Control of random sensitivity Boolean networks via of Boolean functions

In this paper, we discuss how to transform the disordered phase into an ordered phase in random Boolean networks. To increase the effectiveness, a control scheme is proposed, which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes. Theoretical analysis is carried out to estimate the expected critical value of the fraction, and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes. Finally, the simulation is given for illustrating the effectiveness of the proposed method.     

Control of random Boolean networks via average sensitivity of Boolean functions

In this paper,we discuss how to transform the disordered phase into an ordered phase in random Boolean networks.To increase the effectiveness,a control scheme is proposed,which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes.Theoretical analysis is carried out to estimate the expected critical value of the fraction,and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes.Finally,the simulation is given for illustrating the effectiveness of the proposed method.     

On forcing functions in Kauffman's random Boolean networks

The phase transition between frozen and chaotic behavior in Kauffman's cellular automata on a nearest neighbor square lattice does not agree with the percolation threshold of the forcing functions.     

Evolution of a population of random Boolean networks

We investigate the evolution of populations of random Boolean networks under selection for robustness of the dynamics with respect to the perturbation of the state of a node. The fitness landscape contains a huge plateau of maximum fitness that spans the entire network space. When selection is so strong that it dominates over drift, the evolutionary process is accompanied by a slow increase in the mean connectivity and a slow decrease in the mean fitness. Populations evolved with higher mutation rates show a higher robustness under mutations. This means that even though all the evolved populations exist close to the plateau of maximum fitness, they end up in different regions of network space.     

Coevolution of information processing and topology in hierarchical adaptive random Boolean networks

Random Boolean Networks (RBNs) are frequently used for modeling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive random Boolean Network (HARBN) as a system consisting of distinct adaptive RBNs (ARBNs) – subnetworks – connected by a set of permanent interlinks. We investigate mean node information, mean edge information as well as mean node degree. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. The main natural feature of ARBNs, i.e. their adaptability, is preserved in HARBNs and they evolve towards critical configurations which is documented by power law distributions of network attractor lengths. The mean information processed by a single node or a single link increases with the number of interlinks added to the system. The mean length of network attractors and the mean steady-state connectivity possess minima for certain specific values of the quotient between the density of interlinks and the density of all links in networks. It means that the modular network displays extremal values of its observables when subnetworks are connected with a density a few times lower than a mean density of all links.     

The phase diagram of random Boolean networks with nested canalizing functions

We obtain the phase diagram of random Boolean networks with nested canalizing functions. Using the annealed approximation, we obtain the evolution of the number b _t of nodes with value one, and the network sensitivity λ, and compare with numerical simulations of quenched networks. We find that, contrary to what was reported by Kauffman et al. [Proc. Natl. Acad. Sci. 101, 17102 (2004)], these networks have a rich phase diagram, were both the “chaotic" and frozen phases are present, as well as an oscillatory regime of the value of b _t . We argue that the presence of only the frozen phase in the work of Kauffman et al. was due simply to the specific parametrization used, and is not an inherent feature of this class of functions. However, these networks are significantly more stable than the variant where all possible Boolean functions are allowed.     

Freezing transition in asymmetric random neural networks with deterministic dynamics

A network of binary elements (spins, neurons) which are completely connected by random couplings is investigated for a deterministic dynamics. For general values of the symmetry parameter extensive numerical simulations have been performed. With increasing symetry a sharp transition from a chaotic motion to a frozen state is found.     

Population dynamics on random networks: simulations and analytical models

We study the phase diagram of the standard pair approximation equations for two different models in population dynamics, the susceptible-infective-recovered-susceptible model of infection spread and a predator-prey interaction model, on a network of homogeneous degree k. These models have similar phase diagrams and represent two classes of systems for which noisy oscillations, still largely unexplained, are observed in nature. We show that for a certain range of the parameter k both models exhibit an oscillatory phase in a region of parameter space that corresponds to weak driving. This oscillatory phase, however, disappears when k is large. For k = 3, 4, we compare the phase diagram of the standard pair approximation equations of both models with the results of simulations on regular random graphs of the same degree. We show that for parameter values in the oscillatory phase, and even for large system sizes, the simulations either die out or exhibit damped oscillations, depending on the initial conditions. We discuss this failure of the standard pair approximation model to capture even the qualitative behavior of the simulations on large regular random graphs and the relevance of the oscillatory phase in the pair approximation diagrams to explain the cycling behavior found in real populations.     

