Dynamical robustness and its structural dependence in biological networks.
J Theor Biol
; 526: 110808, 2021 10 07.
Article
en En
| MEDLINE
| ID: mdl-34118264
ABSTRACT
We discuss the dynamical robustness of biological networks represented by directed graphs, such as neural networks and gene regulatory networks. The theoretical results indicate that networks with low indegree variance and high outdegree variance are dynamically robust. We propose a machine learning method that gives equilibrium states to input-output networks with a recurrent hidden layer. We verify the theory by using the learned networks having various indegree and outdegree distributions. We also show that the basin of attraction of an equilibrium state is narrow when networks are dynamically robust.
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Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Aprendizaje Automático
Idioma:
En
Revista:
J Theor Biol
Año:
2021
Tipo del documento:
Article