Information-theoretic Coding

Information-Theoretic Coding is a computational framework within sensory coding that uses principles from information theory to characterize how sensory stimuli are efficiently represented and transmitted through neural systems by quantifying the reduction of uncertainty in neural responses relative to stimulus information.

649

Publications

53.2K

Citations

1.3K

Authors

541

Institutions

Publications per year

2017–2026

116

Authors

1.3K

  1. MM
    Muriel Médard

    Massachusetts Institute of Technology, United States

    8 publications · 351 citations

    8

    h-index

  2. OS
    Osvaldo Simeone

    New Jersey Institute of Technology, United States

    8 publications · 2.1K citations

    8

    h-index

  3. SS
    Shlomo Shamai

    Technion – Israel Institute of Technology, Israel

    7 publications · 2K citations

    7

    h-index

  4. GC
    Giuseppe Caire

    University of Southern California, United States

    7 publications · 320 citations

    7

    h-index

  5. SD
    Suhas Diggavi

    University of California, Los Angeles, United States

    7 publications · 274 citations

    7

    h-index

1–5 of 1.3K

Venues

  1. IEEE Transactions on Information Theory

    86 publications · 12.4K citations

    45

    h-index

  2. Journal of Neurophysiology

    JN · 17 publications · 2.3K citations

    16

    h-index

  3. arXiv (Cornell University)

    Repository · 21 publications · 529 citations

    14

    h-index

  4. IEEE Transactions on Communications

    18 publications · 545 citations

    14

    h-index

  5. Journal of Neuroscience

    11 publications · 1.5K citations

    11

    h-index

1–5