Dimensionality Reduction For Decoding
Dimensionality Reduction for Decoding is a computational method that transforms high-dimensional neural activity data into a lower-dimensional latent space while preserving task-relevant informational structure to improve the accuracy and efficiency of neural decoding.
31
Publications
1.7K
Citations
129
Authors
84
Institutions
Publications per year
2017–2026
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University of California San Diego
San Diego, United States
2 h-index
3 publications
514 citations
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Paris, France
2 h-index
11 publications
376 citations
ETH Zurich
Zurich, Switzerland
2 h-index
5 publications
93 citations
Dalian University of Technology
Dalian, China
1 h-index
1 publications
40 citations
RF Laboratories (United States)
Oostburg, United States
1 h-index
1 publications
354 citations
University of Brescia
Brescia, Italy
1 h-index
2 publications
28 citations
University of Pennsylvania
Philadelphia, United States
1 h-index
6 publications
288 citations

Vanderbilt University
Nashville, United States
1 h-index
1 publications
354 citations
1–8 of 84
4 publications · 457 citations
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3 publications · 79 citations
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1 publications · 64 citations
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1 publications · 15 citations
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