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.

73

Publications

5.4K

Citations

144

Authors

92

Institutions

Publications per year

2017–2026

29

Variants

Continuous Attractor Neural Networks

Authors

144

  1. ET
    Edmund T. Rolls

    University of Oxford, United Kingdom

    12 publications · 731 citations

    12

    h-index

  2. TT
    Thomas Trappenberg

    Dalhousie University, Canada

    10 publications · 658 citations

    10

    h-index

  3. SM
    Simon M. Stringer

    University of Oxford, United Kingdom

    10 publications · 674 citations

    10

    h-index

  4. SW
    Si Wu

    Beijing Normal University, China

    7 publications · 292 citations

    7

    h-index

  5. IF
    Ila Fiete

    The University of Texas at Austin, United States

    5 publications · 1.4K citations

    6

    h-index

1–5 of 144

Venues

  1. Neural Computation

    8 publications · 326 citations

    8

    h-index

  2. Network Computation in Neural Systems

    6 publications · 478 citations

    6

    h-index

  3. PLoS Computational Biology

    4 publications · 1K citations

    5

    h-index

  4. eLife

    4 publications · 112 citations

    4

    h-index

  5. Frontiers in Computational Neuroscience

    4 publications · 151 citations

    4

    h-index

1–5