arXiv subjects

Elbert E. N. Macau

Publications and source records attributed to Elbert E. N. Macau.

At least 19 recordsLinked to original sources

arXiv2025

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques

This work proposes an innovative approach using machine learning to predict extreme events in time series of chaotic dynamical systems. The research focuses on the time series of the Hénon map, a two-dimensional model known for its chaotic behavior. The method consists of identifying time windows that anticipate extreme events, using convolutional neural networks to classify the system states. By reconstructing attractors and classifying (normal and transitional) regimes, the model shows high accuracy in predicting normal regimes, although forecasting transitional regimes remains challenging, particularly for longer intervals and rarer events. The method presents a result above 80% of success for predicting the transition regime up to 3 steps before the occurrence of the extreme event. Despite limitations posed by the chaotic nature of the system, the approach opens avenues for further exploration of alternative neural network architectures and broader datasets to enhance forecasting capabilities.

nlin.CD↗

arXiv2025

Noise-induced Extreme Events in Hodgkin-Huxley Neural Networks

Extreme events are rare, large-scale deviations from typical system behavior that can occur in nonlinear dynamical systems. In this study, we explore the emergence of extreme events within a network of identical stochastic Hodgkin-Huxley neurons with mean-field coupling. The neurons are exposed to uncorrelated noise, which introduces stochastic electrical fluctuations that influence their spiking activity. Analyzing the variations in the amplitude of the mean field, we observe a smooth transition from small-amplitude, out-of-sync activity to synchronized spiking activity as the coupling parameter increases, while an abrupt transition occurs with increasing noise intensity. However, beyond a certain threshold, the coupling abruptly suppresses the spiking activity of the network. Our analysis reveals that the influence of noise combined with neuronal coupling near the abrupt transitions can trigger cascades of synchronized spiking activity, identified as extreme events. The analysis of the entropy of the mean field allows us to detect the parameter region where these events occur. We characterize the statistics of these events and find that, as the network size increases, the parameter range where they occur decreases significantly. Our findings shed light on the mechanisms driving extreme events in neural networks and how noise and neural coupling shape collective behavior.

q-bio.NC↗

arXiv2023

Phase synchronization in a sparse network of randomly connected neurons under the effect of Poissonian spike inputs

This article investigates the emergence of phase synchronization in a network of randomly connected neurons by chemical synapses. The study uses the classic Hodgkin-Huxley model to simulate the neuronal dynamics under the action of a train of Poissonian spikes. In such a scenario, we observed the emergence of irregular spikes for a specific range of conductances, and also that the phase synchronization of the neurons is reached when the external current is strong enough to induce spiking activity but without overcoming the coupling current. Conversely, if the external current assumes very high values, then an opposite effect is observed, i.e. the prevention of the network synchronization. We explain such behaviors considering different mechanisms involved in the system, such as incoherence, minimization of currents, and stochastic effects from the Poissonian spikes. Furthermore, we present some numerical simulations where the stimulation of only a fraction of neurons, for instance, can induce phase synchronization in the non-stimulated fraction of the network, besides cases in which for larger coupling values it is possible to propagate the spiking activity in the network when considering stimulation over only one neuron.

physics.bio-ph↗

arXiv2022

The effect of time delay for synchronisation suppression in neuronal networks

We study the time delay in the synaptic conductance for suppression of spike synchronisation in a random network of Hodgkin Huxley neurons coupled by means of chemical synapses. In the first part, we examine in detail how the time delay acts over the network during the synchronised and desynchronised neuronal activities. We observe a relation between the neuronal dynamics and the syaptic conductance distributions. We find parameter values in which the time delay has high effectiveness in promoting the suppression of spike synchronisation. In the second part, we analyse how the delayed neuronal networks react when pulsed inputs with different profiles (periodic, random, and mixed) are applied on the neurons. We show the main parameters responsible for inducing or not synchronous neuronal oscillations in delayed networks.