Stability of Boolean and continuous dynamics

Regulatory dynamics in biology is often described by continuous rate equations for continuously varying chemical concentrations. Binary discretization of state space and time leads to Boolean dynamics. In the latter, the dynamics has been called unstable if flip perturbations lead to damage spreading. Here, we find that this stability classification strongly differs from the stability properties of the original continuous dynamics under small perturbations of the state vector. In particular, random networks of nodes with large sensitivity yield stable dynamics under small perturbations.     

Quantum dynamics of tight-binding networks coherently controlled by external fields

With some reviews on the investigations on the schemes for quantum state transfer based on spin systems, we discuss the quantum dynamics of magnetically-controlled networks for Bloch electrons. The networks are constructed by connecting several tight-binding chains with uniform nearest-neighbor hopping integrals. The external magnetic field and the connecting hopping integrals can be used to control the intrinsic properties of the networks. For several typical networks, rigorous results are shown for some specific values of external magnetic field and the connecting hopping integrals: a complicated network can be reduced into a virtual network, which is a direct sum of some independent chains with uniform nearest-neighbor hopping integrals. These reductions are due to the fermionic statistics and the Aharonov-Bohm effects. In application, we study the quantum dynamics of wave packet motion of Bloch electrons in such networks. For various geometrical configurations, these networks can function as some optical devices, such as beam splitters, switches and interferometers. When the Bloch electrons as Gaussian wave packets input these devices, various quantum coherence phenomena can be observed, e.g., the perfect quantum state transfer without reflection in a Y-shaped beam, the multi-mode entanglers of electron wave by star-shaped network, magnetically controlled switches, and Bloch electron interferometer with the lattice Aharonov-Bohm effects. With these quantum coherent features, the networks are expected to be used as quantum information processors for the fermion system based on the possible engineered solid state systems, such as the array of quantum dots that can be implemented experimentally.      

Evolution of canalizing Boolean networks

Boolean networks with canalizing functions are used to model gene regulatory networks. In order to learn how such networks may behave under evolutionary forces, we simulate the evolution of a single Boolean network by means of an adaptive walk, which allows us to explore the fitness landscape. Mutations change the connections and the functions of the nodes. Our fitness criterion is the robustness of the dynamical attractors against small perturbations. We find that with this fitness criterion the global maximum is always reached and that there is a huge neutral space of 100% fitness. Furthermore, in spite of having such a high degree of robustness, the evolved networks still share many features with “chaotic” networks.     

Dynamical motifs: building blocks of complex dynamics in sparsely connected random networks

Spatiotemporal network dynamics is an emergent property of many complex systems that remains poorly understood. We suggest a new approach to its study based on the analysis of dynamical motifs-small subnetworks with periodic and chaotic dynamics. We simulate randomly connected neural networks and, with increasing density of connections, observe the transition from quiescence to periodic and chaotic dynamics. This transition is explained by the appearance of dynamical motifs in the structure of these networks. We also observe domination of periodic dynamics in simulations of spatially distributed networks with local connectivity and explain it by the absence of chaotic and the presence of periodic motifs in their structure.     

随机外磁场对一维Blume-Capel模型动力学性质的影响

近几十年来,量子自旋系统的动力学性质引起了人们的广泛关注,随着研究的不断深入,随机自旋系统的性质受到了人们的重视. 利用递推关系式方法研究了高温极限下随机外磁场中自旋s=1的一维Blume-Capel模型的动力学性质, 通过计算自旋自关联函数和相应的谱密度,探讨了外场对系统动力学行为的影响.研究表明,在无晶格场的情况下, 当外场满足双模分布时,系统的动力学性质存在从中心峰值行为到集体模行为的交跨效应.当外场满足Gauss分布, 标准偏差较小时,系统也存在交跨效应;标准偏差足够大时,系统只表现为无序行为. 另外还研究了晶格场对系统动力学性质的影响,发现晶格场的存在减弱了系统的集体模行为.     

