arXiv subjects

Antonio C. Roque

Publications and source records attributed to Antonio C. Roque.

12 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.

nlin.CD↗

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.

nlin.CD↗

arXiv2021

Impact of the activation rate of the hyperpolarization-activated current $I_{\rm h}$ on the neuronal membrane time constant and synaptic potential duration

The temporal dynamics of membrane voltage changes in neurons is controlled by ionic currents. These currents are characterized by two main properties: conductance and kinetics. The hyperpolarization-activated current ($I_{\rm h}$) strongly modulates subthreshold potential changes by shortening the excitatory postsynaptic potentials and decreasing their temporal summation. Whereas the shortening of the synaptic potentials caused by the $I_{\rm h}$ conductance is well understood, the role of the $I_{\rm h}$ kinetics remains unclear. Here, we use a model of the $I_{\rm h}$ current model with either fast or slow kinetics to determine its influence on the membrane time constant ($τ_m$) of a CA1 pyramidal cell model. Our simulation results show that the $I_{\rm h}$ with fast kinetics decreases $τ_m$ and attenuates and shortens the excitatory postsynaptic potentials more than the slow $I_{\rm h}$. We conclude that the $I_{\rm h}$ activation kinetics is able to modulate $τ_m$ and the temporal properties of excitatory postsynaptic potentials (EPSPs) in CA1 pyramidal cells. In order to elucidate the mechanisms by which $I_{\rm h}$ kinetics controls $τ_m$, we propose a new concept called "time scaling factor". Our main finding is that the $I_{\rm h}$ kinetics influences $τ_m$ by modulating the contribution of the $I_{\rm h}$ derivative conductance to $τ_m$.

q-bio.NC↗

arXiv2021

Modeling and characterizing stochastic neurons based on in vitro voltage-dependent spike probability functions

Neurons in the nervous system are submitted to distinct sources of noise, such as ionic-channel and synaptic noise, which introduces variability in their responses to repeated presentations of identical stimuli. This motivates the use of stochastic models to describe neuronal behavior. In this work, we characterize an intrinsically stochastic neuron model based on a voltage-dependent spike probability function. We determine the effect of the intrinsic noise in single neurons by measuring the spike time reliability and study the stochastic resonance phenomenon. The model was able to show increased reliability for non-zero intrinsic noise values, according to what is known from the literature, and the addition of intrinsic stochasticity in it enhanced the region in which stochastic resonance is present. We proceeded to the study at the network level where we investigated the behavior of a random network composed of stochastic neurons. In this case, the addition of an extra dimension, represented by the intrinsic noise, revealed dynamic states of the system that could not be found otherwise. Finally, we propose a method to estimate the spike probability curve from in vitro electrophysiological data.

q-bio.NC↗

arXiv2021

Building a model of the brain: from detailed connectivity maps to network organization

The field of computational modeling of the brain is advancing so rapidly that now it is possible to model large scale networks representing different brain regions with a high level of biological detail in terms of numbers and synapses. For a theoretician approaching a neurobiological question, it is important to analyze the pros and cons of each of the models available. Here, we provide a tutorial review on recent models for different brain circuits, which are based on experimentally obtained connectivity maps. We discuss particularities that may be relevant to the modeler when choosing one of the reviewed models. The objective of this review is to give the reader a fair notion of the computational models covered, with emphasis on the corresponding connectivity maps, and how to use them.

physics.bio-ph↗

arXiv2021

Granger causality in the frequency domain: derivation and applications

Physicists are starting to work in areas where noisy signal analysis is required. In these fields, such as Economics, Neuroscience, and Physics, the notion of causality should be interpreted as a statistical measure. We introduce to the lay reader the Granger causality between two time series and illustrate ways of calculating it: a signal $X$ ``Granger-causes'' a signal $Y$ if the observation of the past of $X$ increases the predictability of the future of $Y$ when compared to the same prediction done with the past of $Y$ alone. In other words, for Granger causality between two quantities it suffices that information extracted from the past of one of them improves the forecast of the future of the other, even in the absence of any physical mechanism of interaction. We present derivations of the Granger causality measure in the time and frequency domains and give numerical examples using a non-parametric estimation method in the frequency domain. Parametric methods are addressed in the Appendix. We discuss the limitations and applications of this method and other alternatives to measure causality.

