topologies dominated by one or two loops. This results in a multitude of stable splay states, which differ in their firing sequences. With increasing coupling strength their frequency increases linearly and the oscillators become less synchronized. The essential features of the two classes of states are captured analytically in perturbation analyses of the extended Kuramoto model used in the simulations.
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.
GPS Time Synchronization in School-Network Cosmic Ray Detectors
Berns, H G; Gran, R; Wilkes, R J; Berns, Hans-Gerd; Burnett, Toby H.; Gran, Richard
2003-01-01
The QuarkNet DAQ card for school-network cosmic ray detectors provides a low-cost alternative to using standard particle and nuclear physics fast pulse electronics modules. Individual detector stations, each consisting of 4 scintillation counter modules, front-end electronics, and a GPS receiver, produce a stream of data in form of ASCII text strings in identifiable set of formats for different functions. The card includes a low-cost GPS receiver module, which permits timestamping event triggers to about 50 nanosecond accuracy in UTC between widely separated sites. The technique used for obtaining precise GPS time employs the 1PPS signal, which is not normally available to users of the commercial GPS module. We had the stock model slightly custom-modified to access this signal. The method for deriving time values was adapted from methods developed for the K2K long-baseline neutrino experiment. Performance of the low-cost GPS module used is compared to that of a more expensive unit with known quality.
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.
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...
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.
Wang, Jian-an; Ma, Xiaohui; Wen, Xinyu; Sun, Qianlai
2016-11-01
This paper develops an intermittent control with two switched periods to study the pinning lag synchronization problem of drive-response complex networks. These two switched periods may have different control rates. By designing appropriate adaptive intermittent pinning controllers and using Lyapunov stability theory, some sufficient conditions for ensuring the lag synchronization between two coupled networks are derived. The minimum number of pinned nodes is determined by node dynamics, coupling strength, inner coupling matrix and a design parameter. Two simple pinning lag synchronization criteria are obtained from the proposed conditions. A numerical example is provided to illustrate the feasibility of the theoretical results.
Emotion-Induced Topological Changes in Functional Brain Networks.
Park, Chang-Hyun; Lee, Hae-Kook; Kweon, Yong-Sil; Lee, Chung Tai; Kim, Ki-Tae; Kim, Young-Joo; Lee, Kyoung-Uk
2016-01-01
In facial expression perception, a distributed network is activated according to stimulus context. We proposed that an interaction between brain activation and stimulus context in response to facial expressions could signify a pattern of interactivity across the whole brain network beyond the face processing network. Functional magnetic resonance imaging data were acquired for 19 young healthy subjects who were exposed to either emotionally neutral or negative facial expressions. We constructed group-wise functional brain networks for 12 face processing areas [bilateral inferior occipital gyri (IOG), fusiform gyri (FG), superior temporal sulci (STS), amygdalae (AMG), inferior frontal gyri (IFG), and orbitofrontal cortices (OFC)] and for 73 whole brain areas, based on partial correlation of mean activation across subjects. We compared the topological properties of the networks with respect to functional distance-based measures, global and local efficiency, between the two types of face stimulus. In both face processing and whole brain networks, global efficiency was lower and local efficiency was higher for negative faces relative to neutral faces, indicating that network topology differed according to stimulus context. Particularly in the face processing network, emotion-induced changes in network topology were attributable to interactions between core (bilateral IOG, FG, and STS) and extended (bilateral AMG, IFG, and OFC) systems. These results suggest that changes in brain activation patterns in response to emotional face stimuli could be revealed as changes in the topological properties of functional brain networks for the whole brain as well as for face processing areas.
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.
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
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.
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.
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.
