Articles de revista
http://hdl.handle.net/2117/3215
20170223T14:32:53Z

Global atmospheric dynamics investigated by using Hilbert frequency analysis
http://hdl.handle.net/2117/101413
Global atmospheric dynamics investigated by using Hilbert frequency analysis
Zappala, Dario; Barreiro, Marcelo; Masoller Alonso, Cristina
The Hilbert transform is a wellknown tool of time series analysis that has been widely used to investigate oscillatory signals that resemble a noisy periodic oscillation, because it allows instantaneous phase and frequency to be estimated, which in turn uncovers interesting properties of the underlying process that generates the signal. Here we use this tool to analyze atmospheric data: we consider dailyaveraged Surface Air Temperature (SAT) time series recorded over a regular grid of locations covering the Earth’s surface. From each SAT time series, we calculate the instantaneous frequency time series by considering the Hilbert analytic signal. The properties of the obtained frequency data set are investigated by plotting the map of the average frequency and the map of the standard deviation of the frequency fluctuations. The average frequency map reveals welldefined largescale structures: in the extratropics, the average frequency in general corresponds to the expected oneyear period of solar forcing, while in the tropics, a different behaviour is found, with particular regions having a faster average frequency. In the standard deviation map, largescale structures are also found, which tend to be located over regions of strong annual precipitation. Our results demonstrate that Hilbert analysis of SAT timeseries uncovers meaningful information, and is therefore a promising tool for the study of other climatological variables.
20170222T19:10:46Z
Zappala, Dario
Barreiro, Marcelo
Masoller Alonso, Cristina
The Hilbert transform is a wellknown tool of time series analysis that has been widely used to investigate oscillatory signals that resemble a noisy periodic oscillation, because it allows instantaneous phase and frequency to be estimated, which in turn uncovers interesting properties of the underlying process that generates the signal. Here we use this tool to analyze atmospheric data: we consider dailyaveraged Surface Air Temperature (SAT) time series recorded over a regular grid of locations covering the Earth’s surface. From each SAT time series, we calculate the instantaneous frequency time series by considering the Hilbert analytic signal. The properties of the obtained frequency data set are investigated by plotting the map of the average frequency and the map of the standard deviation of the frequency fluctuations. The average frequency map reveals welldefined largescale structures: in the extratropics, the average frequency in general corresponds to the expected oneyear period of solar forcing, while in the tropics, a different behaviour is found, with particular regions having a faster average frequency. In the standard deviation map, largescale structures are also found, which tend to be located over regions of strong annual precipitation. Our results demonstrate that Hilbert analysis of SAT timeseries uncovers meaningful information, and is therefore a promising tool for the study of other climatological variables.

Unravelling the community structure of the climate system by using lags and symbolic timeseries analysis
http://hdl.handle.net/2117/101373
Unravelling the community structure of the climate system by using lags and symbolic timeseries analysis
Tirabassi, Giulio; Masoller Alonso, Cristina
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among timeseries recorded at different grid points, and by applying symbolic timeseries analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of largescale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through timeseries analysis of the observed output signals.
This work is licensed under a Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/
20170222T12:06:34Z
Tirabassi, Giulio
Masoller Alonso, Cristina
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among timeseries recorded at different grid points, and by applying symbolic timeseries analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of largescale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through timeseries analysis of the observed output signals.