q-bio.NC↗

arXiv2022

The starting dates of COVID-19 multiple waves

The severe acute respiratory syndrome of coronavirus 2 spread globally very quickly, causing great concern at the international level due to the severity of the associated respiratory disease, the so-called COVID-19. Considering Rio de Janeiro city (Brazil) as an example, the first diagnosis of this disease occurred in March 2020, but the exact moment when the local spread of the virus started is uncertain as the Brazilian epidemiological surveillance system was not widely prepared to detect suspected cases of COVID-19 at that time. Improvements in this surveillance system occurred over the pandemic, but due to the complex nature of the disease transmission process, specifying the exact moment of emergence of new community contagion outbreaks is a complicated task. This work aims to propose a general methodology to determine possible start dates for the multiple community outbreaks of COVID-19, using for this purpose a parametric statistical approach that combines surveillance data, nonlinear regression, and information criteria to obtain a statistical model capable of describing the multiple waves of contagion observed. The dynamics of COVID-19 in the city of Rio de Janeiro is taken as a case study, and the results suggest that the original strain of the virus was already circulating in Rio de Janeiro city as early as late February 2020, probably being massively disseminated in the population during the carnival festivities.

q-bio.PE↗

arXiv2021

Basin sizes depend on stable eigenvalues in the Kuramoto model

We show that for the Kuramoto model (with identical phase oscillators equally coupled) its global statistics and size of the basins of attraction can be estimated through the eigenvalues of all stable (frequency) synchronized states. This result is somehow unexpected since, by doing that, one could just use local analysis to obtain global dynamic properties. But recent works based on Koopman and Perron-Frobenius operators demonstrate that global features of a nonlinear dynamical system, with some specific conditions, are somehow encoded in the local eigenvalues of its equilibrium states. Recognized numerical simulations in the literature reinforce our analytical results.

nlin.CD↗

arXiv2021

The Effect of Time Series Distance Functions on Functional Climate Networks

Complex network theory provides an important tool for the analysis of complex systems such as the Earth's climate. In this context, functional climate networks can be constructed using a spatiotemporal climate dataset and a suitable time series distance function. The resulting coarse-grained view on climate variability consists of representing distinct areas on the globe (i.e., grid cells) by nodes and connecting pairs of nodes that present similar time series. One fundamental concern when constructing such a functional climate network is the definition of a metric that captures the mutual similarity between time series. Here we study systematically the effect of 29 time series distance functions on functional climate network construction based on global temperature data. We observe that the distance functions previously used in the literature commonly generate very similar networks while alternative ones result in rather distinct network structures and reveal different long-distance connection patterns. These patterns are highly important for the study of climate dynamics since they generally represent pathways for the long-distance transportation of energy and can be used to forecast climate variability on subseasonal to interannual or even decadal scales. Therefore, we propose the measures studied here as alternatives for the analysis of climate variability and to further exploit their complementary capability of capturing different aspects of the underlying dynamics that may help gaining a more holistic empirical understanding of the global climate system.

physics.data-an↗

arXiv2020

Spatiotemporal data analysis with chronological networks

The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet.

cs.SI↗

arXiv2020

Measuring the engagement level in encrypted group conversations by using temporal networks

Chat groups are well-known for their capacity to promote viral political and marketing campaigns, spread fake news, and create rallies by hundreds of thousands on the streets. Also, with the increasing public awareness regarding privacy and surveillance, many platforms have started to deploy end-to-end encrypted protocols. In this context, the group's conversations are not accessible in plain text or readable format by third-party organizations or even the platform owner. Then, the main challenge that emerges is related to getting insights from users' activity of those groups, but without accessing the messages. Previous approaches evaluated the user engagement by assessing user's activity, however, on limited conditions where the data is encrypted, they cannot be applied. In this work, we present a framework for measuring the level of engagement of group conversations and users, without reading the messages. Our framework creates an ensemble of interaction networks that represent the temporal evolution of the conversation, then, we apply the proposed Engagement Index (EI) for each interval of conversations to assess users' participation. Our results in five datasets from real-world WhatsApp Groups indicate that, based on the EI, it is possible to identify the most engaged users within a time interval, create rankings, and group users according to their engagement and monitor their performance over time.

