scholarly journals Long-range temporal correlations in scale-free neuromorphic networks

2020 ◽  
Vol 4 (2) ◽  
pp. 432-447 ◽  
Author(s):  
Shota Shirai ◽  
Susant Kumar Acharya ◽  
Saurabh Kumar Bose ◽  
Joshua Brian Mallinson ◽  
Edoardo Galli ◽  
...  
Keyword(s):  
Long Range ◽  
Neuronal Networks ◽  
Temporal Dynamics ◽  
Structural Characteristics ◽  
Small World ◽  
Mammalian Brain ◽  
Scale Free ◽  
Temporal Correlations ◽  
Significant Step ◽  
Computational Ability

Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing.

scholarly journals Characterizing the Structure of the Railway Network in China: A Complex Weighted Network Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Cao ◽  
Xiangnan Feng ◽  
Jianmin Jia ◽  
Hong Zhang
Keyword(s):  
Structural Characteristics ◽  
Flow Distribution ◽  
Small World ◽  
Centrality Measures ◽  
Railway Network ◽  
Weighted Network ◽  
Scale Free ◽  
Topological Measures ◽  
Free Network

Understanding the structure of the Chinese railway network (CRN) is crucial for maintaining its efficiency and planning its future development. To advance our knowledge of CRN, we modeled CRN as a complex weighted network and explored the structural characteristics of the network via statistical evaluations and spatial analysis. Our results show CRN as a small-world network whose train flow obeys power-law decaying, demonstrating that CRN is a mature transportation infrastructure with a scale-free structure. CRN also shows significant spatial heterogeneity and hierarchy in its regionally uneven train flow distribution. We then examined the nodal centralities of CRN using four topological measures: degree, strength, betweenness, and closeness. Nodal degree is positively correlated with strength, betweenness, and closeness. Unlike the common feature of a scale-free network, the most connected nodes in CRN are not necessarily the most central due to underlying geographical, political, and socioeconomic factors. We proposed an integrated measure based on the four centrality measures to identify the global role of each node and the multilayer structure of CRN and confirm that stable connections hold between different layers of CRN.

Attenuation of long-range temporal correlations in neuronal oscillations in patients with schizophrenia

2011 ◽  
Vol 26 (S2) ◽  
pp. 1461-1461
Author(s):  
V.V. Nikulin ◽  
E.G. Jönsson ◽  
G. Curio ◽  
T. Brismar
Keyword(s):  
Long Range ◽  
Negative Symptoms ◽  
Temporal Dynamics ◽  
Normal Subjects ◽  
Fluctuation Analysis ◽  
Normal Brain ◽  
Neuronal Dynamics ◽  
Neuronal Oscillations ◽  
Temporal Correlations ◽  
And Gender

IntroductionAlthough schizophrenia was previously associated with affected spatial neuronal synchronization, surprisingly little is known about the temporal dynamics of neuronal oscillations in schizophrenia. However, since coordination of neuronal process in time represents an essential aspect of practically all cognitive processes, it might be strongly affected in schizophrenia patients.ObjectivesTo test the hypothesis of abnormal temporal neuronal dynamics in patients with schizophrenia.AimsWe aimed at quantification and comparisons of long-range temporal correlations (LRTCs) in patients and normal subjects.MethodsWe measured 21 patients with schizophrenia (n = 18) or schizoaffective disorder (n = 3) and 28 age and gender matched controls. Ongoing neuronal oscillations were recorded with multi-channel EEG at rest condition. EEG was analyzed with spectral analysis and with the detrended fluctuation analysis allowing quantification of LRTCs.ResultsThe amplitude of neuronal oscillations in alpha and beta frequency ranges did not differ between the patients and controls. However, LRTCs were strongly attenuated in schizophrenia patients: in centro-parietal areas and fronto-central areas for alpha and beta oscillations, respectively. In addition we observed a negative correlation between the strength of the negative symptoms and LRTCs.ConclusionsSmall values of LRTCs and their correlation with the negative symptoms in schizophrenia patients demonstrate that the temporal dynamics of neuronal oscillations are essential for normal brain functioning. Attenuated LRTCs might indicate a more intermittent neuronal dynamics possibly allowing for more random associations between neuronal activations, which in turn might relate to the occurrence of positive symptoms in schizophrenia.

scholarly journals Co-Occurrence Network of High-Frequency Words in the Bioinformatics Literature: Structural Characteristics and Evolution

2018 ◽  
Vol 8 (10) ◽  
pp. 1994 ◽  
Author(s):  
Taoying Li ◽  
Jie Bai ◽  
Xue Yang ◽  
Qianyu Liu ◽  
Yan Chen
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Structural Characteristics ◽  
Small World ◽  
Small World Network ◽  
Frequency Word ◽  
Scale Free ◽  
Rich Club ◽  
Word Selection ◽  
High Frequency Words

