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Dynamical behavior and influence of stochastic noise on certain generalized Boolean networks

Beck, Gary L.; Matache, Mihaela T. - In: Physica A: Statistical Mechanics and its Applications 387 (2008) 19, pp. 4947-4958

This study considers a simple Boolean network with N nodes, each node’s state at time t being determined by a certain number of parent nodes. The network is analyzed when the connectivity k is fixed or variable. Making use of a Boolean rule that is a generalization of Rule 22 of elementary...

Persistent link: https://www.econbiz.de/10010873556

On Lempel–Ziv complexity for multidimensional data analysis

Zozor, S.; Ravier, P.; Buttelli, O. - In: Physica A: Statistical Mechanics and its Applications 345 (2005) 1, pp. 285-302

In this paper, a natural extension of the Lempel–Ziv complexity for several finite-time sequences, defined on finite … size alphabets is proposed. Some results on the defined joint Lempel–Ziv complexity are given, as well as properties in … connection with the Lempel–Ziv complexity of the individual sequences. Also, some links with Shannon entropies are exhibited and …

Persistent link: https://www.econbiz.de/10010590655

Evolution of network structure by temporal learning

Jost, Jürgen; Kolwankar, Kiran M. - In: Physica A: Statistical Mechanics and its Applications 388 (2009) 9, pp. 1959-1966

We study the effect of learning dynamics on network topology. A network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity. This...

Persistent link: https://www.econbiz.de/10011058491

Computational modeling of the dependence of kindling rate on network properties

Biswal, B.; Niranjan, B.R.; Ullal, G.; Dasgupta, C. - In: Physica A: Statistical Mechanics and its Applications 364 (2006) C, pp. 565-580

size and complexity of the network are independent of some of the assumptions made in our modeling. …

Persistent link: https://www.econbiz.de/10010589974

Feedback between node and network dynamics can produce real-world network properties

Brot, Hilla; Muchnik, Lev; Goldenberg, Jacob; Louzoun, Yoram - In: Physica A: Statistical Mechanics and its Applications 391 (2012) 24, pp. 6645-6654

Real-world networks are characterized by common features, including among others a scale-free degree distribution, a high clustering coefficient and a short typical distance between nodes. These properties are usually explained by the dynamics of edge and node addition and deletion.

Persistent link: https://www.econbiz.de/10010590440

Biologically inspired learning in a layered neural net

Bedaux, J.; van Leeuwen, W.A. - In: Physica A: Statistical Mechanics and its Applications 335 (2004) 1, pp. 279-299

A feed-forward neural net with adaptable synaptic weights and fixed, zero or non-zero threshold potentials is studied, in the presence of a global feedback signal that can only have two values, depending on whether the output of the network in reaction to its input is right or wrong.

Persistent link: https://www.econbiz.de/10010591832

Multiscale entropy-based methods for heart rate variability complexity analysis

Silva, Luiz Eduardo Virgilio; Cabella, Brenno Caetano Troca - In: Physica A: Statistical Mechanics and its Applications 422 (2015) C, pp. 143-152

Physiologic complexity is an important concept to characterize time series from biological systems, which associated to … complexity metrics as function of time scales. Complexity metrics are derived from nonadditive entropy supported by generation of … surrogate data, i.e. SDiffqmax, qmax and qzero. In order to access accuracy of proposed complexity metrics, receiver operating …

Persistent link: https://www.econbiz.de/10011194017

EMD based refined composite multiscale entropy analysis of complex signals

Wang, Jing; Shang, Pengjian; Xia, Jianan; Shi, Wenbin - In: Physica A: Statistical Mechanics and its Applications 421 (2015) C, pp. 583-593

Multiscale entropy (MSE) is an effective method to measure the complexity of signals from complex systems, which has … complexity measurement. Thus we introduce an empirical mode decomposition based RCMSE, called EMD–RCMSE to first eliminate such … effects of trends and then measure the complexity of signals. It is validated from simulated signals and has good estimation …

Persistent link: https://www.econbiz.de/10011194044

Complexity measures and self-similarity on spreading depression waves

Piqueira, José Roberto C.; Lima, Vera Maura Fernandes de; … - In: Physica A: Statistical Mechanics and its Applications 401 (2014) C, pp. 271-277

the circular wave formations. Shiner–Davison–Landsberg (SDL) complexity measure framework has been used in several … were collected in two different spatial scales. The SDL complexity was calculated for the data and two main results …

Persistent link: https://www.econbiz.de/10010871856

The effect of microscopic correlations on the information geometric complexity of Gaussian statistical models

Ali, S.A.; Cafaro, C.; Kim, D.-H.; Mancini, S. - In: Physica A: Statistical Mechanics and its Applications 389 (2010) 16, pp. 3117-3127

We present an analytical computation of the asymptotic temporal behavior of the information geometric complexity (IGC … connection between (micro)correlations and (macro)complexity in Gaussian statistical dynamical systems. …

Persistent link: https://www.econbiz.de/10010871921