scholarly journals The synchronization of neuronal oscillators determined by the directed network structure of the suprachiasmatic nucleus under different photoperiods

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Changgui Gu ◽  
Ming Tang ◽  
Huijie Yang
Keyword(s):  
Suprachiasmatic Nucleus ◽  
Network Structure ◽  
Directed Network ◽  
Neuronal Oscillators

Optimizing complex networks controllability by local structure information

2016 ◽  
Vol 27 (10) ◽  
pp. 1650115
Author(s):  
Houyi Yan ◽  
Lvlin Hou ◽  
Yunxiang Ling ◽  
Guohua Wu
Keyword(s):  
Numerical Simulation ◽  
Complex Networks ◽  
Network Structure ◽  
Local Structure ◽  
Global Structure ◽  
Directed Network ◽  
Structure Information ◽  
Different Types ◽  
Extensive Numerical Simulation ◽  
Network Controllability

Research in network controllability has mostly been focused on the effects of the network structure on its controllability, and some methods have been proposed to optimize the network controllability. However, they are all based on global structure information of networks. We propose two different types of methods to optimize controllability of a directed network by local structure information. Extensive numerical simulation on many modeled networks demonstrates that this method is effective. Since the whole topologies of many real networks are not visible and we only get some local structure information, this strategy is potentially more practical.

NETWORK AS A CHAOTIC DYNAMICAL SYSTEM

2007 ◽  
Vol 17 (10) ◽  
pp. 3529-3533 ◽  
Author(s):  
SYUJI MIYAZAKI ◽  
YASUSHI NAGASHIMA
Keyword(s):  
Dynamical System ◽  
Network Structure ◽  
Transition Matrix ◽  
Chaotic Dynamics ◽  
Large Deviation ◽  
Piecewise Linear ◽  
Directed Network ◽  
Chaotic Dynamical System ◽  
One Dimensional ◽  
Finite Time Lyapunov Exponents

A directed network such as the WWW can be represented by a transition matrix. Comparing this matrix to a Frobenius–Perron matrix of a chaotic piecewise-linear one-dimensional map whose domain can be divided into Markov subintervals, we are able to relate the network structure itself to chaotic dynamics. Just like various large deviation properties of local expansion rates (finite-time Lyapunov exponents) related to chaotic dynamics, we can also discuss those properties of network structure.

scholarly journals Windowed Granger causal inference strategy improves discovery of gene regulatory networks

2018 ◽  
Vol 115 (9) ◽  
pp. 2252-2257 ◽  
Author(s):  
Justin D. Finkle ◽  
Jia J. Wu ◽  
Neda Bagheri
Keyword(s):  
Time Series ◽  
Network Structure ◽  
Time Delays ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Directed Network ◽  
Rapid Characterization

Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene–gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene–gene influences.

scholarly journals Disassortative Network Structure Improves the Synchronization between Neurons in the Suprachiasmatic Nucleus

2019 ◽  
Vol 34 (5) ◽  
pp. 515-524
Author(s):  
Changgui Gu ◽  
Xiangwei Gu ◽  
Ping Wang ◽  
Henggang Ren ◽  
Tongfeng Weng ◽  
...  
Keyword(s):  
Suprachiasmatic Nucleus ◽  
Network Structure ◽  
Proper Function ◽  
Collective Behaviors ◽  
Single Slice ◽  
Endogenous Clock ◽  
Heterogeneous Nature ◽  
Behavioral Activities ◽  
The Brain ◽  
Heterogeneous Coupling

In mammals, an endogenous clock located in the suprachiasmatic nucleus (SCN) of the brain regulates the circadian rhythms of physiological and behavioral activities. The SCN is composed of about 20,000 neurons that are autonomous oscillators with nonidentical intrinsic periods ranging from 22 h to 28 h. These neurons are coupled through neurotransmitters and synchronized to form a network, which produces a robust circadian rhythm of a uniform period. The neurons, which are the nodes in the network, are known to be heterogeneous in their characteristics, which is reflected in different phenotypes and different functionality. This heterogeneous nature of the nodes of the network leads to the question as to whether the structure of the SCN network is assortative or disassortative. Thus far, the disassortativity of the SCN network has not been assessed and neither have its effects on the collective behaviors of the SCN neurons. In the present study, we build a directed SCN network composed of hundreds of neurons for a single slice using the method of transfer entropy, based on the experimental data. Then, we measured the synchronization degree as well as the disassortativity coefficient of the network structure (calculated by either the out-degrees or the in-degrees of the nodes) and found that the network of the SCN is a disassortative network. Furthermore, a positive relationship is observed between the synchronization degree and disassortativity of the network, which is confirmed by simulations of our modeling. Our finding suggests that the disassortativity of the network structure plays a role in the synchronization between SCN neurons; that is, the synchronization degree increases with the increase of the disassortativity, which implies that a more heterogeneous coupling in the network of the SCN is important for proper function of the SCN.

scholarly journals Modeling the Seasonal Adaptation of Circadian Clocks by Changes in the Network Structure of the Suprachiasmatic Nucleus

2012 ◽  
Vol 8 (9) ◽  
pp. e1002697 ◽  
Author(s):  
Christian Bodenstein ◽  
Marko Gosak ◽  
Stefan Schuster ◽  
Marko Marhl ◽  
Matjaž Perc
Keyword(s):  
Suprachiasmatic Nucleus ◽  
Network Structure ◽  
Circadian Clocks ◽  
Seasonal Adaptation

scholarly journals The Dorsomedial Suprachiasmatic Nucleus Times Circadian Expression of Kiss1 and the Luteinizing Hormone Surge

Endocrinology ◽  
2012 ◽  
Vol 153 (6) ◽  
pp. 2839-2850 ◽  
Author(s):  
Benjamin L. Smarr ◽  
Emma Morris ◽  
Horacio O. de la Iglesia
Keyword(s):  
Circadian Clock ◽  
Suprachiasmatic Nucleus ◽  
Phase Coherence ◽  
Reproductive Disorders ◽  
Lh Surge ◽  
Circadian Expression ◽  
Neural Input ◽  
Periventricular Nucleus ◽  
Gnrh Neurons ◽  
Neuronal Oscillators

Ovulation in mammals is gated by a master circadian clock in the suprachiasmatic nucleus (SCN). GnRH neurons represent the converging pathway through which the brain triggers ovulation, but precisely how the SCN times GnRH neurons is unknown. We tested the hypothesis that neurons expressing kisspeptin, a neuropeptide coded by the Kiss1 gene and necessary for the activation of GnRH cells during ovulation, represent a relay station for circadian information that times ovulation. We first show that the circadian increase of Kiss1 expression, as well as the activation of GnRH cells, relies on intact ipsilateral neural input from the SCN. Second, by desynchronizing the dorsomedial (dm) and ventrolateral (vl) subregions of the SCN, we show that a clock residing in the dmSCN acts independently of the light-dark cycle, and the vlSCN, to time Kiss1 expression in the anteroventral periventricular nucleus of the hypothalamus and that this rhythm is always in phase with the LH surge. In addition, we show that although the timing of the LH surge is governed by the dmSCN, its amplitude likely depends on the phase coherence between the vlSCN and dmSCN. Our results suggest that whereas dmSCN neuronal oscillators are sufficient to time the LH surge through input to kisspeptin cells in the anteroventral periventricular nucleus of the hypothalamus, the phase coherence among dmSCN, vlSCN, and extra-SCN oscillators is critical for shaping it. They also suggest that female reproductive disorders associated with nocturnal shift work could emerge from the desynchronization between subregional oscillators within the master circadian clock.

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