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Introduction to Random Boolean Networks [article]

Carlos Gershenson
<span title="2004-08-12">2004</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ...  I thank Marcelle Kaufman for introducing me to the work of René Thomas. This work was supported in part by the Consejo Nacional de Ciencia y Tecnología (CONACYT) of México.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/nlin/0408006v3">arXiv:nlin/0408006v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/er5crt3cmfawnlar7v6n5qteu4">fatcat:er5crt3cmfawnlar7v6n5qteu4</a> </span>
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The Immunity of Random Boolean Networks Based on Importance Index of Nodes

<span title="">2017</span> <i title="Clausius Scientific Press, Inc."> Journal of Materials, Processing and Design </i> &nbsp;
Random Boolean network is a model of gene regulation network developed by Kauffman in 1969.In this paper, we focus on the evolution of the configuration space of random Boolean network, by the perspective  ...  As contrast, the immunization based on importance effect of BA networks is more pronounced than random immunization.  ...  Introduction Boolean network was first proposed by Kauffman as a mathematical model of gene regulatory networks in 1969, also as known as N-K model or Kauffman network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23977/jmpd.2017.11004">doi:10.23977/jmpd.2017.11004</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oql43bw4zbcuvlf6tbfued4knq">fatcat:oql43bw4zbcuvlf6tbfued4knq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180721110903/http://www.clausiuspress.com/assets/default/article/2017/03/24/article_1490375631.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7f/76/7f7678d9b97445a7770994e424d0d9182ebb750c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23977/jmpd.2017.11004"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

The Gene Modular Detection of Random Boolean Networks by Dynamic Characteristics Analysis

<span title="">2017</span> <i title="Clausius Scientific Press, Inc."> Journal of Materials, Processing and Design </i> &nbsp;
In this paper, based on the classical random Boolean networks, we investigate the different dynamic characteristics between random and BA Boolean networks.  ...  In the last decades, random Boolean networks have been widely used in the sociology, biology and other fields.  ...  Conclusions In this paper, we use the ER random Boolean network and BA random Boolean network in the study of random Boolean networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23977/jmpd.2017.11003">doi:10.23977/jmpd.2017.11003</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lkmbvdrbh5cevdb3e2larix46a">fatcat:lkmbvdrbh5cevdb3e2larix46a</a> </span>
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Statistical Properties of Randomly Constructed Boolean Networks that Resemble the Transcription Factor Network of Escherichia coli

Chikoo Oosawa, Michael A. Savageau
<span title="">2002</span> <i title="Japanese Society for Bioinformatics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jf2bmhtlzjh4zjrkh3rivxd2sq" style="color: black;">Genome Informatics Series</a> </i> &nbsp;
Figure 2 : 2 Median number of attractors in random Boolean networks with four diff connectivities and various network sizes.  ...  We have constructed large numbers of random Boolean networks with output connections that have a variety of distributions from uniform to highly skewed (Figure 1 ).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11234/gi1990.13.375">doi:10.11234/gi1990.13.375</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/khfy5ckqc5hcjeb4b2rluvbs5m">fatcat:khfy5ckqc5hcjeb4b2rluvbs5m</a> </span>
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Analysis of random Boolean networks using the average sensitivity [article]

Steffen Schober, Martin Bossert
<span title="2007-04-02">2007</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We show how to apply the proof to networks with arbitrary connectivity K and to random networks with biased Boolean functions.  ...  In this work we consider random Boolean networks that provide a general model for genetic regulatory networks.  ...  Acknowledgement We would like to thank our colleges Georg Schmidt and Stephan Stiglmayr for proofreading and Uwe Schoening for useful hints.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/0704.0197v1">arXiv:0704.0197v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p4xi4dqxtzdnhkwe5x7oosuose">fatcat:p4xi4dqxtzdnhkwe5x7oosuose</a> </span>
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Boolean Networks with Multi-Expressions and Parameters [article]

