4,793 research outputs found
Effects of the network structural properties on its controllability
In a recent paper, it has been suggested that the controllability of a
diffusively coupled complex network, subject to localized feedback loops at
some of its vertices, can be assessed by means of a Master Stability Function
approach, where the network controllability is defined in terms of the spectral
properties of an appropriate Laplacian matrix. Following that approach, a
comparison study is reported here among different network topologies in terms
of their controllability. The effects of heterogeneity in the degree
distribution, as well as of degree correlation and community structure, are
discussed.Comment: Also available online at: http://link.aip.org/link/?CHA/17/03310
Using synchronism of chaos for adaptive learning of network topology
In this paper we consider networks of dynamical systems that evolve in
synchrony and investigate how dynamical information from the synchronization
dynamics can be effectively used to learn the network topology, i.e., identify
the time evolution of the couplings between the network nodes. To this aim, we
present an adaptive strategy that, based on a potential that the network
systems seek to minimize in order to maintain synchronization, can be
successfully applied to identify the time evolution of the network from limited
information. This strategy takes advantage of the properties of synchronism of
chaos and of the presence of different communication delays over the network
links. As a motivating example we consider a network of sensors surveying an
area, in which information regarding the time evolution of the network
connections can be used, e.g., to detect changes taking place within the area.
We propose two different setups for our strategy. In the first one,
synchronization has to be achieved at each node (as well as the identification
of the couplings over the network links), based solely on a single scalar
signal representing a superposition of signals from the other nodes in the
network. In the second one, we incorporate an additional node, termed the
maestro, having the function of maintaining network synchronization. We will
see that when such an arrangement is realized, it will become possible to
effectively identify the time evolution of networks that are much larger than
would be possible in the absence of a maestro.Comment: 22 pages, 12 figures, accepted for publication on Physical Review
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Semantic and inferencing abilities in children with communication disorders
Background: Semantic and inferencing abilities have not been fully examined in children with communication difficulties.
Aims: To investigate the inferential and semantic abilities of children with communication difficulties using newly designed tasks.
Methods & Procedures: Children with different types of communication disorder were compared with each other and with three groups of typically developing children: those of the same chronological age and two groups of younger children. In total, 25 children aged 11 years with specific language impairment and 22 children, also 11 years of age, with primary pragmatic difficulties were recruited. Typically developing groups aged 11 (nâ=â35; ageâmatch), and those aged 9 (nâ=â40) and 7 (nâ=â37; language similar) also participated as comparisons.
Outcomes & Results: For Semantic Choices, children with specific language impairment performed significantly more poorly than 9â and 11âyearâolds, whilst the pragmatic difficulties group scored significantly lower than all the typically developing groups. Borderline differences between specific language impairment and pragmatic difficulties groups were found. For inferencing, children with communication impairments performed significantly below the 11âyearâold peers, but not poorer than 9â and 7âyearâolds, suggesting that this skill is in line with language ability. Six children in the pragmatic difficulties group who met diagnosis for autism performed more poorly than the other two clinical groups on both tasks, but not statistically significantly so.
Conclusions: Both tasks were more difficult for those with communication impairments compared with peers. Semantic but not inferencing abilities showed a nonâsignificant trend for differences between the two clinical groups and children with pragmatic difficulties performed more poorly than all typically developing groups. The tasks may relate to each other in varying ways according to type of communication difficulty
Inhibition of phosphoinositide 3-kinase/protein kinase B signaling hampers the vasopressin-dependent stimulation of myogenic differentiation
Arginine-vasopressin (AVP) promotes muscle differentiation, hypertrophy, and regeneration through the combined activation of the calcineurin and Calcium/Calmodulin-dependent Protein Kinase (CaMK) pathways. The AVP system is impaired in several neuromuscular diseases, suggesting that AVP may act as a physiological factor in skeletal muscle. Since the Phosphoinositide 3-kinases/Protein Kinase B/mammalian Target Of Rapamycin (PI3K/Akt/mTOR) signaling plays a significant role in regulating muscle mass, we evaluated its role in the AVP myogenic effect. In L6 cells AKT1 expression was knocked down, and the AVP-dependent expression of mTOR and Forkhead box O3 (FoxO) was analyzed by Western blotting. The effect of the PI3K inhibitor LY294002 was evaluated by cellular and molecular techniques. Akt knockdown hampered the AVP-dependent mTOR expression while increased the levels of FoxO transcription factor. LY294002 treatment inhibited the AVP-dependent expression of Myocyte Enhancer Factor-2 (MEF2) and myogenin and prevented the nuclear translocation of MEF2. LY294002 also repressed the AVP-dependent nuclear export of histone deacetylase 4 (HDAC4) interfering with the formation of multifactorial complexes on the myogenin promoter. We demonstrate that the PI3K/Akt pathway is essential for the full myogenic effect of AVP and that, by targeting this pathway, one may highlight novel strategies to counteract muscle wasting in aging or neuromuscular disorders
Dynamic filtering of static dipoles in magnetoencephalography
We consider the problem of estimating neural activity from measurements
of the magnetic fields recorded by magnetoencephalography. We exploit
the temporal structure of the problem and model the neural current as a
collection of evolving current dipoles, which appear and disappear, but whose
locations are constant throughout their lifetime. This fully reflects the physiological
interpretation of the model.
In order to conduct inference under this proposed model, it was necessary
to develop an algorithm based around state-of-the-art sequential Monte
Carlo methods employing carefully designed importance distributions. Previous
work employed a bootstrap filter and an artificial dynamic structure
where dipoles performed a random walk in space, yielding nonphysical artefacts
in the reconstructions; such artefacts are not observed when using the
proposed model. The algorithm is validated with simulated data, in which
it provided an average localisation error which is approximately half that of
the bootstrap filter. An application to complex real data derived from a somatosensory
experiment is presented. Assessment of model fit via marginal
likelihood showed a clear preference for the proposed model and the associated
reconstructions show better localisation
The stability of adaptive synchronization of chaotic systems
In past works, various schemes for adaptive synchronization of chaotic
systems have been proposed. The stability of such schemes is central to their
utilization. As an example addressing this issue, we consider a recently
proposed adaptive scheme for maintaining the synchronized state of identical
coupled chaotic systems in the presence of a priori unknown slow temporal drift
in the couplings. For this illustrative example, we develop an extension of the
master stability function technique to study synchronization stability with
adaptive coupling. Using this formulation, we examine local stability of
synchronization for typical chaotic orbits and for unstable periodic orbits
within the synchronized chaotic attractor (bubbling). Numerical experiments
illustrating the results are presented. We observe that the stable range of
synchronism can be sensitively dependent on the adaption parameters, and we
discuss the strong implication of bubbling for practically achievable adaptive
synchronization.Comment: 21 pages, 6 figure
Adaptive coupling for achieving stable synchronization of chaos
We consider synchronization of coupled chaotic systems and propose an
adaptive strategy that aims at evolving the strength of the coupling to achieve
stability of the synchronized evolution. We test this idea in a simple
configuration in which two chaotic systems are unidirectionally coupled (a
sender and a receiver) and we study conditions for the receiver to adaptively
synchronize with the sender. Numerical simulations show that, under certain
conditions, our strategy is successful in dynamically evolving the coupling
strength until it converges to a value that is compatible with synchronization.Comment: 12 Pages, 9 figures, accepted for publication in Physical Review
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