Closed-loop Neural Data Analysis

Closed-Loop Neural Data Analysis is a neural data analysis framework that integrates real-time processing and feedback mechanisms to dynamically modulate experimental parameters or neural activity based on ongoing neural recordings.

3

Publications

54

Citations

19

Authors

14

Institutions

Publications per year

2017–2026

3

Authors

19

  1. JA
    Jeffrey A. Herron

    University of Wisconsin–Madison, United States

    1 publications · 22 citations

    1

    h-index

  2. RA
    R Anderson

    Stanford University, United States

    1 publications · 22 citations

    1

    h-index

  3. MK
    Mitsuo Kawato

    Advanced Telecommunications Research Institute International, Japan

    1 publications · 19 citations

    1

    h-index

  4. YS
    Yuka Sasaki

    Brown University, United States

    1 publications · 19 citations

    1

    h-index

  5. HB
    Helen Brontë‐Stewart

    Stanford University, United States

    1 publications · 22 citations

    1

    h-index

1–5 of 19

Venues

  1. Journal of Neural Engineering

    1 publications · 13 citations

    1

    h-index

  2. Scientific Data

    1 publications · 19 citations

    1

    h-index

1–2