Robustness of the Scale-free Spiking Neural Network with Small-world Property

2020 ◽  
Author(s):  
Dongzhao Liu ◽  
Lei Guo ◽  
Youxi Wu ◽  
Guizhi Xu
Keyword(s):  
Neural Network ◽  
Small World ◽  
Spiking Neural Network ◽  
Scale Free ◽  
Small World Property

scholarly journals Directed random geometric graphs

2019 ◽  
Vol 7 (5) ◽  
pp. 792-816
Author(s):  
Jesse Michel ◽  
Sushruth Reddy ◽  
Rikhav Shah ◽  
Sandeep Silwal ◽  
Ramis Movassagh
Keyword(s):  
Real World ◽  
Word Association ◽  
Graph Model ◽  
Small World ◽  
Geometric Graph ◽  
Clustering Coefficient ◽  
Random Geometric Graph ◽  
Random Geometric Graphs ◽  
Scale Free ◽  
Small World Property

Abstract Many real-world networks are intrinsically directed. Such networks include activation of genes, hyperlinks on the internet and the network of followers on Twitter among many others. The challenge, however, is to create a network model that has many of the properties of real-world networks such as power-law degree distributions and the small-world property. To meet these challenges, we introduce the Directed Random Geometric Graph (DRGG) model, which is an extension of the random geometric graph model. We prove that it is scale-free with respect to the indegree distribution, has binomial outdegree distribution, has a high clustering coefficient, has few edges and is likely small-world. These are some of the main features of aforementioned real-world networks. We also empirically observed that word association networks have many of the theoretical properties of the DRGG model.

HIERARCHICAL MODULARITY OF THE FUNCTIONAL NEURAL NETWORK ORGANIZED BY SPIKE TIMING DEPENDENT SYNAPTIC PLASTICITY

2007 ◽  
Vol 21 (23n24) ◽  
pp. 4124-4129
Author(s):  
CHANG-WOO SHIN ◽  
SEUNGHWAN KIM
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Small World ◽  
Spike Timing ◽  
Synaptic Reorganization ◽  
Scale Free ◽  
Functional Neural Network

We study the emergent functional neural network organized by synaptic reorganization by the spike timing dependent synaptic plasticity (STDP). We show that small-world and scale-free functional structures organized by STDP, in the case of synaptic balance, exhibit hierarchial modularity.

scholarly journals Complex network description of the ionosphere

2018 ◽  
Vol 25 (1) ◽  
pp. 233-240
Author(s):  
Shikun Lu ◽  
Hao Zhang ◽  
Xihai Li ◽  
Yihong Li ◽  
Chao Niu ◽  
...  
Keyword(s):  
Complex Network ◽  
Hypothesis Test ◽  
Small World ◽  
Dynamic Processes ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Total Electron ◽  
Scale Free ◽  
Probabilistic Graph ◽  
Small World Property

Abstract. Complex networks have emerged as an essential approach of geoscience to generate novel insights into the nature of geophysical systems. To investigate the dynamic processes in the ionosphere, a directed complex network is constructed, based on a probabilistic graph of the vertical total electron content (VTEC) from 2012. The results of the power-law hypothesis test show that both the out-degree and in-degree distribution of the ionospheric network are not scale-free. Thus, the distribution of the interactions in the ionosphere is homogenous. None of the geospatial positions play an eminently important role in the propagation of the dynamic ionospheric processes. The spatial analysis of the ionospheric network shows that the interconnections principally exist between adjacent geographical locations, indicating that the propagation of the dynamic processes primarily depends on the geospatial distance in the ionosphere. Moreover, the joint distribution of the edge distances with respect to longitude and latitude directions shows that the dynamic processes travel further along the longitude than along the latitude in the ionosphere. The analysis of “small-world-ness” indicates that the ionospheric network possesses the small-world property, which can make the ionosphere stable and efficient in the propagation of dynamic processes.

Encoding specificity of scale-free spiking neural network under different external stimulations

2020 ◽  
Vol 418 ◽  
pp. 126-138
Author(s):  
Lei Guo ◽  
LiTing Hou ◽  
YouXi Wu ◽  
Huan Lv ◽  
HongLi Yu
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Scale Free ◽  
Encoding Specificity

DYNAMICS BEHAVIORS OF WEIGHTED LOCAL-WORLD EVOLVING NETWORKS WITH EXTENDED LINKS

2009 ◽  
Vol 20 (11) ◽  
pp. 1719-1735 ◽  
Author(s):  
GUANGHUI WEN ◽  
ZHISHENG DUAN
Keyword(s):  
Weak Interactions ◽  
Small World ◽  
Local Information ◽  
Scale Free ◽  
Evolving Networks ◽  
Free Behavior ◽  
Small World Property ◽  
Evolving Model ◽  
Analytical Expressions ◽  
Good Agreement

