Sparse Connectivity is a neural network architecture characterized by a deliberately reduced number of synaptic connections between neurons, in contrast to fully connected layers, to enhance computational efficiency and model interpretability while preserving or minimizing degradation of predictive performance.
196
Publications
12.4K
Citations
674
Authors
323
Institutions
Publications per year
2017–2026
86
Parents
Publications
196
Pruning Filters for Efficient ConvNets
Hao Li, Asim Kadav, Igor Đurđanović et al. · arXiv (Cornell University) · 2016 · 685 citations · Full text
Synaptic mechanisms of pattern completion in the hippocampal CA3 network
Segundo J. Guzman, Alois Schlögl, Michael Frotscher et al. · Science · 2016 · 307 citations
1–5 of 196
Authors
674
- VD
3
h-index
- ND
3
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- WM
3
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- RA
3
h-index
- RA
3
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1–5 of 674
Institutions
323
Centre National de la Recherche Scientifique
Paris, France
9 h-index
25 publications
1.9K citations
Purdue University West Lafayette
West Lafayette, United States
6 h-index
10 publications
281 citations
Princeton University
Princeton, United States
6 h-index
12 publications
2.7K citations
ETH Zurich
Zurich, Switzerland
6 h-index
12 publications
437 citations
University of Cambridge
Cambridge, United Kingdom
5 h-index
10 publications
693 citations
University College London
London, United Kingdom
5 h-index
10 publications
1K citations
Stanford University
Stanford, United States
4 h-index
5 publications
231 citations
University of California San Diego
San Diego, United States
4 h-index
8 publications
351 citations
1–8 of 323
Venues
- arXiv (Cornell University)
Repository · 17 publications · 1.5K citations
15
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- NeuroImage
8 publications · 421 citations
8
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- Journal of Neuroscience
6 publications · 603 citations
6
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- PLoS ONE
5 publications · 247 citations
5
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- Neural Computation
4 publications · 429 citations
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