cs.SI↗

arXiv2020

Chaotic Phase Synchronization and Desynchronization in an Oscillator Network for Object Selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.

cs.CV↗

arXiv2019

Short-term and spike-timing-dependent plasticities facilitate the formation of modular neural networks

The brain has the phenomenal ability to reorganize itself by forming new connections among neurons and by pruning others. The so-called neural or brain plasticity facilitates the modification of brain structure and function over different time scales. Plasticity might occur due to external stimuli received from the environment, during recovery from brain injury, or due to modifications within the body and brain itself. In this paper, we study the combined effect of short-term (STP) and spike-timing-dependent plasticities (STDP) on the synaptic strength of excitatory coupled Hodgkin-Huxley neurons and show that plasticity can facilitate the formation of modular neural networks with complex topologies that resemble those of networks with preferential attachment properties. In particular, we use an STDP rule that alters the synaptic coupling intensity based on time intervals between spikes of postsynaptic and presynaptic neurons. Previous works have shown that STDP may induce the appearance of directed connections from high to low-frequency spiking neurons. On the other hand, STP is attributed to the release of neurotransmitters in the synaptic cleft of neurons that alter its synaptic efficiency. Our results suggest that the combined effect of STP and STDP with high recovery time facilitates the formation of connections among neurons with similar spike frequencies only, a kind of preferential attachment. We then pursue this further and show that, when starting with all-to-all neural configurations, depending on the STP recovery time and distribution of neural frequencies, modular neural networks can emerge as a direct result of the combined effect of STP and STDP.

q-bio.NC↗

arXiv2019

Global Fire Season Severity Analysis and Forecasting

In this paper, we divide the globe into a hexagonal grid and we extracted time series of daily fire counts from each cell to estimate and analyze worldwide fire season severity (FSS), here defined as the accumulated fire detections in a season. The central question here is evaluating the accuracy of time series forecasting methods to estimate short-term (months) and medium-term (seasons) using only historical data of active fire detections. This approach is simple, fast, and use globally available data, making it easier for large scale prediction. Our results comprehend descriptive and predictive analyses of the worldwide seasonal fire activity. We verified that in 99% of the cells, the fire seasons have lengths shorter than seven months and that 57% have their lengths decrease. We also observed a declining tendency in the number of active fire counts during the seasons in 61% cells. However, some regions like the Northeast Brazil and the West Coast of the USA present an increasing trend. We verified that the forecasting error is lower than the mean FSS in 95% of the cells, indicating clear predictability in the FSS.

stat.AP↗

arXiv2018

From spatio-temporal data to chronological networks: An application to wildfire analysis

Network theory has established itself as an appropriate tool for complex systems analysis and pattern recognition. In the context of spatiotemporal data analysis, correlation networks are used in the vast majority of works. However, the Pearson correlation coefficient captures only linear relationships and does not correctly capture recurrent events. This missed information is essential for temporal pattern recognition. In this work, we propose a chronological network construction process that is capable of capturing various events. Similar to the previous methods, we divide the area of study into grid cells and represent them by nodes. In our approach, links are established if two consecutive events occur in two different nodes. Our method is computationally efficient, adaptable to different time windows and can be applied to any spatiotemporal data set. As a proof-of-concept, we evaluated the proposed approach by constructing chronological networks from the MODIS dataset for fire events in the Amazon basin. We explore two data analytic approaches: one static and another temporal. The results show some activity patterns on the fire events and a displacement phenomenon over the year. The validity of the analyses in this application indicates that our data modeling approach is very promising for spatio-temporal data mining.