Yi Ming Zou
<span title="2014-04-22">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To model biological systems using networks, it is desirable to allow more than two levels of expression for the nodes and to allow the introduction of parameters.  ...  Certain classes of random asynchronous Boolean networks and deterministic moduli asynchronous Boolean networks are investigated in detail using the setting introduced in this paper.  ...  The two asynchronous Boolean models mentioned in the introduction section can be treated as dynamical systems of PMEBNs. Example 4.2. Random Asynchronous Boolean Models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1404.5516v1">arXiv:1404.5516v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sntlb2fg7jgnxcdv676rvuvmvq">fatcat:sntlb2fg7jgnxcdv676rvuvmvq</a> </span>
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Role of Function Complexity and Network Size in the Generalization Ability of Feedforward Networks [chapter]

Leonardo Franco, José M. Jerez, José M. Bravo
<span title="">2005</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
For a whole set of Boolean symmetric functions it is found that large neural networks have a better generalization ability on a large complexity range of the functions in comparison to smaller ones and  ...  layer network architectures.  ...  quasi-random generated Boolean functions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11494669_1">doi:10.1007/11494669_1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n3d6i72xjzhztgi4jpsgjyasny">fatcat:n3d6i72xjzhztgi4jpsgjyasny</a> </span>
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Characteristics of Dynamical Phase Transitions for Noise Intensities

Muyoung Heo, Jong-Kil Park, Kyungsik Kim
<span title="">2014</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cx3f4s3qmfe6bg4qvuy2cxezyu" style="color: black;">Procedia Computer Science</a> </i> &nbsp;
We simulate and analyze dynamical phase transitions in a Boolean neural network with initial random connections.  ...  The nature of the phase transition are found numerically and analytically in two connections of probability density function and one random network.  ...  Introduction Boolean neural networks have been described as generic models for the dynamics of complex systems of interacting entities, such as social and economic networks, neural networks, and gene or  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.procs.2014.05.235">doi:10.1016/j.procs.2014.05.235</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uqj5x3dc2jdt3kfehsgr27m4we">fatcat:uqj5x3dc2jdt3kfehsgr27m4we</a> </span>
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Hybrid Random Network Coding [chapter]

Chih-Wei Yi
<span title="">2011</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
On the other hand, random Boolean network coding, based on the Boolean algebra in which XOR is the only operation needed for encoding and encoding, is a computation-friendly random network coding scheme  ...  To avoid possible dependence among coded packets, most research works of random linear network coding suggest encoding packets over a Galois field with enough many elements, e.g., GF 2 8 or GF 2 16 .  ...  Introduction The concept of Network Coding (NC) was introduced by Ahlswede et al. [1] to investigate achievable multicast throughput. NC allows intermediate nodes to encode the payload of packets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-23490-3_18">doi:10.1007/978-3-642-23490-3_18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zhgvcax5jrfdnbvkm3ym7wet3i">fatcat:zhgvcax5jrfdnbvkm3ym7wet3i</a> </span>
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Bayesian adaptation of hidden layers in Boolean feedforward neural networks

W. Utschick, J.A. Nossek
<span title="">1996</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jsl2pgelqja2piczru3a6nqkg4" style="color: black;">Proceedings of 13th International Conference on Pattern Recognition</a> </i> &nbsp;
The hidden layer of a multilayer perceptrokneural network is identified of representing the mapping of random vectors.  ...  Training is exclusively carried out on the first layer of the neural network, whereas the definition of the boolean function generally remains a matter of experience or due to considerations of symmetry  ...  According to this stochastical point of view the mapping of a perceptron layer of a neural network may be interpreted as a random vector consisting of random variables, each of them corresponding to the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr.1996.547421">doi:10.1109/icpr.1996.547421</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icpr/UtschickN96.html">dblp:conf/icpr/UtschickN96</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6lju2bz34vh7rargnmztvqpib4">fatcat:6lju2bz34vh7rargnmztvqpib4</a> </span>
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Mapping dynamical states to structural classes for Boolean networks using a classification algorithm

