Behavioral Time Scale Plasticity

Behavioral Time Scale Plasticity is a computational learning framework in neuroscience that extends spike-timing-dependent plasticity by integrating synaptic changes over behavioral timescales, thereby enabling neurons to associate events separated by seconds rather than milliseconds.

72

Publications

4.9K

Citations

271

Authors

141

Institutions

Publications per year

2017–2026

18

Variants

Btsp

Authors

271

  1. YN
    Yoshihisa Nakahata

    National Institute for Physiological Sciences, Japan

    2 publications · 50 citations

    2

    h-index

  2. LF
    L. F. Abbott

    Columbia University, United States

    2 publications · 254 citations

    2

    h-index

  3. AJ
    Anant Jain

    Max Planck Florida Institute for Neuroscience, United States

    2 publications · 50 citations

    2

    h-index

  4. RY
    Ryohei Yasuda

    Max Planck Florida Institute for Neuroscience, United States

    2 publications · 50 citations

    2

    h-index

  5. HF
    Hiro Furukawa

    Cold Spring Harbor Laboratory, United States

    2 publications · 50 citations

    2

    h-index

1–5 of 271

Venues

  1. 6

    h-index

  2. Neuron

    6 publications · 1.2K citations

    6

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 54 citations

    3

    h-index

  4. Journal of Comparative Physiology A

    2 publications · 113 citations

    2

    h-index

  5. Behavioral Ecology and Sociobiology

    2 publications · 177 citations

    2

    h-index

1–5