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Investigating the representation of uncertainty in neuronal circuits
Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of unc...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880493/ https://www.ncbi.nlm.nih.gov/pubmed/33577553 http://dx.doi.org/10.1371/journal.pcbi.1008138 |
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author | Dehaene, Guillaume P. Coen-Cagli, Ruben Pouget, Alexandre |
author_facet | Dehaene, Guillaume P. Coen-Cagli, Ruben Pouget, Alexandre |
author_sort | Dehaene, Guillaume P. |
collection | PubMed |
description | Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of uncertainty. The first one, the Bayesian decoding approach, aims primarily at decoding the posterior probability distribution of the stimulus from population activity using Bayes’ rule, and indirectly yields uncertainty estimates as a by-product. The second one, which we call the correlational approach, searches for specific features of neuronal activity (such as tuning-curve width and maximum firing-rate) which correlate with uncertainty. To compare these two approaches, we derived a new normative model of sound source localization by Interaural Time Difference (ITD), that reproduces a wealth of behavioral and neural observations. We found that several features of neuronal activity correlated with uncertainty on average, but none provided an accurate estimate of uncertainty on a trial-by-trial basis, indicating that the correlational approach may not reliably identify which aspects of neuronal responses represent uncertainty. In contrast, the Bayesian decoding approach reveals that the activity pattern of the entire population was required to reconstruct the trial-to-trial posterior distribution with Bayes’ rule. These results suggest that uncertainty is unlikely to be represented in a single feature of neuronal activity, and highlight the importance of using a Bayesian decoding approach when exploring the neural basis of uncertainty. |
format | Online Article Text |
id | pubmed-7880493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78804932021-02-19 Investigating the representation of uncertainty in neuronal circuits Dehaene, Guillaume P. Coen-Cagli, Ruben Pouget, Alexandre PLoS Comput Biol Research Article Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of uncertainty. The first one, the Bayesian decoding approach, aims primarily at decoding the posterior probability distribution of the stimulus from population activity using Bayes’ rule, and indirectly yields uncertainty estimates as a by-product. The second one, which we call the correlational approach, searches for specific features of neuronal activity (such as tuning-curve width and maximum firing-rate) which correlate with uncertainty. To compare these two approaches, we derived a new normative model of sound source localization by Interaural Time Difference (ITD), that reproduces a wealth of behavioral and neural observations. We found that several features of neuronal activity correlated with uncertainty on average, but none provided an accurate estimate of uncertainty on a trial-by-trial basis, indicating that the correlational approach may not reliably identify which aspects of neuronal responses represent uncertainty. In contrast, the Bayesian decoding approach reveals that the activity pattern of the entire population was required to reconstruct the trial-to-trial posterior distribution with Bayes’ rule. These results suggest that uncertainty is unlikely to be represented in a single feature of neuronal activity, and highlight the importance of using a Bayesian decoding approach when exploring the neural basis of uncertainty. Public Library of Science 2021-02-12 /pmc/articles/PMC7880493/ /pubmed/33577553 http://dx.doi.org/10.1371/journal.pcbi.1008138 Text en © 2021 Dehaene et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dehaene, Guillaume P. Coen-Cagli, Ruben Pouget, Alexandre Investigating the representation of uncertainty in neuronal circuits |
title | Investigating the representation of uncertainty in neuronal circuits |
title_full | Investigating the representation of uncertainty in neuronal circuits |
title_fullStr | Investigating the representation of uncertainty in neuronal circuits |
title_full_unstemmed | Investigating the representation of uncertainty in neuronal circuits |
title_short | Investigating the representation of uncertainty in neuronal circuits |
title_sort | investigating the representation of uncertainty in neuronal circuits |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880493/ https://www.ncbi.nlm.nih.gov/pubmed/33577553 http://dx.doi.org/10.1371/journal.pcbi.1008138 |
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