Probabilistic Population Codes
Probabilistic Population Codes is a computational neuroscience framework that represents probability distributions over stimuli through the collective activity of neural populations, where the firing rates of neurons encode posterior probabilities in a manner that supports Bayesian inference via linear decoding operations.
22
Publications
3.5K
Citations
62
Authors
41
Institutions
Publications per year
2017–2026
7
Parents
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Bayesian inference with probabilistic population codes
Wei Ji, Jeffrey M. Beck, Peter E. Latham et al. · Nature Neuroscience · 2006 · 1.5K citations
Probabilistic Population Codes for Bayesian Decision Making
Jeffrey M. Beck, Wei Ji, Roozbeh Kiani et al. · Neuron · 2008 · 692 citations · Full text
Geoffrey E. Hinton · 1999 · 352 citations
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Oxford Centre for Computational Neuroscience, United Kingdom
2 publications · 2.2K citations
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University of Rochester
Rochester, United States
4 h-index
8 publications
6.9K citations

University of Tübingen
Tübingen, Germany
2 h-index
2 publications
29 citations
University of Geneva
Geneva, Switzerland
2 h-index
2 publications
110 citations
Oxford Centre for Computational Neuroscience
Oxford, United Kingdom
2 h-index
2 publications
2.2K citations
New York University
New York, United States
2 h-index
4 publications
146 citations
University College London
London, United Kingdom
2 h-index
2 publications
1.9K citations
Baylor College of Medicine
Houston, United States
2 h-index
3 publications
772 citations
Ludwig-Maximilians-Universität München
Munich, Germany
1 h-index
3 publications
474 citations
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3 publications · 790 citations
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PNAS · 2 publications · 220 citations
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2 publications · 184 citations
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Repository · 1 publications · 35 citations
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1 publications · 40 citations
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