scholarly journals Exploring Residue Component Contributions to Dynamical Network Models of Allostery

2012 ◽  
Vol 8 (8) ◽  
pp. 2949-2961 ◽  
Author(s):  
Adam T. VanWart ◽  
John Eargle ◽  
Zaida Luthey-Schulten ◽  
Rommie E. Amaro
Keyword(s):  
Network Models ◽  
Dynamical Network

scholarly journals Formulation of Pruning Maps with Rhythmic Neural Firing

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1247
Author(s):  
Feng-Sheng Tsai ◽  
Yi-Li Shih ◽  
Chin-Tzong Pang ◽  
Sheng-Yi Hsu
Keyword(s):  
Network Dynamics ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Oscillatory Activity ◽  
Control Network ◽  
Neural Function ◽  
Network System ◽  
Neural Firing ◽  
Dynamical Network ◽  
Neuronal Connections

Rhythmic neural firing is thought to underlie the operation of neural function. This triggers the construction of dynamical network models to investigate how the rhythms interact with each other. Recently, an approach concerning neural path pruning has been proposed in a dynamical network system, in which critical neuronal connections are identified and adjusted according to the pruning maps, enabling neurons to produce rhythmic, oscillatory activity in simulation. Here, we construct a sort of homomorphic functions based on different rhythms of neural firing in network dynamics. Armed with the homomorphic functions, the pruning maps can be simply expressed in terms of interactive rhythms of neural firing and allow a concrete analysis of coupling operators to control network dynamics. Such formulation of pruning maps is applied to probe the consolidation of rhythmic patterns between layers of neurons in feedforward neural networks.

scholarly journals A New Model for Complex Dynamical Networks Considering Random Data Loss

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 797
Author(s):  
Xu Wu ◽  
Guo-Ping Jiang ◽  
Xinwei Wang
Keyword(s):  
Network Model ◽  
Network Models ◽  
Lyapunov Stability Theory ◽  
Complex Dynamical Networks ◽  
Output Coupling ◽  
Data Loss ◽  
Complex Dynamical Network ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Proposed Model

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

scholarly journals Virtual intracranial electroencephalography for epilepsy surgery using ictal magnetoencephalography

2021 ◽  
Author(s):  
Miao Cao ◽  
Daniel Galvis ◽  
Simon Vogrin ◽  
William Woods ◽  
Sara Vogrin ◽  
...  
Keyword(s):  
Epilepsy Surgery ◽  
Focal Epilepsy ◽  
Network Models ◽  
Brain Regions ◽  
Proof Of Concept ◽  
Brain Areas ◽  
Dynamical Network ◽  
Non Invasive ◽  
Novel Approach ◽  
Intracranial Electroencephalography

Abstract Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A major limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose a novel approach, virtual intracranial electroencephalography (ViEEG), that combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. Our findings demonstrate the advantages of non-invasive ViEEG over the current presurgical ‘gold-standard’ – intracranial electroencephalography (iEEG). Our approach promises to optimise the surgical strategy for patients with complex refractory focal epilepsy.

scholarly journals Structure Identification of Fractional-Order Dynamical Network with Different Orders

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2096
Author(s):  
Mingcong Zhou ◽  
Zhaoyan Wu
Keyword(s):  
Fractional Order ◽  
Dynamical Behavior ◽  
Great Influence ◽  
Network Models ◽  
Switching Topology ◽  
Structure Identification ◽  
Topology Structure ◽  
System Parameters ◽  
Dynamical Network ◽  
Fractional Orders

Topology structure and system parameters have a great influence on the dynamical behavior of dynamical networks. However, they are sometimes unknown or uncertain in advance. How to effectively identify them has been investigated in various network models, from integer-order networks to fractional-order networks with the same order. In the real world, many systems consist of subsystems with different fractional orders. Therefore, the structure identification of a dynamical network with different fractional orders is investigated in this paper. Through designing proper adaptive controllers and parameter updating laws, two network estimators are well constructed. One is for identifying only the unknown topology structure. The other is for identifying both the unknown topology structure and system parameters. Based on the Lyapunov function method and the stability theory of fractional-order dynamical systems, the theoretical results are analytically proved. The effectiveness is verified by three numerical examples as well. In addition, the designed estimators have a good performance in monitoring switching topology. From the practical viewpoint, the designed estimators can be used to monitor the change of current and voltage in the fractional-order circuit systems.

scholarly journals Boolean network models of cellular regulation: prospects and limitations

2008 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Stefan Bornholdt
Keyword(s):  
Regulatory Networks ◽  
Network Models ◽  
Boolean Networks ◽  
Coarse Grained ◽  
Boolean Models ◽  
Theoretical Terms ◽  
Dynamical Network ◽  
Regulatory Circuits ◽  
Abstract Level ◽  
Biological Circuits

Computer models are valuable tools towards an understanding of the cell's biochemical regulatory machinery. Possible levels of description of such models range from modelling the underlying biochemical details to top-down approaches, using tools from the theory of complex networks. The latter, coarse-grained approach is taken where regulatory circuits are classified in graph-theoretical terms, with the elements of the regulatory networks being reduced to simply nodes and links, in order to obtain architectural information about the network. Further, considering dynamics on networks at such an abstract level seems rather unlikely to match dynamical regulatory activity of biological cells. Therefore, it came as a surprise when recently examples of discrete dynamical network models based on very simplistic dynamical elements emerged which in fact do match sequences of regulatory patterns of their biological counterparts. Here I will review such discrete dynamical network models, or Boolean networks, of biological regulatory networks. Further, we will take a look at such models extended with stochastic noise, which allow studying the role of network topology in providing robustness against noise. In the end, we will discuss the interesting question of why at all such simple models can describe aspects of biology despite their simplicity. Finally, prospects of Boolean models in exploratory dynamical models for biological circuits and their mutants will be discussed.

Dynamical network models for cattle trade: towards economy-based epidemic risk assessment

2016 ◽  
Vol 5 (4) ◽  
pp. 604-624 ◽  
Author(s):  
Patrick Hoscheit ◽  
Sébastien Geeraert ◽  
Gaël Beaunée ◽  
Hervé Monod ◽  
Christopher A. Gilligan ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Network Models ◽  
Dynamical Network ◽  
Epidemic Risk

What's in a model? Network models as tools instead of representations of what psychiatric disorders really are

2019 ◽  
Vol 42 ◽  
Author(s):  
Hanna M. van Loo ◽  
Jan-Willem Romeijn
Keyword(s):  
Psychiatric Disorders ◽  
Network Models ◽  
Model Network

AbstractNetwork models block reductionism about psychiatric disorders only if models are interpreted in a realist manner – that is, taken to represent “what psychiatric disorders really are.” A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

Special, radical, failure of reduction in psychiatry

2019 ◽  
Vol 42 ◽  
Author(s):  
Don Ross
Keyword(s):  
Mental Disorder ◽  
Brain Structure ◽  
Causal Structure ◽  
Network Models ◽  
Gambling Addiction ◽  
Brain Disorder ◽  
Mental Processes ◽  
Special Radical ◽  
Intentional Relationships

AbstractUse of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.

Application of Lattice Imaging Techniques to Amorphous Films

1975 ◽  
Vol 33 ◽  
pp. 8-9
Author(s):  
S. R. Herd ◽  
P. Chaudhari
Keyword(s):  
Nearest Neighbor ◽  
Random Network ◽  
Imaging Techniques ◽  
Network Models ◽  
Accurate Method ◽  
Direct Transmission ◽  
Lattice Imaging ◽  
Transmission Electron ◽  
Lattice Planes ◽  
Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

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