Septimia Sarbu, Ilya Shmulevich, Olli Yli-Harja, Matti Nykter, Juha Kesseli
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wiu3vyiu4fcgnd4znybulgkh6m" style="color: black;">2015 23rd European Signal Processing Conference (EUSIPCO)</a> </i> &nbsp;
Complex networks represent a simplified description of the interactions present in such systems. Boolean networks were introduced as models of gene regulatory networks.  ...  Structuredynamics relationships in Boolean networks have been investigated by inferring a particular structure of a network from the time sequence of its dynamical states.  ...  by one random connection (the first module is a fixed K = 3 Boolean network, the second module is a fixed K = 4 Boolean network and the third module is a fixed K = 5 Boolean network).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/eusipco.2015.7362365">doi:10.1109/eusipco.2015.7362365</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/eusipco/SarbuSYNK15.html">dblp:conf/eusipco/SarbuSYNK15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s5nxjrgnpzczhezibn26cpraq4">fatcat:s5nxjrgnpzczhezibn26cpraq4</a> </span>
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Exploring the Dynamic Organization of Random and Evolved Boolean Networks

Gianluca d'Addese, Salvatore Magrì, Roberto Serra, Marco Villani
<span title="2020-10-28">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/63zsvf7vxzfznojpqgfvpyk2lu" style="color: black;">Algorithms</a> </i> &nbsp;
The properties of most systems composed of many interacting elements are neither determined by the topology of the interaction network alone, nor by the dynamical laws in isolation.  ...  Finally, and similarly to what happens in other applications of evolutionary algorithms, the types of dynamic changes strongly depend upon the used fitness function.  ...  Random Boolean Networks Random Boolean Networks are a well-known model of gene regulatory dynamics [17] [18] [19] [20] 24, 30, [39] [40] [41] able to perform computation [42] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/a13110272">doi:10.3390/a13110272</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2wqotiu6kjhofpibeg2y6hlgz4">fatcat:2wqotiu6kjhofpibeg2y6hlgz4</a> </span>
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Activities and Sensitivities in Boolean Network Models

Ilya Shmulevich, Stuart A. Kauffman
<span title="2004-07-22">2004</span> <i title="American Physical Society (APS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pkmpevvs3bgkhal5dvrmsu5mvq" style="color: black;">Physical Review Letters</a> </i> &nbsp;
In a random Boolean network, we show that the expected average sensitivity determines the well-known critical transition curve.  ...  We study how the notions of importance of variables in Boolean functions as well as the sensitivities of the functions to changes in these variables impact the dynamical behavior of Boolean networks.  ...  Introduction.-Boolean networks are complex systems that were initially proposed as models of genetic regulatory networks [1, 2] but have since been used to model a range of complex phenomena [3] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1103/physrevlett.93.048701">doi:10.1103/physrevlett.93.048701</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/15323803">pmid:15323803</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC1490311/">pmcid:PMC1490311</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dddjd6p56bd6phmya7x5udutga">fatcat:dddjd6p56bd6phmya7x5udutga</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20060218172244/http://card.joncaves.com:80/resources/papers/PRLActivities.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5c/b4/5cb47532fc74edcdaf674e19253fe8dc5a2f83f4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1103/physrevlett.93.048701"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> aps.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1490311" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Julian Schwab, Lea Siegle, Silke Kühlwein, Michael Kühl, Hans Kestler
<span title="2017-12-18">2017</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/j5h4zct24vcofi5scx6ttoapnq" style="color: black;">Biology</a> </i> &nbsp;
Boolean networks can be inferred from time-series of gene expression data.  ...  Boolean networks as models of biological pathways allow for simulation of signaling behavior.  ...  Introduction Systems Biology, the study of complex biological systems, is an emerging field in science.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/biology6040046">doi:10.3390/biology6040046</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29258225">pmid:29258225</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5745451/">pmcid:PMC5745451</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/novik66yibgi3bwweldlvp4dzy">fatcat:novik66yibgi3bwweldlvp4dzy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190228121953/http://pdfs.semanticscholar.org/91d9/cce1d65cd8eb2e0b86c74418ea9de321e1d2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/91/d9/91d9cce1d65cd8eb2e0b86c74418ea9de321e1d2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/biology6040046"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5745451" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Spontaneous Emergence of Computation in Network Cascades [article]

Galen Wilkerson, Sotiris Moschoyiannis, Henrik Jeldtoft Jensen
<span title="2022-04-27">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning)  ...  Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the  ...  The tools of Boolean logic then allow us to begin to develop a formalism linking LTM and other cascades to information processing in the theory of computing [39] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.11956v2">arXiv:2204.11956v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mlyavgpwrzbnzpguppquhcmv4e">fatcat:mlyavgpwrzbnzpguppquhcmv4e</a> </span>
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