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arxiv: nlin/0408006 · v3 · submitted 2004-08-02 · 🌊 nlin.AO · cond-mat.stat-mech· cs.CC· nlin.CG· q-bio.MN· q-bio.QM

Introduction to Random Boolean Networks

classification 🌊 nlin.AO cond-mat.stat-mechcs.CCnlin.CGq-bio.MNq-bio.QM
keywords rbnsresearchnetworksbooleandoneliferandomtutorial
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The goal of this tutorial is to promote interest in the study of random Boolean networks (RBNs). These can be very interesting models, since one does not have to assume any functionality or particular connectivity of the networks to study their generic properties. Like this, RBNs have been used for exploring the configurations where life could emerge. The fact that RBNs are a generalization of cellular automata makes their research a very important topic. The tutorial, intended for a broad audience, presents the state of the art in RBNs, spanning over several lines of research carried out by different groups. We focus on research done within artificial life, as we cannot exhaust the abundant research done over the decades related to RBNs.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A general representation of dynamical systems for reservoir computing

    cs.NE 2019-07 unverdicted novelty 3.0

    Cellular automata are recast as artificial neural networks by mapping update rules onto weights and activations to enable reservoir computing via deep learning libraries.