Paper Info
Title
Dynamical stability in random Boolean networks.
Abstract
In this work we propose a new approach to the stability analysis of Random Boolean Networks (RBNs). In particular, we focus on two families of RBNs with k=2, in which only two subsets of canalizing Boolean function are allowed, and we show that the usual measure of RBNs stability - sometimes known as the Derrida parameter (DP) - is similar in the two cases, while their dynamics (e. g. number of attractors, length of cycles, number of frozen nodes) are different. For this reason we have introduced a new measure, that we have called attractor sensitivity (AS), computed in a way similar to DP, but perturbing only the attractors of the networks. It is proven that AS turns out to be different in the two cases analyzed. Finally, we investigate Boolean networks with k=3, tailored to solve the Density Classification Problem, and we show that also in this case the AS describes the system dynamical stability.
Year
DOI
Venue
2011
10.3233/978-1-60750-972-1-120
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
Random Boolean Networks,criticality,sensitivity,Derrida parameter
Boolean network,Computer science,Boolean model,Theoretical computer science,Artificial intelligence,Random dynamical system,Criticality,Machine learning,Random compact set
Conference
Volume
ISSN
Citations 
234
0922-6389
3
PageRank 
References 
Authors
0.45
0
4
Name
Order
Citations
PageRank
Davide Campioli160.91
Marco Villani218835.04
Irene Poli3365.78
Roberto Serra420329.48