dorsal/arxiv
View SchemaPerformance of networks of artificial neurons: The role of clustering
| Authors | Beom Jun Kim |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0402045 |
| URL | https://arxiv.org/abs/q-bio/0402045 |
| DOI | 10.1103/PhysRevE.69.045101 |
| Journal | Phys. Rev. E 69, 045101(R) (2004) |
Abstract
The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barab{\'a}si-Albert network, and the neuronal network of the C. elegans. Through the use of a systematic way of controlling the clustering coefficient, with the degree of each neuron kept unchanged, we find that the networks with the lower clustering exhibit much better performance. The results are discussed in the practical viewpoint of application, and the biological implications are also suggested.
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"abstract": "The performance of the Hopfield neural network model is numerically studied\non various complex networks, such as the Watts-Strogatz network, the\nBarab{\\\u0027a}si-Albert network, and the neuronal network of the C. elegans.\nThrough the use of a systematic way of controlling the clustering coefficient,\nwith the degree of each neuron kept unchanged, we find that the networks with\nthe lower clustering exhibit much better performance. The results are discussed\nin the practical viewpoint of application, and the biological implications are\nalso suggested.",
"arxiv_id": "q-bio/0402045",
"authors": [
"Beom Jun Kim"
],
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"q-bio.NC"
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"doi": "10.1103/PhysRevE.69.045101",
"journal_ref": "Phys. Rev. E 69, 045101(R) (2004)",
"title": "Performance of networks of artificial neurons: The role of clustering",
"url": "https://arxiv.org/abs/q-bio/0402045"
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