physics.soc-ph↗

arXiv2017

Synchronization in Networks with Strongly Delayed Couplings

We investigate the stability of synchronization in networks of dynamical systems with strongly delayed connections. We obtain strict conditions for synchronization of periodic and equilibrium solutions. In particular, we show the existence of a critical coupling strength $κ_{c}$, depending only on the network structure, isolated dynamics and coupling function, such that for large delay and coupling strength $κ<κ_{c}$, the network possesses stable synchronization. The critical coupling $κ_{c}$ can be chosen independently of the delay for the case of equilibria, while for the periodic solution, $κ_{c}$ depends essentially on the delay and vanishes as the delay increases. We observe that, for random networks, the synchronization interval is maximal when the network is close to the connectivity threshold. We also derive scaling of the coupling parameter that allows for a synchronization of large networks for different network topologies.

math.DS↗

arXiv2017

Synaptic Plasticity and Spike Synchronisation in Neuronal Networks

Brain plasticity, also known as neuroplasticity, is a fundamental mechanism of neuronal adaptation in response to changes in the environment or due to brain injury. In this review, we show our results about the effects of synaptic plasticity on neuronal networks composed by Hodgkin-Huxley neurons. We show that the final topology of the evolved network depends crucially on the ratio between the strengths of the inhibitory and excitatory synapses. Excitation of the same order of inhibition revels an evolved network that presents the rich-club phenomenon, well known to exist in the brain. For initial networks with considerably larger inhibitory strengths, we observe the emergence of a complex evolved topology, where neurons sparsely connected to other neurons, also a typical topology of the brain. The presence of noise enhances the strength of both types of synapses, but if the initial network has synapses of both natures with similar strengths. Finally, we show how the synchronous behaviour of the evolved network will reflect its evolved topology.

q-bio.NC↗

arXiv2016

Star-type oscillatory networks with generic Kuramoto-type coupling: a model for "Japanese drums synchrony"

We analyze star-type networks of phase oscillators by virtue of two methods. For identical oscillators we adopt the Watanabe-Strogatz approach, that gives full an- alytical description of states, rotating with constant frequency. For nonidentical oscillators, such states can be obtained by virtue of the self-consistent approach in a parametric form. In this case stability analysis cannot be performed, however with the help of direct numerical simulations we show which solutions are stable and which not. We consider this system as a model for a drum orchestra, where we assume that the drummers follow the signal of the leader without listening to each other and the coupling parameters are determined by a geometrical organization of the orchestra.

nlin.AO↗

arXiv2015

Persistence of Network Synchronization under Nonidentical Coupling Functions

We investigate the persistence of synchronization in networks of diffusively coupled oscillators when the coupling functions are nonidentical. Under mild conditions, we uncover the influence of the network interaction structure on the mismatches of the coupling function. We show that Erdös-Rényi random graphs support large perturbations in the coupling function. In contrast scale-free graphs do not allow large perturbations in the coupling function, that is, as the network size n goes to infinity it forces the coupling functions to be identical.

math.DS↗

arXiv2015

Do the recent severe droughts in the Amazonia have the same period of length?

We propose a new measure based on drought period length to assess the temporal difference between the recent two severe droughts of 2005 and 2010 in the Amazonia. The sensitivity of the measure is demonstrated by disclosing the distinct spatial responding mechanisms of the Northeastern and Southwestern Amazon (NA, SA) to the surrounding sea surface temperature (SST) variabilities. The Pacific and Atlantic oceans have different roles on the precipitation patterns in Amazonia. More specifically, the very dry periods in the NA are influenced by El Niño events, while the very dry periods in the SA are affected by the anomalously warming of the SST in the North Atlantic. We show convincingly that the drought 2005 hit SA, which is caused by the North Atlantic only. There are two phases in the drought 2010: (i) it was started in the NA in August 2009 affected by the El Niño event, and (ii) later shifted the center of action to SA resulted from anomalously high SST in North Atlantic, which further intensifies the impacts on the spatial coverage.

physics.ao-ph↗