physics.data-an↗

arXiv2020

Optimal interplay between synaptic strengths and network structure enhances activity fluctuations and information propagation in hierarchical modular networks

In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here we use an information-theoretical approach to investigate activity propagation in spiking networks with hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under increase of synaptic strength and number of modules we find that these increases are associated with two different effects: (i) increase of autocorrelations among individual neurons, and (ii) increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks.v

q-bio.NC↗

arXiv2019

Asymmetrical voltage response in resonant neurons shaped by nonlinearities

The conventional impedance profile of a neuron can identify the presence of resonance and other properties of the neuronal response to oscillatory inputs, such as nonlinear response amplifications, but it cannot distinguish other nonlinear properties such as asymmetries in the shape of the voltage response envelope. Experimental observations have shown that the response of neurons to oscillatory inputs preferentially enhances either the upper or lower part of the voltage envelope in different frequency bands. These asymmetric voltage responses arise in a neuron model when it is submitted to high enough amplitude oscillatory currents of variable frequencies. We show how the nonlinearities associated to different ionic currents or present in the model as captured by its voltage equation lead to asymmetrical response and how high amplitude oscillatory currents emphasize this response. We propose a geometrical explanation for the phenomenon where asymmetries result not only from nonlinearities in their activation curves but also from nonlinearites captured by the nullclines in the phase-plane diagram and from the system's time-scale separation. In addition, we identify an unexpected frequency-dependent pattern which develops in the gating variables of these currents and is a product of strong nonlinearities in the system as we show by controlling such behavior by manipulating the activation curve parameters. The results reported in this paper shed light on the ionic mechanisms by which brain embedded neurons process oscillatory information.

q-bio.NC↗

arXiv2018

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.

q-bio.NC↗

arXiv2018

Interplay of activation kinetics and the derivative conductance determines resonance properties of neurons

In a neuron with hyperpolarization activated current ($I_h$), the correct input frequency leads to an enhancement of the output response. This behavior is known as resonance and is well described by the neuronal impedance. In a simple neuron model we derive equations for the neuron's resonance and we link its frequency and existence with the biophysical properties of $I_h$. For a small voltage change, the component of the ratio of current change to voltage change ($dI/dV$) due to the voltage-dependent conductance change ($dg/dV$) is known as derivative conductance ($G_h^{Der}$). We show that both $G_h^{Der}$ and the current activation kinetics (characterized by the activation time constant $τ_h$) are mainly responsible for controlling the frequency and existence of resonance. The increment of both factors ($G_h^{Der}$ and $τ_h$) greatly contributes to the appearance of resonance. We also demonstrate that resonance is voltage dependent due to the voltage dependence of $G_h^{Der}$. Our results have important implications and can be used to predict and explain resonance properties of neurons with the $I_h$ current.

q-bio.NC↗

arXiv2008

The Nonequilibrium Nature of Culinary Evolution

Food is an essential part of civilization, with a scope that ranges from the biological to the economic and cultural levels. Here we study the statistics of ingredients and recipes taken from Brazilian, British, French, and Medieval cookbooks. We find universal distributions with scale invariant behavior. We propose a copy-mutate process to model culinary evolution that fits very well our empirical data. We find a cultural founder effect produced by the nonequilibrium dynamics of the model. Both the invariant and idiosyncratic aspects of culture are accounted by our model, which may have applications in other kinds of evolutionary processes.

physics.soc-ph↗

arXiv2004

Physics of Psychophysics: Stevens and Weber-Fechner laws are transfer functions of excitable media

Sensory arrays made of coupled excitable elements can improve both their input sensitivity and dynamic range due to collective non-linear wave properties. This mechanism is studied in a neural network of electrically coupled (e.g. via gap junctions) elements subject to a Poisson signal process. The network response interpolates between a Weber-Fechner logarithmic law and a Stevens power law depending on the relative refractory period of the cell. Therefore, these non-linear transformations of the input level could be performed in the sensory periphery simply due to a basic property: the transfer function of excitable media.

cond-mat.dis-nn↗