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Boolean Dynamics with Random Couplings

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TLDR
In this article, the authors review a class of generic dissipative dynamical systems called N-K models, where the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable.
Abstract
This paper reviews a class of generic dissipative dynamical systems called N-K models. In these models, the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable. Each of the N Boolean elements at a given time is given a value which depends upon K elements in the previous time step. We review the work of many authors on the behavior of the models, looking particularly at the structure and lengths of their cycles, the sizes of their basins of attraction, and the flow of information through the systems. In the limit of infinite N, there is a phase transition between a chaotic and an ordered phase, with a critical phase in between. We argue that the behavior of this system depends significantly on the topology of the network connections. If the elements are placed upon a lattice with dimension d,the system shows correlations related to the standard percolation or directed percolation phase transition on such a lattice. On the other hand, a very different behavior is seen in the Kauffman net in which all spins are equally likely to be coupled to a given spin. In this situation, coupling loops are mostly suppressed, and the behavior of the system is much more like that of a mean field theory. We also describe possible applications of the models to, for example, genetic networks, cell differentiation, evolution, democracy in social systems and neural networks.

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Boolean dynamics of networks with scale-free topology

TL;DR: In this paper, the scale-free topology of the input connections and that of the output connections of a Boolean network has been studied and the existence of a phase transition from ordered to chaotic dynamics, governed by the value of the scale free exponent of the network, is shown analytically by analyzing the overlap between two distinct trajectories.
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Boolean network models of cellular regulation: prospects and limitations

TL;DR: The interesting question of why at all such simple models can describe aspects of biology despite their simplicity is discussed, and prospects of Boolean models in exploratory dynamical models for biological circuits and their mutants will be discussed.
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Robustness and evolvability in genetic regulatory networks.

TL;DR: This work shows that an intrinsic property of this kind of networks is that, after the divergence of the parent and duplicate genes, with a high probability the previous phenotypes, encoded in the attractor landscape of the network, are preserved and new ones might appear.
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Critical Dynamics in Genetic Regulatory Networks: Examples from Four Kingdoms

TL;DR: The results show that the gene transcription networks reported for S. cerevisiae, E. coli, and B. subtilis indeed operate close to criticality, and suggests that criticality at the genetic level might constitute a fundamental evolutionary mechanism that generates the great diversity of dynamically robust living forms that the authors observe around us.
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Superpolynomial growth in the number of attractors in Kauffman networks

TL;DR: In this paper, the average number of attractors grows faster than any power law with system size, and it is shown that the number of attracted nodes in a random Boolean network grows with the size of the network.
References
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Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
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Neural networks and physical systems with emergent collective computational abilities

TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
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A logical calculus of the ideas immanent in nervous activity

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