13 resultados para Projective synchronization

em Aston University Research Archive


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This thesis was focused on theoretical models of synchronization to cortical dynamics as measured by magnetoencephalography (MEG). Dynamical systems theory was used in both identifying relevant variables for brain coordination and also in devising methods for their quantification. We presented a method for studying interactions of linear and chaotic neuronal sources using MEG beamforming techniques. We showed that such sources can be accurately reconstructed in terms of their location, temporal dynamics and possible interactions. Synchronization in low-dimensional nonlinear systems was studied to explore specific correlates of functional integration and segregation. In the case of interacting dissimilar systems, relevant coordination phenomena involved generalized and phase synchronization, which were often intermittent. Spatially-extended systems were then studied. For locally-coupled dissimilar systems, as in the case of cortical columns, clustering behaviour occurred. Synchronized clusters emerged at different frequencies and their boundaries were marked through oscillation death. The macroscopic mean field revealed sharp spectral peaks at the frequencies of the clusters and broader spectral drops at their boundaries. These results question existing models of Event Related Synchronization and Desynchronization. We re-examined the concept of the steady-state evoked response following an AM stimulus. We showed that very little variability in the AM following response could be accounted by system noise. We presented a methodology for detecting local and global nonlinear interactions from MEG data in order to account for residual variability. We found crosshemispheric nonlinear interactions of ongoing cortical rhythms concurrent with the stimulus and interactions of these rhythms with the following AM responses. Finally, we hypothesized that holistic spatial stimuli would be accompanied by the emergence of clusters in primary visual cortex resulting in frequency-specific MEG oscillations. Indeed, we found different frequency distributions in induced gamma oscillations for different spatial stimuli, which was suggestive of temporal coding of these spatial stimuli. Further, we addressed the bursting character of these oscillations, which was suggestive of intermittent nonlinear dynamics. However, we did not observe the characteristic-3/2 power-law scaling in the distribution of interburst intervals. Further, this distribution was only seldom significantly different to the one obtained in surrogate data, where nonlinear structure was destroyed. In conclusion, the work presented in this thesis suggests that advances in dynamical systems theory in conjunction with developments in magnetoencephalography may facilitate a mapping between levels of description int he brain. this may potentially represent a major advancement in neuroscience.

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We propose a new simple method to achieve precise symbol synchronization using one start-of-frame (SOF) symbol in optical fast orthogonal frequency-division multiplexing (FOFDM) with subchannel spacing equal to half of the symbol rate per sub-carrier. The proposed method first identifies the SOF symbol, then exploits the evenly symmetric property of the discrete cosine transform in FOFDM, which is also valid in the presence of chromatic dispersion, to achieve precise symbol synchronization. We demonstrate its use in a 16.88-Gb/s phase-shifted-keying-based FOFDM system over a 124-km field-installed single-mode fiber link and show that this technique operates well in automatic precise symbol synchronization at an optical signal-to-noise ratio as low as 3 dB and after transmission.

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Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.

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We present a phase locking scheme that enables the demonstration of a practical dual pump degenerate phase sensitive amplifier for 10 Gbit/s non-return to zero amplitude shift keying signals. The scheme makes use of cascaded Mach Zehnder modulators for creating the pump frequencies as well as of injection locking for extracting the signal carrier and synchronizing the local lasers. An in depth optimization study has been performed, based on measured error rate performance, and the main degradation factors have been identified.

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EEG Hyperscanning is a method for studying two or more individuals simultaneously with the objective of elucidating how co-variations in their neural activity (i.e., hyperconnectivity) are influenced by their behavioral and social interactions. The aim of this study was to compare the performance of different hyper-connectivity measures using (i) simulated data, where the degree of coupling could be systematically manipulated, and (ii) individually recorded human EEG combined into pseudo-pairs of participants where no hyper-connections could exist. With simulated data we found that each of the most widely used measures of hyperconnectivity were biased and detected hyper-connections where none existed. With pseudo-pairs of human data we found spurious hyper-connections that arose because there were genuine similarities between the EEG recorded from different people independently but under the same experimental conditions. Specifically, there were systematic differences between experimental conditions in terms of the rhythmicity of the EEG that were common across participants. As any imbalance between experimental conditions in terms of stimulus presentation or movement may affect the rhythmicity of the EEG, this problem could apply in many hyperscanning contexts. Furthermore, as these spurious hyper-connections reflected real similarities between the EEGs, they were not Type-1 errors that could be overcome by some appropriate statistical control. However, some measures that have not previously been used in hyperconnectivity studies, notably the circular correlation co-efficient (CCorr), were less susceptible to detecting spurious hyper-connections of this type. The reason for this advantage in performance is discussed and the use of the CCorr as an alternative measure of hyperconnectivity is advocated. © 2013 Burgess.

