Neural Decision Fields

Neural Decision Fields is a computational neuroscience model that formalizes decision-making as the evolution of activation patterns in neural populations, where competing alternatives are represented as dynamic fields of neurons whose activity accumulates evidence over time until a threshold is reached, thereby distinguishing it from static or symbolic models of choice by emphasizing continuous, distributed, and biologically plausible neural dynamics.

11

Publications

9.1K

Citations

28

Authors

16

Institutions

Publications per year

2017–2026

3

Authors

28

  1. BY
    Benjamin Y. Hayden

    University of Minnesota, United States

    1 publications · 12 citations

    1

    h-index

  2. PW
    Philip Woodgate

    National Hospital for Neurology and Neurosurgery, United Kingdom

    1 publications · 31 citations

    1

    h-index

  3. DM
    David Milstein

    Albert Einstein College of Medicine, United States

    1 publications · 145 citations

    1

    h-index

  4. AC
    Ana Carolina Pereira

    Instituto de Investigaciones Biológicas Clemente Estable, Uruguay

    1 publications · 35 citations

    1

    h-index

  5. JJ
    J. J. Hopfield

    Princeton University, United States

    1 publications · 6K citations

    1

    h-index

1–5 of 28

Venues

  1. Psychological Review

    2 publications · 813 citations

    2

    h-index

  2. Neural Networks

    1 publications · 31 citations

    1

    h-index

  3. Neuron

    1 publications · 32 citations

    1

    h-index

  4. Brain and Neuroscience Advances

    1 publications · 12 citations

    1

    h-index

  5. Information Sciences

    1 publications · 22 citations

    1

    h-index

1–5