Neural Sampling
Neural Sampling is a computational framework within population coding that models perceptual inference and decision-making by representing probability distributions over stimuli through the stochastic activity patterns of neural populations, where each neuron's spiking activity corresponds to a sample from the underlying posterior distribution.
64
Publications
3.4K
Citations
187
Authors
78
Institutions
Publications per year
2017–2026
27
Parents
64
Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex
Gergő Orbán, Pietro Berkes, József Fiser et al. · Neuron · 2016 · 307 citations · Full text
Perceptual Decision-Making as Probabilistic Inference by Neural Sampling
Ralf M. Haefner, Pietro Berkes, József Fiser · Neuron · 2016 · 238 citations · Full text
Event-driven contrastive divergence for spiking neuromorphic systems
Emre Neftci, Srinjoy Das, Bruno U. Pedroni et al. · Frontiers in Neuroscience · 2014 · 218 citations · Full text
Alias-free sampling of neuronal spike trains
Andrew S. French, Arun V. Holden · Kybernetik · 1971 · 178 citations
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University of California San Diego
San Diego, United States
5 h-index
15 publications
1.4K citations
University of Cambridge
Cambridge, United Kingdom
5 h-index
21 publications
1.5K citations
Graz University of Technology
Graz, Austria
4 h-index
12 publications
573 citations
University of Pittsburgh
Pittsburgh, United States
4 h-index
5 publications
142 citations
Tsinghua University
Beijing, China
4 h-index
22 publications
700 citations

University of Notre Dame
Notre Dame, United States
3 h-index
10 publications
405 citations

University of Rochester
Rochester, United States
3 h-index
3 publications
714 citations
The University of Tokyo
Tokyo, Japan
2 h-index
4 publications
104 citations
1–8 of 78
Repository · 4 publications · 68 citations
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3 publications · 65 citations
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Repository · 3 publications · 33 citations
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PNAS · 3 publications · 289 citations
3
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3 publications · 644 citations
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