Ding, Zhixia; Shen, Yi; Wang, Leimin
2016-01-01
This paper is concerned with the global Mittag-Leffler synchronization for a class of fractional-order neural networks with discontinuous activations (FNNDAs). We give the concept of Filippov solution for FNNDAs in the sense of Caputo's fractional derivation. By using a singular Gronwall inequality and the properties of fractional calculus, the existence of global solution under the framework of Filippov for FNNDAs is proved. Based on the nonsmooth analysis and control theory, some sufficient criteria for the global Mittag-Leffler synchronization of FNNDAs are derived by designing a suitable controller. The proposed results enrich and enhance the previous reports. Finally, one numerical example is given to demonstrate the effectiveness of the theoretical results. PMID:26562442
A scheme for synchronizing clocks connected by a packet communication network
dos Santos, R. V.; Monteiro, L. H. A.
2012-07-01
Consider a communication system in which a transmitter equipment sends fixed-size packets of data at a uniform rate to a receiver equipment. Consider also that these equipments are connected by a packet-switched network, which introduces a random delay to each packet. Here we propose an adaptive clock recovery scheme able of synchronizing the frequencies and the phases of these devices, within specified limits of precision. This scheme for achieving frequency and phase synchronization is based on measurements of the packet arrival times at the receiver, which are used to control the dynamics of a digital phase-locked loop. The scheme performance is evaluated via numerical simulations performed by using realistic parameter values.
Qian, Yu
2014-01-01
The synchronization transitions in Newman-Watts small-world neuronal networks (SWNNs) induced by time delay τ and long-range connection (LRC) probability P have been investigated by synchronization parameter and space-time plots. Four distinct parameter regions, that is, asynchronous region, transition region, synchronous region, and oscillatory region have been discovered at certain LRC probability P = 1.0 as time delay is increased. Interestingly, desynchronization is observed in oscillatory region. More importantly, we consider the spatiotemporal patterns obtained in delayed Newman-Watts SWNNs are the competition results between long-range drivings (LRDs) and neighboring interactions. In addition, for moderate time delay, the synchronization of neuronal network can be enhanced remarkably by increasing LRC probability. Furthermore, lag synchronization has been found between weak synchronization and complete synchronization as LRC probability P is a little less than 1.0. Finally, the two necessary conditions, moderate time delay and large numbers of LRCs, are exposed explicitly for synchronization in delayed Newman-Watts SWNNs.
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.
Institute of Scientific and Technical Information of China (English)
Tang Yang; Zhong Hui-Huang; Fang Jian-An
2008-01-01
A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed,which is composed of constant coupling,coupling discrete time-varying delay and coupling distributed timevarying delay.All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion,which reflects a more realistic dynamical behaviour of coupled systems in practice.Based on a simple adaptive feedback controller and stochastic stability theory,several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays.Finally,numerical simulatious illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.
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...
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.
Mapping human whole-brain structural networks with diffusion MRI.
Directory of Open Access Journals (Sweden)
Patric Hagmann
Full Text Available Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.
Brain-on-a-chip integrated neuronal networks
Xie, Sijia
2016-01-01
The brain-on-a-chip technology aims to provide an efficient and economic in vitro platform for brain disease study. In the well-known literature on brain-on-a-chip systems, nonstructured surfaces were conventionally used for the cell attachment in a culture chamber, therefore the neuronal networks g
Zhang, Honghui; Wang, Qingyun; He, Xiaoyan; Chen, Guanrong
2014-01-01
This paper investigates synchronization stability and firing transition in two types of the modified canonical class I neuronal networks, where the short-term plasticity of synapse is introduced. We mainly consider both unidirectional chain and global coupling configurations. Previous studies have shown that the coupled class I neurons can spontaneously de-synchronize. Presently, the short-term plasticity of synapse is considered to check the universality of this phenomenon. Based on the theoretical analysis and numerical simulation, it is shown that unidirectionally chain coupled class I neurons can realize synchronization, whereas bidirectionally coupled chain neurons cannot synchronize, and globally coupled class I neurons de-synchronize. Furthermore, the dynamics of coupled neurons with different firing modes are also studied in numerical simulations, and interesting transitions of different firing modes can be induced by the short-term plasticity. The obtained results can be helpful to further understand important effects of the short-term synaptic plasticity on realistic neuronal systems.
