Sparse Sensory Coding

Sparse Sensory Coding is a computational principle in sensory system modeling whereby sensory information is represented efficiently through the activation of a minimal subset of neurons, such that each stimulus is encoded by few active units relative to the total population.

63

Publications

6.5K

Citations

175

Authors

96

Institutions

Publications per year

2017–2026

13

Authors

175

  1. KP
    Konrad P. Körding

    Northwestern University, United States

    3 publications · 108 citations

    3

    h-index

  2. MP
    Martin Paul Nawrot

    Freie Universität Berlin, Germany

    3 publications · 84 citations

    3

    h-index

  3. MS
    Mark Stopfer

    Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States

    3 publications · 147 citations

    3

    h-index

  4. ET
    Edmund T. Rolls

    University of Oxford, United Kingdom

    3 publications · 793 citations

    3

    h-index

  5. CJ
    Christopher J. Rozell

    Georgia Institute of Technology, United States

    3 publications · 125 citations

    3

    h-index

1–5 of 175

Venues

  1. Journal of Neuroscience

    11 publications · 1.2K citations

    11

    h-index

  2. Neuron

    5 publications · 1.2K citations

    5

    h-index

  3. PLoS Computational Biology

    4 publications · 174 citations

    4

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 4 publications · 267 citations

    4

    h-index

  5. Journal of Neurophysiology

    JN · 4 publications · 786 citations

    4

    h-index

1–5