Performance of networks of artificial neurons: The role of clustering

Kim, Beom Jun

doi:10.1103/physreve.69.045101

Cited by 123 publications

(59 citation statements)

References 43 publications

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“…Future implementations should include more realistic biological topologies as those reviewed in §3. Other models of learning machines have explored the impact of the underlying network topology [66][67][68][69]. However, their goal is restricted to evaluate the performance of artificial neural networks to recognize learned patterns.…”

Section: Summary and Discussionmentioning

confidence: 99%

Characterizing the complexity of brain and mind networks

Zamora-López

Russo

Gleiser

et al. 2011

Phil. Trans. R. Soc. A.

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Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.

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Section: Summary and Discussionmentioning

confidence: 99%

Characterizing the complexity of brain and mind networks

Zamora-López

Russo

Gleiser

et al. 2011

Phil. Trans. R. Soc. A.

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“…Toward this direction, various algorithms for generating network topologies with prescribed characteristics have been proposed including the tuning of the degree distribution, clustering coefficient and assortativity. [13][14][15][16][17][18][19] Here we attempt to analyze the "distinct" effect of the average path length (APL) with respect to the degree and clustering distributions on the emergent dynamics of a simple epidemic model evolving on small-world networks. The path length [together with the clustering coefficient (CC)] is one of the most important statistical measures of small-world topologies and is considered to have a high impact on the spatio-temporal evolution of epidemics.…”

Section: Introductionmentioning

confidence: 99%

On the effect of the path length of small-world networks on epidemic dynamics

2012

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We show how one can trace in a systematic way the coarse-grained solutions of individual-based stochastic epidemic models evolving on heterogeneous complex networks with respect to their topological characteristics. In particular, we illustrate the "distinct" impact of the average path length (with respect to the degree and clustering distributions) on the emergent behavior of detailed epidemic models; to achieve this we have developed an algorithm that allows its tuning at will. The framework could be used to shed more light on the influence of weak social links on epidemic spread within small-world network structures, and ultimately to provide novel systematic computational modeling and exploration of better contagion control strategies.

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“…Kim [12] has recently used rewiring algorithms to introduce large amounts of local clustering into networks. Using a MC simulations at zero-temperature (i.e.…”

Section: Random Network Modelmentioning

confidence: 99%

“…Many random network models have been proposed to replicate important aspects of the topology of real-world networks [1,2,3,4,5,6,7,8,9,10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Random networks with tunable degree distribution and clustering

Volz

2004

Phys. Rev. E

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We present an algorithm for generating random networks with arbitrary degree distribution and clustering (frequency of triadic closure). We use this algorithm to generate networks with exponential, power law, and poisson degree distributions with variable levels of clustering. Such networks may be used as models of social networks and as a testable null hypothesis about network structure. Finally, we explore the effects of clustering on the point of the phase transition where a giant component forms in a random network, and on the size of the giant component.

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Performance of networks of artificial neurons: The role of clustering

Cited by 123 publications

References 43 publications

Characterizing the complexity of brain and mind networks

Characterizing the complexity of brain and mind networks

On the effect of the path length of small-world networks on epidemic dynamics

Random networks with tunable degree distribution and clustering

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