## About

36

Publications

3,716

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204

Citations

Citations since 2017

Introduction

I believe that mathematical and computational modeling combined with theoretical and experimental approaches are of fundamental importance for the development of modern science. My research interests are related to computational neuroscience, synchronization of complex networks, dynamical features of neural systems, numerical simulations, and data analysis.

Additional affiliations

March 2018 - March 2021

August 2016 - February 2018

Education

March 2018 - March 2021

August 2016 - February 2018

March 2012 - July 2016

## Publications

Publications (36)

One of the simplest mathematical models in the study of nonlinear systems is the Kuramoto model, which describes synchronization in systems from swarms of insects to superconductors. We have recently found a connection between the original, real-valued nonlinear Kuramoto model and a corresponding complex-valued system that permits describing the sy...

Recent analyses have found waves of neural activity traveling across entire visual cortical areas in awake animals. These traveling waves modulate excitability of local networks and perceptual sensitivity. The general computational role for these spatiotemporal patterns in the visual system, however, remains unclear. Here, we hypothesize that trave...

We introduce an analytical approach that allows predictions and mechanistic insights into the dynamics of nonlinear oscillator networks with heterogeneous time delays. We demonstrate that time delays shape the spectrum of a matrix associated with the system, leading to the emergence of waves with a preferred direction. We then create analytical pre...

Networked systems have been used to model and investigate the dynamical behavior of a variety of systems. For these systems, different levels of complexity can be considered in the modeling procedure. On one hand, this can offer a more realistic and rich modeling option. On the other hand, it can lead to intrinsic difficulty in analyzing the system...

Multilayer networks have been used to model and investigate the dynamical behavior of a variety of systems. On one hand, this approach offers a more realistic and rich modeling option than single-layer networks. On the other hand, it leads to an intrinsic difficulty in analyzing the system. Here, we introduce an approach to investigate the dynamics...

Understanding the sensitivity of a system's behavior with respect to parameter changes is essential for many applications. This sensitivity may be desired - for instance in the brain, where a large repertoire of different dynamics, particularly different synchronization patterns, is crucial - or may be undesired - for instance in power grids, where...

We introduce an analytical approach that allows predictions and mechanistic insights into the dynamics of nonlinear oscillator networks with heterogeneous time delays. We demonstrate that time delays shape the spectrum of a matrix associated to the system, leading to emergence of waves with a preferred direction. We then create analytical predictio...

One of the simplest mathematical models in the study of nonlinear systems is the Kuramoto model, which describes synchronization in systems from swarms of insects to superconductors. We have recently found a connection between the original, real-valued nonlinear Kuramoto model and a corresponding complex-valued system that permits describing the sy...

Kuramoto networks constitute a paradigmatic model for the investigation of collective behavior in networked systems. Despite many advances in recent years, many open questions remain on the solutions for systems composed of coupled Kuramoto os-cillators on complex networks. In this article, we describe an algebraic method to find equilibrium points...

Time series analysis comprises a wide repertoire of methods for extracting information from data sets. Despite great advances in time series analysis, identifying and quantifying the strength of nonlinear temporal correlations remain a challenge. We have recently proposed a new method based on training a machine learning algorithm to predict the te...

We investigate the role of bistability in the synchronization of a network of identical bursting neurons coupled through an generic electrical mean-field scheme. These neurons can exhibit distinct multistable states and, in particular, bistable behavior is observed when their sodium conductance is varied. With this, we consider three different init...

Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to relia...

In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons’ dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory connections in a sparse random topology, feel no noise or external inputs, and have identical parameters...

Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to relia...

Neurons modeled by the Rulkov map display a variety of dynamic regimes that include tonic spikes and chaotic bursting. Here we study an ensemble of bursting neurons coupled with the Watts-Strogatz small-world topology. We characterize the sequences of bursts using the symbolic method of time-series analysis known as ordinal analysis, which detects...

Despite great advances, the functioning mechanisms behind the interesting phenomena observed in the brain still lacking a fundamental theory, which gives a mathematical description, supports and explains the neural phenomena and neuroscience as a whole. Recent investigations have provided information about the individual properties of neurons, thei...

Despite numerous efforts and advances, neuroscience is still lacking a fundamental theory supporting a mathematical description of the functioning of neural systems. One of the important contributions towards a theory of the brain is the free-energy principle, according to which the brain operates in a way to bound the entropy, minimizing the surpr...

We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patterns, corresponding to either nonsynchronized or phase-synchronized states. Particularly interesting...

