Neural Network Heading Control is a control methodology that employs artificial neural networks to model, predict, and regulate the directional orientation of a dynamic system by learning complex nonlinear relationships between sensor inputs and actuation commands.
20
Publications
1.5K
Citations
53
Authors
27
Institutions
Publications per year
2017–2026
11
Parents
Publications
20
Generation of stable heading representations in diverse visual scenes
Sung Soo Kim, Ann M. Hermundstad, Sandro Romani et al. · Nature · 2019 · 195 citations · Full text
Sensorimotor experience remaps visual input to a heading-direction network
Yvette E. Fisher, Jenny Lu, Isabel D’Alessandro et al. · Nature · 2019 · 188 citations · Full text
Medicine, Neurobiology, Neuroscience +14
1–5 of 20
Authors
53
- TU
2
h-index
- AD
2
h-index
- GM
2
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- PM
2
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- AN
2
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1–5 of 53
Institutions
27
Harvard University
Cambridge, United States
3 h-index
6 publications
906 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
892 citations
Howard Hughes Medical Institute
Chevy Chase, United States
2 h-index
10 publications
1.7K citations
Princeton University
Princeton, United States
2 h-index
2 publications
446 citations
Rockefeller University
New York, United States
2 h-index
10 publications
770 citations
Center for the Neural Basis of Cognition
Pittsburgh, United States
2 h-index
4 publications
892 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
2 publications
446 citations
RIKEN
Wako, Japan
1 h-index
1 publications
51 citations
1–8 of 27
Venues
- Nature
2 publications · 383 citations
2
h-index
- Network Computation in Neural Systems
2 publications · 446 citations
2
h-index
- IEEE Access
1 publications · 63 citations
1
h-index
- Industrial Robot the international journal of robotics research and application
1 publications · 22 citations
1
h-index
- IEEE Journal of Oceanic Engineering
1 publications · 83 citations
1
h-index
1–5