Attractor Networks
Attractor Networks is a type of recurrent neural network model distinguished by its dynamical property of converging to stable states, or attractors, which represent stored memories or patterns through fixed-point, cyclic, or chaotic trajectories in state space.
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Publications
61.8K
Citations
1.8K
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784
Institutions
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2017–2026
234
Variants
Attractor Neural Networks
Parents
784
The yeast cell-cycle network is robustly designed
Fangting Li, Tao Long, Ying Lu et al. · Proceedings of the National Academy of Sciences · 2004 · 1.1K citations · Full text
Life Sciences, Biology, Cell Biology +16
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Scuola Internazionale Superiore di Studi Avanzati, Italy
16 publications · 969 citations
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University of Oxford
Oxford, United Kingdom
23 h-index
51 publications
3.4K citations

Howard Hughes Medical Institute
Chevy Chase, United States
19 h-index
74 publications
7.2K citations
Oxford Centre for Computational Neuroscience
Oxford, United Kingdom
18 h-index
29 publications
1.6K citations
Institució Catalana de Recerca i Estudis Avançats
Barcelona, Spain
18 h-index
21 publications
1.5K citations

Universitat Pompeu Fabra
Barcelona, Spain
18 h-index
32 publications
2.7K citations
Hebrew University of Jerusalem
Jerusalem, Israel
18 h-index
39 publications
2.7K citations

KTH Royal Institute of Technology
Stockholm, Sweden
18 h-index
57 publications
2.7K citations
University College London
London, United Kingdom
16 h-index
38 publications
5.6K citations
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41 publications · 2.6K citations
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29 publications · 5.8K citations
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27 publications · 2.3K citations
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22 publications · 5.3K citations
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PNAS · 18 publications · 4K citations
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