We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patterns, corresponding to either non-synchronized or phase-synchronized states. Particularly interesting...

Neurons modeled by the Rulkov map display a variety of dynamic regimes that include tonic spikes and chaotic bursting. Here we study an ensemble of bursting neurons coupled with the Watts-Strogatz small-world topology. We characterize the sequences of bursts using the symbolic method of time-series analysis known as ordinal analysis, which detects...

The recurrence analysis of dynamic systems has been studied since Poincaré’s seminal work. Since then, several approaches have been developed to study recurrence properties in nonlinear dynamical systems. In this work, we study the recently developed entropy of recurrence microstates. We propose a new quantifier, the maximum entropy (Smax). The new...

An important idea in neural information processing is the communication-through-coherence hypothesis, according to which communication between two brain regions is effective only if they are phase-locked. Also of importance is neuronal variability, a phenomenon in which a single neuron's inter-firing times may be highly variable. In this work, we a...

The connection architecture plays an important role in the synchronization of networks, where the presence of local and nonlocal connection structures are found in many systems, such as the neural ones. Here, we consider a network composed of chaotic bursting oscillators coupled through a Watts-Strogatz-small-world topology. The influence of coupli...

Here we investigate the mechanism for explosive synchronization (ES) of a complex neural network composed of nonidentical neurons and coupled by Newman-Watts small-world matrices. We find a range of nonlocal connection probabilities for which the network displays an abrupt transition to phase synchronization, characterizing ES. The mechanism behind...

Phase synchronization of neurons is fundamental for the functioning of the human
brain which can be related to neurological diseases such as Parkinson and/or seizure
behaviors generated by epilepsy. For small-world networks, an atypically high level
of phase synchronization may occur even for unexpected low values of the coupling
strength when comp...

The synchronization of neurons is fundamental for the functioning of the brain since its lack or excess may be related to neurological disorders, such as autism, Parkinson’s and neuropathies such as epilepsy. In this way, the study of synchronization, as well as its suppression in coupled neurons systems, consists of an important multidisciplinary...

The study of synchronization in complex networks is useful for understanding a variety of systems, including neural systems. However, the properties of the transition to synchronization are still not well known. In this work, we analyze the details of the transition to synchronization in complex networks composed of bursting oscillators under small...

We simulate a small-world neural network composed of 2000 thermally sensitive identical Hodgkin–Huxley type neurons investigating the synchronization characteristics as a function of the coupling strength and the temperature of the neurons. The Kuramoto order parameter computed over individual neuron membrane potential signals, and recurrence analy...

We investigate the dynamical properties of two coupled neural networks with 2,048 identical Hodgkin-Huxley type bursting neurons. The internal connection architecture of each network follows a small-world topology and the external connection scheme is based on the local mean field potential, where one network receives the signal from the other. To...

We study the dynamical proprieties of phase synchronization and intermittent behavior of neural systems using a network of networks structure based on an experimentally obtained human connectome for healthy and Alzheimer-affected brains. We consider a network composed of 78 neural subareas (subnetworks) coupled with a mean-field potential scheme. E...

Anomalous phase synchronization describes a synchronization phenomenon occurring even for the weakly coupled network and characterized by a non-monotonous dependence of the synchronization strength on the coupling strength. Its existence may support a theoretical framework to some neurological diseases, such as Parkinson's and some episodes of seiz...

The transition to phase synchronized states of neural networks with bursting dynamics may have nonstationary characteristics, as well as sensitivity to initial conditions. Here, we analyze the paradigmatic network composed of neurons of Rulkov to investigate dynamic properties of the transitions to phase synchronization displayed by networks under...

It is known that neural networks under small-world topology can present anomalous synchronization and nonstationary behavior for weak coupling regimes. Here, we propose methods to suppress the anomalous synchronization and also to diminish the nonstationary behavior occurring in weakly coupled neural network under small-world topology. We consider...

The brain is one of the most complex parts of the human body and even in current days there are many questions with no answers about it. In this way, there are great efforts on world level to develop researches in order to make possible a better understanding of this organ. The current work has the objective of studying the behavior of neural netwo...

We study the stability of asymptotic states displayed by a complex neural network. We focus on the loss of stability of a stationary state of networks using recurrence quantifiers as tools to diagnose local and global stabilities as well as the multistability of a coupled neural network. Numerical simulations of a neural network composed of 1024 ne...

## Projects

Projects (2)

Recent studies have shown non-trivial synchronization phenomena in single networks and network of networks. We have investigated these phenomena considering different mathematical models and complex topologies.