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Everyday human behaviour relies on our ability to predict outcomes on the basis of moment by moment information. Long-range neural phase synchronization has been hypothesized as a mechanism by which ‘predictions’ can exert an effect on the processing of incoming sensory events. Using magnetoencephalography (MEG) we have studied the relationship between the modulation of phase synchronization in a cerebral network of areas involved in visual target processing and the predictability of target occurrence. Our results reveal a striking increase in the modulation of phase synchronization associated with an increased probability of target occurrence. These observations are consistent with the hypothesis that long-range phase synchronization plays a critical functional role in humans' ability to effectively employ predictive heuristics.

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The present report reviews behavioural, electroencephalographic, and especially magnetoencephalographic findings on the cortical mechanisms underlying attentional processes that separate targets from distractors and that ensure durable target representations for goal-directed action. A common way of investigation is to observe the system’s overt and covert behaviour when capacity limitations are reached. Here we focus on the aspect of temporally enhanced processing load, namely on performance deficits occurring under rapid-serial-visual-presentation (RSVP) conditions. The most prominent of these deficits is the so-called “attentional blink” (AB) effect. We first report MEG findings with respect to the time course of activation that shows modulations around 300 ms after target onset which reflect demands and success of target consolidation. Then, findings regarding long-range inter-area phase synchronization are reported that are hypothesized to mediate communication within the attentional network. Changes in synchronization reflect changes in the attentional demands of the task and are directly related to behavioural performance. Furthermore, enhanced vigilance of the system elicits systematically increased synchronization indices. A hypothetical framework is sketched out that aims at explaining limitations in multiple target consolidation under RSVP conditions.

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The human mirror neuron system (MNS) has recently been a major topic of research in cognitive neuroscience. As a very basic reflection of the MNS, human observers are faster at imitating a biological as compared with a non-biological movement. However, it is unclear which cortical areas and their interactions (synchronization) are responsible for this behavioural advantage. We investigated the time course of long-range synchronization within cortical networks during an imitation task in 10 healthy participants by means of whole-head magnetoencephalography (MEG). Extending previous work, we conclude that left ventrolateral premotor, bilateral temporal and parietal areas mediate the observed behavioural advantage of biological movements in close interaction with the basal ganglia and other motor areas (cerebellum, sensorimotor cortex). Besides left ventrolateral premotor cortex, we identified the right temporal pole and the posterior parietal cortex as important junctions for the integration of information from different sources in imitation tasks that are controlled for movement (biological vs. non-biological) and that involve a certain amount of spatial orienting of attention. Finally, we also found the basal ganglia to participate at an early stage in the processing of biological movement, possibly by selecting suitable motor programs that match the stimulus.

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A synchronization scheme for a two-channel phase sensitive amplifier is implemented based on the injection-locking of single InP quantum-dash mode-locked laser. Error free performance with penalty <1 dB is demonstrated for both channels. © 2011 Optical Society of America.

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We propose a robust adaptive time synchronization and frequency offset estimation method for coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems by applying electrical dispersion pre-compensation (pre-EDC) to the pilot symbol. This technique effectively eliminates the timing error due to the fiber chromatic dispersion, thus increasing significantly the accuracy of the frequency offset estimation process and improving the overall system performance. In addition, a simple design of the pilot symbol is proposed for full-range frequency offset estimation. This pilot symbol can also be used to carry useful data to effectively reduce the overhead due to time synchronization by a factor of 2.

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The traditional use of global and centralised control methods, fails for large, complex, noisy and highly connected systems, which typify many real world industrial and commercial systems. This paper provides an efficient bottom up design of distributed control in which many simple components communicate and cooperate to achieve a joint system goal. Each component acts individually so as to maximise personal utility whilst obtaining probabilistic information on the global system merely through local message-passing. This leads to an implied scalable and collective control strategy for complex dynamical systems, without the problems of global centralised control. Robustness is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, can be implemented adaptively and opens a systematic rich way to information sharing. This paper opens the foreseen direction and inspects the proposed design on a linearised version of coupled map lattice with spatiotemporal chaos. A version close to linear quadratic design gives an initial insight into possible behaviours of such networks.

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A 21.6 Gbit/s 1.78 bit/s/Hz OFDM signal is transmitted over 50 Km of fiber without using DSP in the transmitter or the receiver. The synchronization scheme only requires one PLL to synchronize all the subcarriers.

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In this article we present a numerical study of the collective dynamics in a population of coupled semiconductor lasers with a saturable absorber, operating in the excitable regime under the action of additive noise. We demonstrate that temporal and intensity synchronization takes place in a broad region of the parameter space and for various array sizes. The synchronization is robust and occurs even for a set of nonidentical coupled lasers. The cooperative nature of the system results in a self-organization process which enhances the coherence of the single element of the population too and can have broad impact for detection purposes, for building all-optical simulators of neural networks and in the field of photonics-based computation.