The subjects of literature are the direct expression of the author’s research results. Mining valuable knowledge helps to save time for the readers to understand the content and direction of the literature quickly. Therefore, the co-occurrence network of high-frequency words in the bioinformatics literature and its structural characteristics and evolution were analysed in this paper. First, 242,891 articles from 47 top bioinformatics periodicals were chosen as the object of the study. Second, the co-occurrence relationship among high-frequency words of these articles was analysed by word segmentation and high-frequency word selection. Then, a co-occurrence network of high-frequency words in bioinformatics literature was built. Finally, the conclusions were drawn by analysing its structural characteristics and evolution. The results showed that the co-occurrence network of high-frequency words in the bioinformatics literature was a small-world network with scale-free distribution, rich-club phenomenon and disassortative matching characteristics. At the same time, the high-frequency words used by authors changed little in 2–3 years but varied greatly in four years because of the influence of the state-of-the-art technology.

scholarly journals The Structure and Periodicity of the Chinese Air Passenger Network

2018 ◽  
Vol 11 (1) ◽  
pp. 54
Author(s):  
Hongqi Li ◽  
Haotian Wang ◽  
Ming Bai ◽  
Bin Duan
Keyword(s):  
Time Scale ◽  
Network Performance ◽  
Structural Characteristics ◽  
Small World ◽  
Air Transportation ◽  
Structural Features ◽  
Power Law Distribution ◽  
Scale Free ◽  
Temporal Features ◽  
Scale Interval

China’s air transportation system is evolving with its own unique mechanism. In particular, the structural features of the Chinese air passenger network (CAPN) are of interest. This paper aims to analyze the CAPN from holistic and microcosmic perspectives. Considering that the topological structure and the capacity (i.e., available passenger-seats) flow are important to the air network’s performance, the CAPN structure features from non-weighted and weighted perspectives are analyzed. Subnets extracted by time-scale constraints of one day or every two-hours are used to find the temporal features. This paper provides some valuable conclusions about the structural characteristics and temporal features of the CAPN. The results indicate that the CAPN has a small-world and scale-free structure. The cumulative degree distribution of the CAPN follows a two-regime power-law distribution. The CAPN tends to be disassortative. Some important airports, including national air-hubs and local air-hubs, remarkably affect the CAPN. About 90% of large capacities exist between airports with large degrees. The properties of CAPN subnets extracted by taking two hours as the time-scale interval shed light on the air network performance and the changing rule more accurately and microcosmically. The method of the spectral destiny estimation is used to find the implicit periodicity mathematically. For most indicators, a one-day cycle, two-day cycle, and/or three-day cycle can be found.

Impact of Partial Time Delay on Temporal Dynamics of Watts–Strogatz Small-World Neuronal Networks

2017 ◽  
Vol 27 (07) ◽  
pp. 1750112 ◽  
Author(s):  
Hao Yan ◽  
Xiaojuan Sun
Keyword(s):  
Time Delay ◽  
Firing Rate ◽  
Neuronal Networks ◽  
Temporal Dynamics ◽  
Great Influence ◽  
Small World ◽  
Temporal Coherence ◽  
Small Time ◽  
Neuronal Firing ◽  
Two Parameters

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts–Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay [Formula: see text] and the other is the probability of partial time delay [Formula: see text]. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay [Formula: see text], the probability [Formula: see text] could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay [Formula: see text], temporal coherence and mean firing rate do not have great changes with respect to [Formula: see text]. Time delay [Formula: see text] always has great influence on both temporal coherence and mean firing rate no matter what is the value of [Formula: see text]. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay [Formula: see text]. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

scholarly journals Long range correlations and slow time scales in a boundary driven granular model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Plati ◽  
Andrea Puglisi
Keyword(s):  
Long Range ◽  
Time Scales ◽  
Statistical Physics ◽  
Spatial Correlations ◽  
Slow Time ◽  
Finite Characteristic ◽  
Long Range Correlations ◽  
Scale Free ◽  
Temporal Correlations ◽  
Free Cooling

AbstractWe consider a velocity field with linear viscous interactions defined on a one dimensional lattice. Brownian baths with different parameters can be coupled to the boundary sites and to the bulk sites, determining different kinds of non-equilibrium steady states or free-cooling dynamics. Analytical results for spatial and temporal correlations are provided by analytical diagonalisation of the system’s equations in the infinite size limit. We demonstrate that spatial correlations are scale-free and time-scales become exceedingly long when the system is driven only at the boundaries. On the contrary, in the case a bath is coupled to the bulk sites too, an exponential correlation decay is found with a finite characteristic length. This is also true in the free cooling regime, but in this case the correlation length grows diffusively in time. We discuss the crucial role of boundary driving for long-range correlations and slow time-scales, proposing an analogy between this simplified dynamical model and dense vibro-fluidized granular materials. Several generalizations and connections with the statistical physics of active matter are also suggested.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

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