Continuous Attractor Networks
Continuous Attractor Networks is a type of recurrent neural network model in computational neuroscience characterized by a continuous manifold of stable activity states, enabling the representation and maintenance of continuously varying variables such as spatial orientation or head direction through dynamic attractor dynamics.
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2017–2026
29
Variants
Continuous Attractor Neural Networks
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Specific evidence of low-dimensional continuous attractor dynamics in grid cells
Kijung Yoon, Michael A. Buice, Caswell Barry et al. · Nature Neuroscience · 2013 · 339 citations · Full text
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University of Oxford
Oxford, United Kingdom
7 h-index
20 publications
1.1K citations
Oxford Centre for Computational Neuroscience
Oxford, United Kingdom
7 h-index
15 publications
608 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
5 h-index
10 publications
532 citations

Beijing Normal University
Beijing, China
5 h-index
11 publications
418 citations

Sichuan University
Chengdu, China
4 h-index
5 publications
186 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
6 publications
245 citations
Harvard University
Cambridge, United States
4 h-index
4 publications
1.1K citations

Scuola Internazionale Superiore di Studi Avanzati
Trieste, Italy
4 h-index
10 publications
329 citations
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