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.
Complex networks: new trends for the analysis of brain connectivity
Chavez, Mario; Latora, Vito; Martinerie, Jacques
2010-01-01
Today, the human brain can be studied as a whole. Electroencephalography, magnetoencephalography, or functional magnetic resonance imaging techniques provide functional connectivity patterns between different brain areas, and during different pathological and cognitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently manage local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy changing neural demands.
Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics
Energy Technology Data Exchange (ETDEWEB)
Metz, F L; Theumann, W K [Instituto de Fisica, Universidade Federal do Rio Grande do Sul, Caixa Postal 15051, 91501-970 Porto Alegre (Brazil)], E-mail: fernando@itf.fys.kuleuven.be, E-mail: theumann@if.ufrgs.br
2009-09-25
The synchronous dynamics and the stationary states of a recurrent attractor neural network model with competing synapses between symmetric sequence processing and Hebbian pattern reconstruction are studied in this work allowing for the presence of a self-interaction for each unit. Phase diagrams of stationary states are obtained exhibiting phases of retrieval, symmetric and period-two cyclic states as well as correlated and frozen-in states, in the absence of noise. The frozen-in states are destabilized by synaptic noise and well-separated regions of correlated and cyclic states are obtained. Excitatory or inhibitory self-interactions yield enlarged phases of fixed-point or cyclic behaviour.
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.
Li, Chaojie; Yu, Wenwu; Huang, Tingwen
2014-06-01
In this paper, a novel impulsive control law is proposed for synchronization of stochastic discrete complex networks with time delays and switching topologies, where average dwell time and average impulsive interval are taken into account. The side effect of time delays is estimated by Lyapunov-Razumikhin technique, which quantitatively gives the upper bound to increase the rate of Lyapunov function. By considering the compensation of decreasing interval, a better impulsive control law is recast in terms of average dwell time and average impulsive interval. Detailed results from a numerical illustrative example are presented and discussed. Finally, some relevant conclusions are drawn.
Energy Technology Data Exchange (ETDEWEB)
Peng, J H; Wu, Y J [School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China); Yu, H J [Department of Mechanics, Shanghai Jiao Tong University, Shanghai 200240 (China)], E-mail: jhpeng@ecust.edu.cn
2008-02-15
Synchronization and associative memory in a neural network composed of the widely discussed FitzHugh-Nagumo neurons is investigated in this paper. Based on the reality of the microscopic biological structure in the neural system, the couplings among those neurons are accompanied with randomly distributed time delays which models the times needed for pulses propagating on the axons from the presynaptic neurons to the postsynaptic neurons. The memory is represented in the spatiotemporal firing pattern of the neurons, and the memory retrieval is accomplished with the fluctuations of the noise in the system.
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.
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
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
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.
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
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)
Application of neural networks for permanent magnet synchronous motor direct torque control
Institute of Scientific and Technical Information of China (English)
Zhang Chunmei; Liu Heping; Chen Shujin; Wang Fangjun
2008-01-01
Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control (DTC) is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.
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...
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...
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.
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
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)
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.
The role of axonal delay in the synchronization of networks of coupled cortical oscillators.
Crook, S M; Ermentrout, G B; Vanier, M C; Bower, J M
1997-04-01
Coupled oscillator models use a single phase variable to approximate the voltage oscillation of each neuron during repetitive firing where the behavior of the model depends on the connectivity and the interaction function chosen to describe the coupling. We introduce a network model consisting of a continuum of these oscillators that includes the effects of spatially decaying coupling and axonal delay. We derive equations for determining the stability of solutions and analyze the network behavior for two different interaction functions. The first is a sine function, and the second is derived from a compartmental model of a pyramidal cell. In both cases, the system of coupled neural oscillators can undergo a bifurcation from synchronous oscillations to waves. The change in qualitative behavior is due to the axonal delay, which causes distant connections to encourage a phase shift between cells. We suggest that this mechanism could contribute to the behavior observed in several neurobiological systems.