arXiv subjects

Fernando F. Ferreira

Publications and source records attributed to Fernando F. Ferreira.

At least 19 recordsLinked to original sources

arXiv2026

Modulation of Neuronal Firing Modes by Electric Fields in a Thermosensitive FitzHugh-Nagumo Model

We introduce a thermosensitive FitzHugh-Nagumo neuron model extended with a third dynamic variable representing an external electric field. This unified framework enables a biophysically grounded analysis of how combined thermal and electrical stimuli modulate neuronal excitability. The model incorporates temperature dependence, ion charge density, cell radius, and voltage-driven stimulation, capturing their joint effects on membrane polarization and firing dynamics. Using bifurcation analysis, Lyapunov exponents, and interspike interval variability, we identify transitions between spiking, bursting, and chaotic regimes. We further show how periodic electric fields tune these dynamics as a function of stimulus amplitude, frequency, and cellular geometry. Our results provide mechanistic insights into neuronal excitability and suggest avenues for controlling neural activity via hybrid thermal-electrical modulation, with potential applications in neuromodulation therapies and bioelectronics.

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arXiv2026

Dynamic effects of electric field in hybrid coupling thermosensitive neuronal network

The dynamics of thermosensitive FitzHugh--Nagumo neuronal networks under hybrid synaptic coupling and external electric fields are investigated in a ring topology. Numerical simulations show collective behavior ranging from incoherent activity to coherent traveling waves and chimera and multichimera states, arising from the interplay between thermosensitivity, intrinsic electric fields, and the balance between electrical and chemical synapses. Chemical nonlocal coupling is essential for the stabilization of traveling waves and traveling chimera patterns, whereas purely electrical local coupling of chaotic neurons yields only incoherent states. The frequency and spatial extent of the applied field act as key control parameters: low frequencies and localized stimulation can induce or suppress chimera-like domains, while high frequencies have negligible impact on network dynamics. Intrinsic electric fields, controlled by the cell radius r, modulate single-neuron excitability and reshape the network response to external fields, enabling transitions between incoherence, chimera-like states, and global synchronization. These results suggest that weak, spatially targeted electric fields can serve as effective control knobs for complex patterns of activity in thermosensitive hybrid-coupled networks, with potential implications for selective neuromodulation and bio-inspired computing.

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arXiv2026

Topology-Dependent Emergence of Polychronous Neuronal Groups: A Recurrence-Plot Characterization

Polychronous Neuronal Groups (PNGs) reproducible, time-locked spatiotemporal firing cascades stabilised by Spike-Timing-Dependent Plasticity (STDP) and heterogeneous axonal delays provide a combinatorially rich substrate for neural computation whose structural determinants remain poorly understood. We simulate a recurrent network of N=1000 Izhikevich neurons over ten hours of biological time and identify 1545 unique PNGs via an offline event-driven detection algorithm. A parametric Watts-Strogatz topology sweep reveals that the clusteringcoefficient C is the primary structural driver of PNG yield: the transition from a ring-lattice (C~0.35, $\sim\!850$ \PNGs) to a random graph (C~!0.20$, $<\!50$ \PNGs) reduces representational capacity by more than 90%. We further introduce a sparse-dot-product Recurrence Plot (RP) framework that identifies PNGs as unit-slope diagonal structures in the phase-space recurrence matrix, entirely independent of anatomical neuron labelling. Recurrence Quantification Analysis yields DET~0.65, quantifying the reproducibility of the network's dynamical trajectory. Together, the results establish small-world topology as the structural optimum for polychronization and the \RP decoder as a principled, label-free tool for PNG identification.

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arXiv2026

Characterizing some dynamical states in swarmalators system using recurrence analysis

Chimera or chimera-like states arise in a wide variety of networks and their identification remains challenging particularly when mobility prevents index-based ordering of the nodes. In this work, we propose a recurrence analysis based method to identify and characterize chimera states in two distinct dynamical frameworks: a network of chaotic Colpitts oscillators and a system of swarmalators where delayed interactions induce chimera-like dynamics named boiling state. The suggested strategy is based on the joint recurrence plots and entropy-based measures, to capture the spatio-temporal organization. This approach enables a clear discrimination between complete synchronization, quasi-synchronization and disordered regimes, even when conventional order parameters yield ambiguous results. Furthermore, we introduce the degree of independence, which estimates the proportion of dynamically completely independent nodes in the system. This measure provides a robust characterization of transitions between collective states.

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arXiv2025

Annealing approximation in master-node network model

This paper investigates absorbing-state phase transitions in opinion dynamics through a master-node network model analyzed using annealing approximation. We develop a theoretical framework examining three fundamental regimes: systems converging to complete disagreement, complete consensus, or both states depending on initial conditions. The phase behavior is governed by two key chiral parameters: $R$ measuring right-oriented influence and $L$ measuring left-oriented influence in the network interactions. Our analysis reveals a rich phase diagram featuring both continuous and discontinuous transitions between disordered and ordered phases. The discontinuous transition emerges in systems with two absorbing states, where the final configuration depends critically on initial opinion distributions. The annealing approximation provides fundamental insights into how asymmetric social influences (chirality) shape collective opinion formation, acting as a symmetry-breaking element that drives the system toward polarization or consensus.