Unveiling temporal correlations characteristic to phase transition in the output intensity of a fiber laser
http://hdl.handle.net/2117/101317
Unveiling temporal correlations characteristic to phase transition in the output intensity of a fiber laser
Aragoneses, Andrés; Carpi, Laura; Tarasov, N.; Churkin, D.V.; Torrent Serra, Maria del Carmen; Masoller Alonso, Cristina; Turitsyn, S.K.
We use advanced statistical tools of timeseries analysis to characterize the dynamical complexity of the transition to optical wave turbulence in a fiber laser. Ordinal analysis and the horizontal visibility graph applied to the experimentally measured laser output intensity reveal the presence of temporal correlations during the transition from the laminar to the turbulent lasing regimes. Both methods unveil coherent structures with welldefined time scales and strong correlations both, in the timing of the laser pulses and in their peak intensities. Our approach is generic and may be used in other complex systems that undergo similar transitions involving the generation of extreme fluctuations.
20170221T13:06:11Z
Aragoneses, Andrés
Carpi, Laura
Tarasov, N.
Churkin, D.V.
Torrent Serra, Maria del Carmen
Masoller Alonso, Cristina
Turitsyn, S.K.
We use advanced statistical tools of timeseries analysis to characterize the dynamical complexity of the transition to optical wave turbulence in a fiber laser. Ordinal analysis and the horizontal visibility graph applied to the experimentally measured laser output intensity reveal the presence of temporal correlations during the transition from the laminar to the turbulent lasing regimes. Both methods unveil coherent structures with welldefined time scales and strong correlations both, in the timing of the laser pulses and in their peak intensities. Our approach is generic and may be used in other complex systems that undergo similar transitions involving the generation of extreme fluctuations.

Formation of highorder acoustic Bessel beams by spiral diffraction gratings
http://hdl.handle.net/2117/101154
Formation of highorder acoustic Bessel beams by spiral diffraction gratings
Jimenez, Noe; Pico Vila, Rubén; Sánchez Morcillo, Victor José; Romero García, Vicenç; Garcia Raffi, Luis Miguel; Staliunas, Kestutis
The formation of highorder Bessel beams by a passive acoustic device consisting of an Archimedes' spiral diffraction grating is theoretically, numerically, and experimentally reported in this paper. These beams are propagationinvariant solutions of the Helmholtz equation and are characterized by an azimuthal variation of the phase along its annular spectrum producing an acoustic vortex in the near field. In our system, the scattering of plane acoustic waves by the spiral grating leads to the formation of the acoustic vortex with zero pressure on axis and the angular phase dislocations characterized by the spiral geometry. The order of the generated Bessel beam and, as a consequence, the size of the generated vortex can be fixed by the number of arms in the spiral diffraction grating. The obtained results allow for obtaining Bessel beams with controllable vorticity by a passive device, which has potential applications in lowcost acoustic tweezers and acoustic radiation force devices.
20170216T17:50:42Z
Jimenez, Noe
Pico Vila, Rubén
Sánchez Morcillo, Victor José
Romero García, Vicenç
Garcia Raffi, Luis Miguel
Staliunas, Kestutis
The formation of highorder Bessel beams by a passive acoustic device consisting of an Archimedes' spiral diffraction grating is theoretically, numerically, and experimentally reported in this paper. These beams are propagationinvariant solutions of the Helmholtz equation and are characterized by an azimuthal variation of the phase along its annular spectrum producing an acoustic vortex in the near field. In our system, the scattering of plane acoustic waves by the spiral grating leads to the formation of the acoustic vortex with zero pressure on axis and the angular phase dislocations characterized by the spiral geometry. The order of the generated Bessel beam and, as a consequence, the size of the generated vortex can be fixed by the number of arms in the spiral diffraction grating. The obtained results allow for obtaining Bessel beams with controllable vorticity by a passive device, which has potential applications in lowcost acoustic tweezers and acoustic radiation force devices.