In this paper, we present a local-world evolving model to characterize weighted networks. By introducing the extended links to mimic the weak interactions between the nodes in different local-worlds, the model yields scale-free behavior as well as the small-world property, as confirmed in many real networks. With the increase of the local information, the generated network undergoes a transition from assortative to disassortative, meanwhile the small-world property is preserved. It indicates that the small-world property is a universal characteristic in our model. The numerical simulation results are in good agreement with the analytical expressions.

scholarly journals Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244683
Author(s):  
Lei Guo ◽  
Enyu Kan ◽  
Youxi Wu ◽  
Huan Lv ◽  
Guizhi Xu
Keyword(s):  
Neural Network ◽  
Nervous System ◽  
Synaptic Plasticity ◽  
Gaussian Noise ◽  
Noise Suppression ◽  
White Gaussian Noise ◽  
The Self ◽  
Spiking Neural Network ◽  
Scale Free ◽  
Self Adaptive

With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

scholarly journals Optimisation of the coalescent hyperbolic embedding of complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bianka Kovács ◽  
Gergely Palla
Keyword(s):  
Small World ◽  
Hyperbolic Distance ◽  
Greedy Routing ◽  
Scale Free ◽  
Recent Approach ◽  
Strong Motivation ◽  
Angular Coordinates ◽  
Small World Property ◽  
Logarithmic Loss ◽  
Hyperbolic Embedding

AbstractSeveral observations indicate the existence of a latent hyperbolic space behind real networks that makes their structure very intuitive in the sense that the probability for a connection is decreasing with the hyperbolic distance between the nodes. A remarkable network model generating random graphs along this line is the popularity-similarity optimisation (PSO) model, offering a scale-free degree distribution, high clustering and the small-world property at the same time. These results provide a strong motivation for the development of hyperbolic embedding algorithms, that tackle the problem of finding the optimal hyperbolic coordinates of the nodes based on the network structure. A very promising recent approach for hyperbolic embedding is provided by the noncentered minimum curvilinear embedding (ncMCE) method, belonging to the family of coalescent embedding algorithms. This approach offers a high-quality embedding at a low running time. In the present work we propose a further optimisation of the angular coordinates in this framework that seems to reduce the logarithmic loss and increase the greedy routing score of the embedding compared to the original version, thereby adding an extra improvement to the quality of the inferred hyperbolic coordinates.

Research on the Coal Logistics Network's Property Based on the Complex System Theory of Supply Chain

2013 ◽  
Vol 869-870 ◽  
pp. 251-255
Author(s):  
Li Jun Liu ◽  
Xiao Ji Guan
Keyword(s):  
Complex System ◽  
System Theory ◽  
Supply Chain ◽  
Complex Network ◽  
Small World ◽  
Practical Significance ◽  
Logistics Network ◽  
Scale Free ◽  
Small World Property ◽  
Coal Enterprises

This article puts forward the practical significance of studying the complex characteristic of coal logistics network from the coal resource effective utilization and rational allocation. According to the characteristics of coal enterprises, the coal logistics network is defined as the complex network system. Based on the complex system theory of the supply chain, the coal logistics network is analysed separately from the complexity of node and structure, the small word property as well as the scale-free property and it is pointed out that the coal network has complex network structure, the small world property and the scale-free property.

STOCHASTIC EVOLVING MODEL FOR COMPLEX NETWORKS

2004 ◽  
Vol 18 (23) ◽  
pp. 1157-1164 ◽  
Author(s):  
HYUN-JOO KIM ◽  
YEON-MU CHOI ◽  
JIN MIN KIM
Keyword(s):  
Control Parameter ◽  
Small World ◽  
Network Size ◽  
Clustering Coefficient ◽  
Scale Free ◽  
Hierarchical Topology ◽  
Free Behavior ◽  
Small World Property ◽  
Evolving Model ◽  
High Degree

We introduce an evolving complex network model, where a new vertex is added and new edges between already existing vertices are added with a control parameter p. The model shows the characteristics of real networks such as small-world property, high degree of clustering, scale-free behavior in degree distribution, and hierarchical topology. We obtain the various values of degree exponent γ in the range 2<γ≤3 by adjusting the parameter p and find that the degree exponent decreases logarithmically with p. In addition, the clustering coefficient is tunable by changing the control parameter p, and the average path length L is proportional to ln ( ln N) with nonzero p, where N is the network size.

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