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arXiv2024

Expected and unexpected routes to synchronization in a system of swarmalators

Systems of oscillators whose internal phases and spatial dynamics are coupled, swarmalators, present diverse collective behaviors which in some cases lead to explosive synchronization in a finite population as a function of the coupling parameter between internal phases. Near the synchronization transition, the phase energy of the particles is represented by the XY model, and they undergo a transition which can be of the first order or second depending on the distribution of natural frequencies of their internal dynamics. The first order transition is obtained after an intermediate state (Static Wings Phase Wave state (SWPW)) from which the nodes, in cascade over time, achieve complete phase synchronization at a precise value of the coupling constant. For a particular case of natural frequencies distribution, a new phenomenon of Rotational Splintered Phase Wave state (RSpPW) is observed and leads progressively to synchronization through clusters switching alternatively from one to two and for which the frequency decreases as the phase coupling increases.

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arXiv2022

Critical Exponents of Master-Node Network Model

The dynamics of competing opinions in social network play an important role in society, with many applications in diverse social contexts as consensus, elections, morality and so on. Here we study a model of interacting agents connected in networks to analyze their decision stochastic process. We consider a first-neighbor interaction between agents in a one-dimensional network with a shape of ring topology. Moreover, some agents are also connected to a hub, or master node, that has preferential choice or bias. Such connections are quenched. As the main results, we observed a continuous non-equilibrium phase transition to an absorbing state as a function of control parameters. By using the finite size scaling method, we analyzed the static and dynamic critical exponents to show that this model probably cannot match any universality class already known.

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arXiv2021

Surrogate Monte Carlo

This article proposes an artificial data generating algorithm that is simple and easy to customize. The fundamental concept is to perform random permutation of Monte Carlo generated random numbers which conform to the unconditional probability distribution of the original real time series. Similar to constraint surrogate methods, random permutations are only accepted if a given objective function is minimized. The objective function is selected in order to describe the most important features of the stochastic process. The algorithm is demonstrated by producing simulated log-returns of the S\&P 500 stock index.

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arXiv2019

Detailed study of a moving average trading rule

We present a detailed study of the performance of a trading rule that uses moving average of past returns to predict future returns on stock indexes. Our main goal is to link performance and the stochastic process of the traded asset. Our study reports short, medium and long term effects by looking at the Sharpe ratio (SR). We calculate the Sharpe ratio of our trading rule as a function of the probability distribution function of the underlying traded asset and compare it with data. We show that if the performance is mainly due to presence of autocorrelation in the returns of the traded assets, the SR as a function of the portfolio formation period (look-back) is very different from performance due to the drift (average return). The SR shows that for look-back periods of a few months the investor is more likely to tap into autocorrelation. However, for look-back larger than few months, the drift of the asset becomes progressively more important. Finally, our empirical work reports a new long-term effect, namely oscillation of the SR and propose a non-stationary model to account for such oscillations.

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arXiv2019

A New (aleph) Stochastic Quenched Disorder Model for Interaction of Network- Master node

We consider a first neighbor interaction where agents are spread through a uni-dimensional network. Some agents are also connected to a hub, or master node, who has preferential values (or orientation). The role of master node is to persuade some individuals to follow a specific orientation, subject to a probability of successful persuasion. The connections between master node and the network society are quenched in disorder. Despite its simplicity, we found a phase transition from disorder to order for three different control parameters. We also discuss how this model may be useful as a framework to study the spread of morality, innovation, opinion formation and consensus. Is important to recall the route from disorder to order in social systems still a great challenge. We hope to contribute with a novel approach to model a this issues.

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arXiv2016

Competing metabolic strategies in a multilevel selection model

The interplay between energy efficiency and evolutionary mechanisms is addressed. One important question is how evolutionary mechanisms can select for the optimised usage of energy in situations where it does not lead to immediate advantage. For example, this problem is of great importance to improve our understanding about the major transition from unicellular to multicellular form of life. The immediate advantage of gathering efficient individuals in an energetic context is not clear. Although this process increases relatedness among individuals, it also increases local competition. To address this question, we propose a model of two competing metabolic strategies that makes explicit reference to the resource usage. We assume the existence of an efficient strain, which converts resource into energy at high efficiency but displays a low rate of resource consumption, and an inefficient strain, which consumes resource at a high rate with a low efficiency in converting it to energy. We explore the dynamics in both well-mixed and structured populations. The selection for optimised energy usage is measured by the likelihood of that an efficient strain can invade a population only comprised by inefficient strains. It is found that the region of the parameter space at which the efficient strain can thrive in structured populations is always larger than observed in well-mixed populations. In fact, in well-mixed populations the efficient strain is only evolutionarily stable in the domain whereupon there is no evolutionary dilemma. We also observe that small group sizes enhance the chance of invasion by the efficient strain in spite of increasing the competition among relatives. This outcome corroborates the key role played by kin selection and shows that the group dynamics relied on group expansion, overlapping generations and group split can balance the negative effects of local competition.