Asymmetric light transmission in PTSymmetric microring resonators
http://hdl.handle.net/2117/100764
Asymmetric light transmission in PTSymmetric microring resonators
Giden, I.; Dadashi, Kh.; Botey Cumella, Muriel; Herrero Simon, Ramon; Staliunas, Kestutis; Kurt, Hamza
We propose a new type of adddrop microring resonator made of gain and loss materials. Microring resonators are compact, narrow band, and optical channel dropping filters. In such linear systems, light transmission to sidecoupled signal waveguides is always symmetric. However, we prove that properly arranging a gain and loss modulation in the microring resonator provides a new functionality: asymmetric transmission; so that different resonant modes can be promoted depending on the input channel. This can be achieved when the resonator holds paritytime (PT) symmetry, with periodic gainloss and index modulations. PTsymmetry in optics generally requires that the index and gainloss modulations are dephased by a quarter of the wavenumber of the modulation. Besides, we show that a simple halfgain halfloss microring also produces analogous results to a periodic PTsymmetric system. The results are numerically proved and also accounted by a simple analytical model. The effect of using complex modulated resonators with smaller periodicities is also analyzed.
20170209T13:32:20Z
Giden, I.
Dadashi, Kh.
Botey Cumella, Muriel
Herrero Simon, Ramon
Staliunas, Kestutis
Kurt, Hamza
We propose a new type of adddrop microring resonator made of gain and loss materials. Microring resonators are compact, narrow band, and optical channel dropping filters. In such linear systems, light transmission to sidecoupled signal waveguides is always symmetric. However, we prove that properly arranging a gain and loss modulation in the microring resonator provides a new functionality: asymmetric transmission; so that different resonant modes can be promoted depending on the input channel. This can be achieved when the resonator holds paritytime (PT) symmetry, with periodic gainloss and index modulations. PTsymmetry in optics generally requires that the index and gainloss modulations are dephased by a quarter of the wavenumber of the modulation. Besides, we show that a simple halfgain halfloss microring also produces analogous results to a periodic PTsymmetric system. The results are numerically proved and also accounted by a simple analytical model. The effect of using complex modulated resonators with smaller periodicities is also analyzed.

Quantification of network structural dissimilarities
http://hdl.handle.net/2117/100483
Quantification of network structural dissimilarities
Schieber, Tiabo A.; Carpi, Laura; Díaz Guilera, Albert; Pardalos, Panos M.; Masoller Alonso, Cristina; Ravetti, Martin G.
Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and realworld networks show that this measure returns nonzero values only when the graphs are nonisomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.
© 2017. This version is made available under the CCBYNCND 4.0 license http://creativecommons.org/licenses/byncnd/4.0/
20170202T11:41:14Z
Schieber, Tiabo A.
Carpi, Laura
Díaz Guilera, Albert
Pardalos, Panos M.
Masoller Alonso, Cristina
Ravetti, Martin G.
Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and realworld networks show that this measure returns nonzero values only when the graphs are nonisomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

Analysis of noiseinduced temporal correlations in neuronal spike sequences
http://hdl.handle.net/2117/99468
Analysis of noiseinduced temporal correlations in neuronal spike sequences
Reinoso, Jose A.; Torrent Serra, Maria del Carmen; Masoller Alonso, Cristina
We investigate temporal correlations in sequences of noiseinduced neuronal spikes, using a symbolic method of timeseries analysis. We focus on the sequence of timeintervals between consecutive spikes (interspikeintervals, ISIs). The analysis method, known as ordinal analysis, transforms the ISI sequence into a sequence of ordinal patterns (OPs), which are defined in terms of the relative ordering of consecutive ISIs. The ISI sequences are obtained from extensive simulations of two neuron models (FitzHughNagumo, FHN, and integrateandfire, IF), with correlated noise. We find that, as the noise strength increases, temporal order gradually emerges, revealed by the existence of more frequent ordinal patterns in the ISI sequence. While in the FHN model the most frequent OP depends on the noise strength, in the IF model it is independent of the noise strength. In both models, the correlation time of the noise affects the OP probabilities but does not modify the most probable pattern.
This is a copy of the author 's final draft version of an article published in the journal European physical journal. Special topics.
The final publication is available at Springer via http://dx.doi.org/10.1140/epjst/e2016600246
20170117T13:04:24Z
Reinoso, Jose A.
Torrent Serra, Maria del Carmen
Masoller Alonso, Cristina
We investigate temporal correlations in sequences of noiseinduced neuronal spikes, using a symbolic method of timeseries analysis. We focus on the sequence of timeintervals between consecutive spikes (interspikeintervals, ISIs). The analysis method, known as ordinal analysis, transforms the ISI sequence into a sequence of ordinal patterns (OPs), which are defined in terms of the relative ordering of consecutive ISIs. The ISI sequences are obtained from extensive simulations of two neuron models (FitzHughNagumo, FHN, and integrateandfire, IF), with correlated noise. We find that, as the noise strength increases, temporal order gradually emerges, revealed by the existence of more frequent ordinal patterns in the ISI sequence. While in the FHN model the most frequent OP depends on the noise strength, in the IF model it is independent of the noise strength. In both models, the correlation time of the noise affects the OP probabilities but does not modify the most probable pattern.

