Low-dimensional Neural Dynamics

Low-Dimensional Neural Dynamics is a theoretical construct in systems neuroscience that characterizes the collective activity of large neural populations as evolving within a reduced, lower-dimensional latent subspace of neural state space, capturing essential functional patterns and temporal structure while filtering out redundant or noisy dimensions in neural representation.

188

Publications

12.6K

Citations

530

Authors

292

Institutions

Publications per year

2017–2026

115

Authors

530

  1. SO
    Srdjan Ostojic

    Laboratoire de Neurosciences Cognitives, France

    8 publications · 601 citations

    8

    h-index

  2. KV
    Krishna V. Shenoy

    Stanford University, United States

    7 publications · 1.8K citations

    7

    h-index

  3. ES
    Eric Shea‐Brown

    University of Washington, United States

    6 publications · 247 citations

    6

    h-index

  4. BM
    Byron M. Yu

    Carnegie Mellon University, United States

    6 publications · 1.3K citations

    6

    h-index

  5. GD
    Gustavo Deco

    Universitat Pompeu Fabra, Spain

    5 publications · 356 citations

    5

    h-index

1–5 of 530

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 17 publications · 728 citations

    12

    h-index

  2. PLoS Computational Biology

    11 publications · 589 citations

    11

    h-index

  3. Neural Computation

    9 publications · 1.1K citations

    9

    h-index

  4. arXiv (Cornell University)

    Repository · 8 publications · 252 citations

    8

    h-index

  5. Neuron

    8 publications · 1.4K citations

    8

    h-index

1–5