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We investigate mechanisms of the typically observed recoverable prevalence in epidemic spreading. Assuming the time-independent connectivity correlations, we analyze the dynamics of spreading on linearly growing scale-free (SF) networks,…

Statistical Mechanics · Physics 2007-05-23 Yukio Hayashi

Developing networks of neural systems can exhibit spontaneous, synchronous activities called neural bursts, which can be important in the organization of functional neural circuits. Before the network matures, the activity level of a burst…

Neurons and Cognition · Quantitative Biology 2017-05-24 Chih-Hsu Huang , Yu-Ting Huang , Chun-Chung Chen , C. K. Chan

Realizations of low firing rates in neural networks usually require globally balanced distributions among excitatory and inhibitory links, while feasibility of temporal coding is limited by neuronal millisecond precision. We show that…

Neurons and Cognition · Quantitative Biology 2015-06-16 Roni Vardi , Amir Goldental , Hagar Marmari , Haya Brama , Edward Stern , Shira Sardi , Pinhas Sabo , Ido Kanter

Node similarity is a significant property driving the growth of real networks. In this paper, based on the observed spreading results we apply the node similarity metrics to reconstruct propagation networks. We find that the reconstruction…

Physics and Society · Physics 2014-10-01 Hao Liao , An Zeng

Many networks of physical and biological interest are characterized by a long-range coupling mediated by a chemical which diffuses through a medium in which oscillators are embedded. We considered a one-dimensional model for this effect for…

We study analytically the dynamics of a network of sparsely connected inhibitory integrate-and-fire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the limit when the number of neurons N tends to…

Disordered Systems and Neural Networks · Physics 2007-05-23 N. Brunel , V. Hakim

In this work we study diffusion in networks with community structure. We first replicate and extend work on networks with non-overlapping community structure. We then study diffusion on network models that have overlapping community…

Physics and Society · Physics 2015-03-19 Fergal Reid , Neil Hurley

Recurrent neural networks are powerful tools for understanding and modeling computation and representation by populations of neurons. Continuous-variable or "rate" model networks have been analyzed and applied extensively for these…

Neurons and Cognition · Quantitative Biology 2016-01-29 Brian DePasquale , Mark M. Churchland , L. F. Abbott

The coding properties of cells with different types of receptive fields have been studied for decades. ON-type neurons fire in response to positive fluctuations of the time-dependent stimulus, whereas OFF cells are driven by negative…

Neurons and Cognition · Quantitative Biology 2009-09-10 Eugenio Urdapilleta , Ines Samengo

Although recent neurophysiological experiments suggest that synchronous neural activity is involved in some perceptual and cognitive processes, the functional role of such coherent neuronal behavior is not well understood. As a first step…

Neurons and Cognition · Quantitative Biology 2007-05-23 Takaaki Aoki , Toshio Aoyagi

Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A…

Neurons and Cognition · Quantitative Biology 2015-06-19 Sadra Sadeh , Stefan Rotter

We investigate spreading properties of solutions of a large class of two-component reaction-diffusion systems, including prey-predator systems as a special case. By spreading properties we mean the long time behaviour of solution fronts…

Analysis of PDEs · Mathematics 2019-07-08 Arnaud Ducrot , Thomas Giletti , Hiroshi Matano

We consider the problem of inferring the functional connectivity of a large-scale computer network from sparse time series of events emitted by its nodes. We do so under the following three domain-specific constraints: (a) non-stationarity…

Machine Learning · Computer Science 2018-02-13 Antoine Messager , George Parisis , Istvan Z Kiss , Robert Harper , Phil Tee , Luc Berthouze

The way in which different types of dynamics unfold in complex networks is intrinsically related to the propagation of activation along nodes, which is strongly affected by the network connectivity. In this work we investigate to which…

Physics and Society · Physics 2008-11-25 Luciano da Fontoura Costa

We study spreading on networks where the contact dynamics between the nodes is governed by a random process and where the inter-contact time distribution may differ from the exponential. We consider a process of imperfect spreading, where…

Physics and Society · Physics 2016-07-07 Martin Gueuning , Jean-Charles Delvenne , Renaud Lambiotte

The objective of the present study is twofold. First, the last developments and validation results of a hybrid model designed to simulate fire patterns in heterogeneous landscapes are presented. The model combines the features of a…

Atmospheric and Oceanic Physics · Physics 2016-02-08 Mohamed Drissi

We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing…

Neurons and Cognition · Quantitative Biology 2011-09-23 Chun-Chung Chen , David Jasnow

This is the third part of our series of work devoted to the dynamics of an epidemic model with nonlocal diffusions and free boundary. This part is concerned with the rate of accelerated spreading for three types of kernel functions when…

Analysis of PDEs · Mathematics 2024-12-09 Lei Li , Mingxin Wang

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates.…

Neurons and Cognition · Quantitative Biology 2015-07-17 Michael A. Schwemmer , Adrienne L. Fairhall , Sophie Denéve , Eric T. Shea-Brown

Two neural networks which are trained on their mutual output bits are analysed using methods of statistical physics. The exact solution of the dynamics of the two weight vectors shows a novel phenomenon: The networks synchronize to a state…

Disordered Systems and Neural Networks · Physics 2007-05-23 Wolfgang Kinzel , Ido Kanter
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