Displaying similar documents to “Dynamics of Stochastic Neuronal Networks and the Connections to Random Graph Theory”

Using R to Build and Assess Network Models in Biology

G. Hartvigsen (2011)

Mathematical Modelling of Natural Phenomena

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In this paper we build and analyze networks using the statistical and programming environment R and the igraph package. We investigate random, small-world, and scale-free networks and test a standard problem of connectivity on a random graph. We then develop a method to study how vaccination can alter the structure of a disease transmission network. We also discuss a variety of other uses for networks in biology.

Local dependency in networks

Miloš Kudělka, Šárka Zehnalová, Zdeněk Horák, Pavel Krömer, Václav Snášel (2015)

International Journal of Applied Mathematics and Computer Science

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Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network's nodes...

Reinforced walk on graphs and neural networks

Józef Myjak, Ryszard Rudnicki (2008)

Studia Mathematica

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A directed-edge-reinforced random walk on graphs is considered. Criteria for the walk to end up in a limit cycle are given. Asymptotic stability of some neural networks is shown.