Associative Synaptic Learning
Associative Synaptic Learning is a theoretical construct in neuroscience that posits synapses as dynamic computational elements whose strength changes in response to correlated pre- and postsynaptic activity, thereby enabling neurons to associate temporally contiguous inputs through Hebbian plasticity mechanisms.
133
Publications
8.2K
Citations
631
Authors
258
Institutions
Publications per year
2017–2026
47
Parents
133
1–5 of 133
631
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National Institutes of Health
Bethesda, United States
5 h-index
18 publications
1.4K citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
19 publications
841 citations
Stanford University
Stanford, United States
4 h-index
13 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
4 h-index
15 publications
695 citations
National Institute of Neurological Disorders and Stroke
Bethesda, United States
4 h-index
20 publications
831 citations

East China Normal University
Shanghai, China
4 h-index
30 publications
1.2K citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
8 publications
376 citations
Columbia University
New York, United States
3 h-index
8 publications
674 citations
1–8 of 258
7 publications · 454 citations
7
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6 publications · 400 citations
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PNAS · 5 publications · 330 citations
5
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4 publications · 100 citations
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4 publications · 94 citations
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1–5