5,307 research outputs found
Optimal network topologies for information transmission in active networks
This work clarifies the relation between network circuit (topology) and
behavior (information transmission and synchronization) in active networks,
e.g. neural networks. As an application, we show how to determine a network
topology that is optimal for information transmission. By optimal, we mean that
the network is able to transmit a large amount of information, it possesses a
large number of communication channels, and it is robust under large variations
of the network coupling configuration. This theoretical approach is general and
does not depend on the particular dynamic of the elements forming the network,
since the network topology can be determined by finding a Laplacian matrix (the
matrix that describes the connections and the coupling strengths among the
elements) whose eigenvalues satisfy some special conditions. To illustrate our
ideas and theoretical approaches, we use neural networks of electrically
connected chaotic Hindmarsh-Rose neurons.Comment: 20 pages, 12 figure
Uniform synchronous criticality of diversely random complex networks
We investigate collective synchronous behaviors in random complex networks of
limit-cycle oscillators with the non-identical asymmetric coupling scheme, and
find a uniform coupling criticality of collective synchronization which is
independent of complexity of network topologies. Numerically simulations on
categories of random complex networks have verified this conclusion.Comment: 8 pages, 4 figure
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
- …