Transition between functional regimes in an integrateandfire network model of the thalamus
http://hdl.handle.net/2117/99035
Transition between functional regimes in an integrateandfire network model of the thalamus
Barardi, Alessandro; García Ojalvo, Jordi; Mazzoni, Alberto
The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7  15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different informationprocessing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and GABAergic reticular (RE) neurons described by adaptive integrateandfire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TCRE loops generate spindlelike oscillations and that a minimum level of clustering (i.e. local connectivity density) in the RERE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarizationrebounddriven firing to depolarizationdriven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex.
20170111T14:33:37Z
Barardi, Alessandro
García Ojalvo, Jordi
Mazzoni, Alberto
The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7  15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different informationprocessing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and GABAergic reticular (RE) neurons described by adaptive integrateandfire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TCRE loops generate spindlelike oscillations and that a minimum level of clustering (i.e. local connectivity density) in the RERE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarizationrebounddriven firing to depolarizationdriven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex.

Temporally correlated fluctuations drive epileptiform dynamics
http://hdl.handle.net/2117/98638
Temporally correlated fluctuations drive epileptiform dynamics
Jedynak, Maciej; Pons Rivero, Antonio Javier; García Ojalvo, Jordi; Goodfellow, Marc
Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in nonmodelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/fb spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain.
To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates “healthy” or “epileptiform” dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an OrnsteinUhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the d and ¿ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine largescale models with noise of a variety of different types in order to understand brain (dys)function.
20161220T15:33:36Z
Jedynak, Maciej
Pons Rivero, Antonio Javier
García Ojalvo, Jordi
Goodfellow, Marc
Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in nonmodelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/fb spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain.
To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates “healthy” or “epileptiform” dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an OrnsteinUhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the d and ¿ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine largescale models with noise of a variety of different types in order to understand brain (dys)function.

Quantitative identification of dynamical transitions in a semiconductor laser with optical feedback
http://hdl.handle.net/2117/98226
Quantitative identification of dynamical transitions in a semiconductor laser with optical feedback
Quintero Quiroz, Carlos Alberto; Tiana Alsina, Jordi; Roma, Josep; Torrent Serra, Maria del Carmen; Masoller Alonso, Cristina
Identifying transitions to complex dynamical regimes is a fundamental open problem with many practical applications. Semi conductor lasers with optical feedback are excellent testbeds for studying such transitions, as they can generate a rich variety of output signals. Here we apply three analysis tools to quantify various aspects of the dynamical transitions that occur as the laser pump current increases. These tools allow to quantitatively detect the onset of two different regimes, lowfrequency fluctuations and coherence collapse, and can be used for identifying the operating conditions that result in specific dynamical properties of the laser output. These tools can also be valuable for analyzing regime transitions in other complex systems.
20161214T14:16:15Z
Quintero Quiroz, Carlos Alberto
Tiana Alsina, Jordi
Roma, Josep
Torrent Serra, Maria del Carmen
Masoller Alonso, Cristina
Identifying transitions to complex dynamical regimes is a fundamental open problem with many practical applications. Semi conductor lasers with optical feedback are excellent testbeds for studying such transitions, as they can generate a rich variety of output signals. Here we apply three analysis tools to quantify various aspects of the dynamical transitions that occur as the laser pump current increases. These tools allow to quantitatively detect the onset of two different regimes, lowfrequency fluctuations and coherence collapse, and can be used for identifying the operating conditions that result in specific dynamical properties of the laser output. These tools can also be valuable for analyzing regime transitions in other complex systems.