q-bio.PE↗

arXiv2014

Information ratio analysis of momentum strategies

In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of a momentum strategy, and show that if momentum is mainly due to the positive autocorrelation in returns, IR as a function of the portfolio formation period (look-back) is very different from momentum due to the drift (average return). The IR shows that for look-back periods of a few months, the investor is more likely to tap into autocorrelation. However, for look-back periods closer to 1 year, the investor is more likely to tap into the drift. We compare the historical data to the theoretical IR by constructing stationary periods. The empirical study finds that there are periods/regimes where the autocorrelation is more important than the drift in explaining the IR (particularly pre-1975) and others where the drift is more important (mostly after 1975). We conclude our study by applying our momentum strategy to 100 plus years of the Dow-Jones Industrial Average. We report damped oscillations on the IR for look-back periods of several years and model such oscilations as a reversal to the mean growth rate.

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arXiv2013

Local attractors, degeneracy and analyticity: symmetry effects on the locally coupled Kuramoto model

In this work we study the local coupled Kuramoto model with periodic boundary conditions. Our main objective is to show how analytical solutions may be obtained from symmetry assumptions, and while we proceed on our endeavor we show apart from the existence of local attractors, some unexpected features resulting from the symmetry properties, such as intermittent and chaotic period phase slips, degeneracy of stable solutions and double bifurcation composition. As a result of our analysis, we show that stable fixed points in the synchronized region may be obtained with just a small amount of the existent solutions, and for a class of natural frequencies configuration we show analytical expressions for the critical synchronization coupling as a function of the number of oscillators, both exact and asymptotic.

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arXiv2012

Identifying financial crises in real time

Following the thermodynamic formulation of multifractal measure that was shown to be capable of detecting large fluctuations at an early stage, here we propose a new index which permits us to distinguish events like financial crisis in real time . We calculate the partition function from where we obtain thermodynamic quantities analogous to free energy and specific heat. The index is defined as the normalized energy variation and it can be used to study the behavior of stochastic time series, such as financial market daily data. Famous financial market crashes - Black Thursday (1929), Black Monday (1987) and Subprime crisis (2008) - are identified with clear and robust results. The method is also applied to the market fluctuations of 2011. From these results it appears as if the apparent crisis of 2011 is of a different nature from the other three. We also show that the analysis has forecasting capabilities.

q-fin.ST↗

arXiv2012

Stock prices assessment: proposal of a new index based on volume weighted historical prices through the use of computer modeling

The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes. In this paper we present a computer model that could fulfill this gap, proposing a new index to evaluate stock prices based on their historical prices and volumes traded. Besides the model can be considered mathematically very simple, it was able to improve significantly the performance of agents operating with real financial data. Based on the results obtained, and also on the very intuitive logic of our model, we believe that the index proposed here can be very useful to help investors on the activity of determining ideal price ranges for buying and selling stocks in the financial market.

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arXiv2011

Multistable behavior above synchronization in a locally coupled Kuramoto model

A system of nearest neighbors Kuramoto-like coupled oscillators placed in a ring is studied above the critical synchronization transition. We find a richness of solutions when the coupling increases, which exists only within a solvability region (SR). We also find that they posses different characteristics, depending on the section of the boundary of the SR where the solutions appear. We study the birth of these solutions and how they evolve when {K} increases, and determine the diagram of solutions in phase space.

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arXiv2009

A Markovian Model Market - Akerlof's Lemmons and the Asymmetry of Information

In this work we study an economic agent based model under different asymmetric information degrees. This model is quite simple and can be treated analytically since the buyers evaluate the quality of a certain good taking into account only the quality of the last good purchased plus her perceptive capacity β. As a consequence the system evolves according to a stationary Markovian stochastic process. The value of a product offered by the seller increases with quality according to the exponent α, which is a measure of technology. It incorporates all the technological capacity of production systems such as education, scientific development and techniques that change the productivity growth. The technological level plays an important role to explain how the asymmetry of information may affect the market evolution in this model. We observe that, for high technological levels, the market can control adverse selection. The model allows us to compute the maximum asymmetric information degree before market collapse. Below this critical point the market evolves during a very limited time and then dies out completely. When βis closer to 1(symmetric information), the market becomes more profitable for high quality goods, although high and low quality markets coexist. All the results we obtained from the model are analytical and the maximum asymmetric information level is a consequence of an ergodicity breakdown in the process of quality evaluation.

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arXiv2008

Transition to complete synchronization in phase coupled oscillators with nearest neighbours coupling

We investigate synchronization in a Kuramoto-like model with nearest neighbour coupling. Upon analyzing the behaviour of individual oscillators at the onset of complete synchronization, we show that the time interval between bursts in the time dependence of the frequencies of the oscillators exhibits universal scaling and blows up at the critical coupling strength. We also bring out a key mechanism that leads to phase locking. Finally, we deduce forms for the phases and frequencies at the onset of complete synchronization.

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