The architectonic subdivisions of the brain are believed to be functional modules, each processing parts of global functions. Previously, we showed that neurons in different regions operate in different firing regimes in monkeys. It is possible that firing regimes reflect differences in underlying information processing, and consequently the firing regimes in homologous regions across animal species might be similar. We analyzed neuronal spike trains recorded from behaving mice, rats, cats, and monkeys. The firing regularity differed systematically, with differences across regions in one species being greater than the differences in similar areas across species. Neuronal firing was consistently most regular in motor areas, nearly random in visual and prefrontal/medial prefrontal cortical areas, and bursting in the hippocampus in all animals examined. This suggests that firing regularity (or irregularity) plays a key role in neural computation in each functional subdivision, depending on the types of information being carried.Key words: firing irregularity/regularity; interspecies similarity; neuronal firing pattern; neuronal firing regime Significance StatementBy analyzing neuronal spike trains recorded from mice, rats, cats, and monkeys, we found that different brain regions have intrinsically different firing regimes that are more similar in homologous areas across species than across areas in one species. Because different regions in the brain are specialized for different functions, the present finding suggests that the different activity regimes of neurons are important for supporting different functions, so that appropriate neuronal codes can be used for different modalities.
Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations. DOI: 10.1103/PhysRevLett.119.108301 Introduction.-Social contact networks-on which infectious diseases occur in humans and animals or viral information spreads online and offline-are mostly dynamic. Switching of partners and the (usually nonMarkovian) activity of individuals, for example, shape network dynamics on such temporal networks [1][2][3]. A better understanding of epidemic dynamics on temporal networks is needed to help improve predictions of, and interventions in, emergent infectious diseases, to design vaccination strategies, and to identify viral marketing opportunities. This is particularly so because what we know about epidemic processes on static networks [4][5][6][7] is only valid when the time scales of the network dynamics and of the infectious processes are well separated. In fact, temporal properties of networks, such as long-tailed distributions of intercontact times, temporal and cross-edge correlation in intercontact times, and entries and exits of nodes, considerably alter how infections spread in a network [1][2][3]8,9].In the present study, we focus on a relatively neglected component of temporal networks, i.e., the number of concurrent contacts that a node has. Even if two temporal networks are the same when aggregated over a time horizon, they may be different as temporal networks due to different levels of concurrency. Concurrency is a longstanding concept in epidemiology, in particular in the context of monogamy or polygamy affecting sexually transmitted infections [10][11][12]. Modeling studies to date largely agree that a level of high concurrency (e.g., polygamy as opposed to monogamy) enhances epidemic spreading in a population. However, this finding, while intuitive, lacks theoretical underpinning. First, some models assume that the mean degree, or equivalently the average contact rate, of nodes increases as the concurrency increases [13][14][15][16]. In these cases, the observed enhancement in epidemic spreading is an obvious outcome of a higher density of edges rather than a high concurrency. Second, other models that vary the level
Self-exciting point processes describe the manner in which every event facilitates the occurrence of succeeding events, as in the case of epidemics or human activity. By increasing excitability, the event occurrences start to exhibit bursts even in the absence of external stimuli. We revealed that the transition is uniquely determined by the average number of events added by a single event, 1-1/√2≈0.2929, independently of the temporal excitation profile. We further extended the theory to multidimensional processes, to be able to incite or inhibit bursting in networks of agents by altering their connections.
A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading.
There is a commonality among contagious diseases, tweets, and neuronal firings that past events facilitate the future occurrence of events. The spread of events has been extensively studied such that the systems exhibit catastrophic chain reactions if the interaction represented by the ratio of reproduction exceeds unity; however, their subthreshold states are not fully understood. Here, we report that these systems are possessed by nonstationary cascades of event-occurrences already in the subthreshold regime. Event cascades can be harmful in some contexts, when the peak-demand causes vaccine shortages, heavy traffic on communication lines, but may be beneficial in other contexts, such that spontaneous activity in neural networks may be used to generate motion or store memory. Thus it is important to comprehend the mechanism by which such cascades appear, and consider controlling a system to tame or facilitate fluctuations in the event-occurrences. The critical interaction for the emergence of cascades depends greatly on the network structure in which individuals are connected. We demonstrate that we can predict whether cascades may emerge, given information about the interactions between individuals. Furthermore, we develop a method of reallocating connections among individuals so that event cascades may be either impeded or impelled in a network.
New ideas and technologies adopted by a small number of individuals occasionally spread globally through a complex web of social ties. Here, we present a simple and general approximation method, namely, a message-passing approach, that allows us to describe the diffusion processes on (sparse) random networks in an almost exact manner. We consider two classes of binary-action games where the best pure strategies for individual players are characterized as variants of the threshold rule. We verify that the dynamics of diffusion observed on synthetic networks are accurately replicated by the message-passing equation, whose fixed point corresponds to a Nash equilibrium, while the conventional mean-field method tends to overestimate the size and frequency of diffusion. Generalized cascade conditions under which a global diffusion can occur are also provided. We extend the framework to analyze multiplex networks in which social interactions take place in multiple layers.
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