Probabilistic Population Coding
Probabilistic Population Coding is a neural coding framework that represents probability distributions over stimuli through the collective activity of neuronal populations, where the firing rates of neurons encode the parameters of a likelihood function in a manner that enables Bayesian inference via linear readout mechanisms.
31
Publications
4.8K
Citations
87
Authors
61
Institutions
Publications per year
2017–2026
8
Parents
31
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
1–5 of 31
87
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Oxford Centre for Computational Neuroscience, United Kingdom
4 publications · 3K citations
4
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2
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2
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1–5 of 87
61

University of Rochester
Rochester, United States
6 h-index
12 publications
8.5K citations
New York University
New York, United States
3 h-index
5 publications
191 citations
University of California, Los Angeles
Los Angeles, United States
3 h-index
3 publications
942 citations
University of Illinois Chicago
Chicago, United States
2 h-index
2 publications
1K citations

University of Tübingen
Tübingen, Germany
2 h-index
2 publications
205 citations
Baylor College of Medicine
Houston, United States
2 h-index
3 publications
772 citations
Oxford Centre for Computational Neuroscience
Oxford, United Kingdom
2 h-index
2 publications
2.2K citations
Netherlands Institute for Neuroscience
Amsterdam, Netherlands
2 h-index
9 publications
992 citations
1–8 of 61
5 publications · 2.8K citations
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3 publications · 315 citations
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3 publications · 790 citations
3
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2 publications · 73 citations
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PNAS · 2 publications · 220 citations
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