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Information Transmission in Dynamical Networks: The Normal Network Case.
Baggio, Giacomo; Rutten, Virginia; Hennequin, Guillaume; Zampieri, Sandro.
Afiliación
  • Baggio G; Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA.
  • Rutten V; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK and the Gatsby Computational Neuroscience Unit, University College London, London, UK.
  • Hennequin G; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
  • Zampieri S; Dipartimento di Ingegneria dell'Informazione, Università di Padova, Padova, Italy.
Proc IEEE Conf Decis Control ; 20182019 Apr 26.
Article en En | MEDLINE | ID: mdl-31327889
Reliable information processing is a hallmark of many physical and biological networked systems. In this paper, we propose a novel framework for modelling information transmission within a linear dynamical network. Information propagation is modelled by means of a digital communication protocol that takes into account the realistic phenomenon of inter-symbol interference. Building on this framework, we adopt Shannon information rate to quantify the amount of information that can be reliably sent over the network within a fixed time window. We investigate how the latter information metric is affected by the connectivity structure of the network. Here, we focus in particular on networks characterized by a normal adjacency matrix. We show that for such networks the maximum achievable information rate depends only on the spectrum of the adjacency matrix. We then provide numerical results suggesting that non-normal network architectures could benefit information transmission in our framework.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Proc IEEE Conf Decis Control Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Proc IEEE Conf Decis Control Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos