Population Vector Coding

Population Vector Coding is a neural coding framework that decodes distributed population activity by computing a weighted vector sum of preferred stimuli, where the weights correspond to individual neuron firing rates, thereby estimating the encoded stimulus or movement direction from ensemble neural responses.

34

Publications

5.1K

Citations

82

Authors

61

Institutions

Publications per year

2017–2026

6

Authors

82

  1. AB
    Andrew B. Schwartz

    University of Pittsburgh, United States

    6 publications · 1.9K citations

    6

    h-index

  2. AP
    Apostolos P. Georgopoulos

    University of Minnesota Medical Center, United States

    4 publications · 1.1K citations

    4

    h-index

  3. SI
    Stephen I. Helms Tillery

    Arizona State University, United States

    3 publications · 202 citations

    3

    h-index

  4. DW
    Daniel W. Moran

    Washington University in St. Louis, United States

    2 publications · 471 citations

    2

    h-index

  5. KT
    Kazuyoshi Takeda

    Juntendo University, Japan

    2 publications · 112 citations

    2

    h-index

1–5 of 82

Venues

  1. Journal of Neurophysiology

    JN · 5 publications · 1.5K citations

    5

    h-index

  2. Journal of Neuroscience

    3 publications · 167 citations

    3

    h-index

  3. European Journal of Neuroscience

    EJN · 2 publications · 147 citations

    2

    h-index

  4. Biological Cybernetics

    2 publications · 57 citations

    2

    h-index

  5. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 742 citations

    2

    h-index

1–5