Attraction by pairwise coherence explains the emergence of ideological sorting.

Political polarization has become a growing concern in democratic societies, as it drives tribal alignments and erodes civic deliberation among citizens. Given its prevalence across different countries, previous research has sought to understand under which conditions people tend to endorse extreme opinions. However, in polarized contexts, citizens not only adopt more extreme views but also become correlated across issues that are, a priori, seemingly unrelated.

Promoting Erroneous Divergent Opinions Increases the Wisdom of Crowds.

The aggregation of many lay judgments generates surprisingly accurate estimates. This phenomenon, called the "wisdom of crowds," has been demonstrated in domains such as medical decision-making and financial forecasting. Previous research identified two factors driving this effect: the accuracy of individual assessments and the diversity of opinions.

Reconstructing social sensitivity from evolution of content volume in Twitter.

We set up a simple mathematical model for the dynamics of public interest in terms of media coverage and social interactions. We test the model on a series of events related to violence in the US during 2020, using the volume of tweets and retweets as a proxy of public interest, and the volume of news as a proxy of media coverage. The model successfully fits the data and allows inferring a measure of social sensibility that correlates with human mobility data.

News sharing on Twitter reveals emergent fragmentation of media agenda and persistent polarization.

Unlabelled: News sharing on social networks reveals how information disseminates among users. This process, constrained by user preferences and social ties, plays a key role in the formation of public opinion. In this work, we used bipartite news-user networks to study the news sharing behavior of main Argentinian media outlets in Twitter.

Unconsciousness reconfigures modular brain network dynamics.

The dynamic core hypothesis posits that consciousness is correlated with simultaneously integrated and differentiated assemblies of transiently synchronized brain regions. We represented time-dependent functional interactions using dynamic brain networks and assessed the integrity of the dynamic core by means of the size and flexibility of the largest multilayer module. As a first step, we constrained parameter selection using a newly developed benchmark for module detection in heterogeneous temporal networks.

Analytical approach to the Axelrod model based on similarity vectors.

Complex problems of social interaction are usually studied within the framework of agent-based models. Some of these problems, such as issue alignment and opinion polarization, are better suited in the framework of n-dimensional opinion space. Although this kind of complex problem may be explored by numerical simulations, these simulations can hinder our ability to obtain general results.

Polarizing crowds: Consensus and bipolarization in a persuasive arguments model.

Understanding the opinion formation dynamics in social systems is of vast relevance in diverse aspects of society. In particular, it is relevant for political deliberation and other group decision-making processes. Although previous research has reported different approaches to model social dynamics, most of them focused on interaction mechanisms where individuals modify their opinions in line with the opinions of others, without invoking a latent mechanism of argumentation.

Erdós-Rényi phase transition in the Axelrod model on complete graphs.

The Axelrod model has been widely studied since its proposal for social influence and cultural dissemination. In particular, the community of statistical physics focused on the presence of a phase transition as a function of its two main parameters, F and Q. In this work, we show that the Axelrod model undergoes a second-order phase transition in the limit of F→∞ on a complete graph.

On wakefulness fluctuations as a source of BOLD functional connectivity dynamics.

Human brain dynamics and functional connectivity fluctuate over a range of temporal scales in coordination with internal states and environmental demands. However, the neurobiological significance and consequences of functional connectivity dynamics during rest have not yet been established. We show that the coarse-grained clustering of whole-brain dynamic connectivity measured with magnetic resonance imaging reveals discrete patterns (dynamic connectivity states) associated with wakefulness and sleep.

Dynamical Signatures of Structural Connectivity Damage to a Model of the Brain Posed at Criticality.

Synchronization of brain activity fluctuations is believed to represent communication between spatially distant neural processes. These interareal functional interactions develop in the background of a complex network of axonal connections linking cortical and subcortical neurons, termed the human "structural connectome." Theoretical considerations and experimental evidence support the view that the human brain can be modeled as a system operating at a critical point between ordered (subcritical) and disordered (supercritical) phases.

The Undecided Have the Key: Interaction-Driven Opinion Dynamics in a Three State Model.

The effects of interpersonal interactions on individual's agreements result in a social aggregation process which is reflected in the formation of collective states, as for instance, groups of individuals with a similar opinion about a given issue. This field, which has been a longstanding concern of sociologists and psychologists, has been extended into an area of experimental social psychology, and even has attracted the attention of physicists and mathematicians. In this article, we present a novel model of opinion formation in which agents may either have a strict preference for a choice, or be undecided.

Brain organization into resting state networks emerges at criticality on a model of the human connectome.

The relation between large-scale brain structure and function is an outstanding open problem in neuroscience. We approach this problem by studying the dynamical regime under which realistic spatiotemporal patterns of brain activity emerge from the empirically derived network of human brain neuroanatomical connections. The results show that critical dynamics unfolding on the structural connectivity of the human brain allow the recovery of many key experimental findings obtained from functional magnetic resonance imaging, such as divergence of the correlation length, the anomalous scaling of correlation fluctuations, and the emergence of large-scale resting state networks.

Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis.

Functional magnetic resonance imaging (fMRI) techniques have contributed significantly to our understanding of brain function. Current methods are based on the analysis of gradual and continuous changes in the brain blood oxygenated level dependent (BOLD) signal. Departing from that approach, recent work has shown that equivalent results can be obtained by inspecting only the relatively large amplitude BOLD signal peaks, suggesting that relevant information can be condensed in discrete events.

Disrupted functional connectivity of the pain network in fibromyalgia.

Objective: To investigate the impact of chronic pain on brain dynamics at rest.

Methods: Functional connectivity was examined in patients with fibromyalgia (FM) (n = 9) and healthy controls (n = 11) by calculating partial correlations between low-frequency blood oxygen level-dependent fluctuations extracted from 15 brain regions.

Results: Patients with FM had more positive and negative correlations within the pain network than healthy controls.

Modular organization of brain resting state networks in chronic back pain patients.

Recent work on functional magnetic resonance imaging large-scale brain networks under resting conditions demonstrated its potential to evaluate the integrity of brain function under normal and pathological conditions. A similar approach is used in this work to study a group of chronic back pain patients and healthy controls to determine the impact of long enduring pain over brain dynamics. Correlation networks were constructed from the mutual partial correlations of brain activity's time series selected from ninety regions using a well validated brain parcellation atlas.

Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state.

Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed).

Brain resting state is disrupted in chronic back pain patients.

Recent brain functional magnetic resonance imaging (fMRI) studies have shown that chronic back pain (CBP) alters brain dynamics beyond the feeling of pain. In particular, the response of the brain default mode network (DMN) during an attention task was found abnormal. In the present work similar alterations are demonstrated for spontaneous resting patterns of fMRI brain activity over a population of CBP patients (n=12, 29-67 years old, mean=51.

Ising-like dynamics in large-scale functional brain networks.

Brain "rest" is defined--more or less unsuccessfully--as the state in which there is no explicit brain input or output. This work focuses on the question of whether such state can be comparable to any known dynamical state. For that purpose, correlation networks from human brain functional magnetic resonance imaging are contrasted with correlation networks extracted from numerical simulations of the Ising model in two dimensions at different temperatures.

Coincidence detection of inharmonic pulses in a nonlinear crystal.

Two trains of light pulses at periods that are equally shifted from the harmonics of a missing fundamental are combined in a nonlinear crystal. As a result of a noncollinear phase-matched second-order nonlinear generation, a new train of pulses is obtained. When the temporal width of the input pulses is large, the frequency of the resulting pulse train follows the observations from classical experiments on the perception of virtual pitch by the brain.

Episodic synchronization in dynamically driven neurons.

We examine the response of type II excitable neurons to trains of synaptic pulses, as a function of the pulse frequency and amplitude. Similarly to the case of harmonic inputs, these neurons exhibit a resonant behavior also for pulsed inputs. We interpret this phenomenon in terms of the subthreshold response of the neuron.

Ghost resonance in a pool of heterogeneous neurons.

We numerically study the subharmonic response of a heterogeneous pool of neurons to a pair of independent inputs. The neurons are stimulated with periodic pulse trains of frequencies f(1)=2 Hz and f(2)=3 Hz, and with inharmonic pulses whose frequencies f(1) and f(2) are equally shifted an amount Delta f. When both inputs are subthreshold, we find that the neurons respond at a frequency equal to f(2)-f(1) in the harmonic situation (Delta f=0), that increases linearly with Delta f in the inharmonic case.

Role of chemical synapses in coupled neurons with noise.

We examine the behavior in the presence of noise of an array of Morris-Lecar neurons coupled via chemical synapses. Special attention is devoted to comparing this behavior with the better known case of electrical coupling arising via gap junctions. In particular, our numerical simulations show that chemical synapses are more efficient than gap junctions in enhancing coherence at an optimal noise (what is known as array-enhanced coherence resonance): in the case of (nonlinear) chemical coupling, we observe a substantial increase in the stochastic coherence of the system, in comparison with (linear) electrical coupling.

Neural mechanism for binaural pitch perception via ghost stochastic resonance.

We present a physiologically plausible binaural mechanism for the perception of the pitch of complex sounds via ghost stochastic resonance. In this scheme, two neurons are driven by noise and a different periodic signal each (with frequencies f(1)=kf(0) and f(2)=(k+1)f(0), where k>1), and their outputs (plus noise) are applied synaptically to a third neuron. Our numerical results, using the Morris-Lecar neuron model with chemical synapses explicitly considered, show that intermediate noise levels enhance the response of the third neuron at frequencies close to f(0), as in the cases previously described of ghost resonance.