Effects of classical random external field on the dynamics of entanglement in a four-qubit system

We investigate the dynamics of entanglement through negativity and witness operators in a system of four non-interacting qubits driven by a classical phase noisy laser characterized by a classical random external field (CREF). The qubits are initially prepared in the GHZ-type and W-type states and interact with the CREF in two different qubit-field configurations, namely, common environment and independent environments in which the cases of equal and different field phase probabilities are distinguished. We find that entanglement exhibits different decaying behavior, depending on the input states of the qubits, the qubit-field coupling configuration, and field phase probabilities. On the one hand, we demonstrate that the coupling of the qubits in a common environment is an alternative and more efficient strategy to completely shield the system from the detrimental impacts of the decoherence process induced by a CREF, independent of the input state and the field phase probabilities considered. Also, we show that GHZ-type states have strong dynamics under CREF as compared to W-type states. On the other hand, we demonstrate that in the model investigated the system robustness's can be greatly improved by increasing the number of qubits constituting the system.     

Synchronization in an array of coupled Boolean networks

This Letter presents an analytical study of synchronization in an array of coupled deterministic Boolean networks. A necessary and sufficient criterion for synchronization is established based on algebraic representations of logical dynamics in terms of the semi-tensor product of matrices. Some basic properties of a synchronized array of Boolean networks are then derived for the existence of transient states and the upper bound of the number of fixed points. Particularly, an interesting consequence indicates that a “large” mismatch between two coupled Boolean networks in the array may result in loss of synchrony in the entire system. Examples, including the Boolean model of coupled oscillations in the cell cycle, are given to illustrate the present results.     

The value of less connected agents in Boolean networks

In multiagent systems, agents often face binary decisions where one seeks to take either the minority or the majority side. Examples are minority and congestion games in general, i.e., situations that require coordination among the agents in order to depict efficient decisions. In minority games such as the El Farol Bar Problem, previous works have shown that agents may reach appropriate levels of coordination, mostly by looking at the history of past decisions. Not many works consider any kind of structure of the social network, i.e., how agents are connected. Moreover, when structure is indeed considered, it assumes some kind of random network with a given, fixed connectivity degree. The present paper departs from the conventional approach in some ways. First, it considers more realistic network topologies, based on preferential attachments. This is especially useful in social networks. Second, the formalism of random Boolean networks is used to help agents to make decisions given their attachments (for example acquaintances). This is coupled with a reinforcement learning mechanism that allows agents to select strategies that are locally and globally efficient. Third, we use agent-based modeling and simulation, a microscopic approach, which allows us to draw conclusions about individuals and/or classes of individuals. Finally, for the sake of illustration we use two different scenarios, namely the El Farol Bar Problem and a binary route choice scenario. With this approach we target systems that adapt dynamically to changes in the environment, including other adaptive decision-makers. Our results using preferential attachments and random Boolean networks are threefold. First we show that an efficient equilibrium can be achieved, provided agents do experimentation. Second, microscopic analysis show that influential agents tend to consider few inputs in their Boolean functions. Third, we have also conducted measurements related to network clustering and centrality that help to see how agents are organized.     

Clique percolation in random networks

The notion of k-clique percolation in random graphs is introduced, where k is the size of the complete subgraphs whose large scale organizations are analytically and numerically investigated. For the Erdos-Rényi graph of N vertices we obtain that the percolation transition of k-cliques takes place when the probability of two vertices being connected by an edge reaches the threshold p(c) (k) = [(k - 1)N](-1/(k - 1)). At the transition point the scaling of the giant component with N is highly nontrivial and depends on k. We discuss why clique percolation is a novel and efficient approach to the identification of overlapping communities in large real networks.