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
Parents
11
1–5 of 11
28
1
h-index
National Hospital for Neurology and Neurosurgery, United Kingdom
1 publications · 31 citations
1
h-index
1
h-index
Instituto de Investigaciones Biológicas Clemente Estable, Uruguay
1 publications · 35 citations
1
h-index
1
h-index
1–5 of 28
16
California Institute of Technology
Pasadena, United States
2 h-index
2 publications
6.1K citations
Canadian Institutes of Health Research
Ottawa, Canada
1 h-index
2 publications
290 citations

University of Central Florida
Orlando, United States
1 h-index
1 publications
35 citations
Instituto de Investigaciones Biológicas Clemente Estable
Montevideo, Uruguay
1 h-index
4 publications
140 citations
German Primate Center
Göttingen, Germany
1 h-index
2 publications
66 citations
University of Arizona
Tucson, United States
1 h-index
2 publications
44 citations
University of Iowa
Iowa City, United States
1 h-index
2 publications
3.9K citations

University of Minnesota
Minneapolis, United States
1 h-index
1 publications
12 citations
1–8 of 16
2 publications · 813 citations
2
h-index
1 publications · 31 citations
1
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1 publications · 32 citations
1
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1 publications · 12 citations
1
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1 publications · 22 citations
1
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1–5