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Bayesian Integration of Information in Hippocampal Place Cells
Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of informat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945610/ https://www.ncbi.nlm.nih.gov/pubmed/24603429 http://dx.doi.org/10.1371/journal.pone.0089762 |
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author | Madl, Tamas Franklin, Stan Chen, Ke Montaldi, Daniela Trappl, Robert |
author_facet | Madl, Tamas Franklin, Stan Chen, Ke Montaldi, Daniela Trappl, Robert |
author_sort | Madl, Tamas |
collection | PubMed |
description | Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We compare the predictions of our model with physiological data from rats. Our results suggest that useful predictions regarding the firing fields of place cells can be made based on a single underlying principle, Bayesian cue integration, and that such predictions are possible using a remarkably small number of model parameters. |
format | Online Article Text |
id | pubmed-3945610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39456102014-03-12 Bayesian Integration of Information in Hippocampal Place Cells Madl, Tamas Franklin, Stan Chen, Ke Montaldi, Daniela Trappl, Robert PLoS One Research Article Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We compare the predictions of our model with physiological data from rats. Our results suggest that useful predictions regarding the firing fields of place cells can be made based on a single underlying principle, Bayesian cue integration, and that such predictions are possible using a remarkably small number of model parameters. Public Library of Science 2014-03-06 /pmc/articles/PMC3945610/ /pubmed/24603429 http://dx.doi.org/10.1371/journal.pone.0089762 Text en © 2014 Madl 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Madl, Tamas Franklin, Stan Chen, Ke Montaldi, Daniela Trappl, Robert Bayesian Integration of Information in Hippocampal Place Cells |
title | Bayesian Integration of Information in Hippocampal Place Cells |
title_full | Bayesian Integration of Information in Hippocampal Place Cells |
title_fullStr | Bayesian Integration of Information in Hippocampal Place Cells |
title_full_unstemmed | Bayesian Integration of Information in Hippocampal Place Cells |
title_short | Bayesian Integration of Information in Hippocampal Place Cells |
title_sort | bayesian integration of information in hippocampal place cells |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945610/ https://www.ncbi.nlm.nih.gov/pubmed/24603429 http://dx.doi.org/10.1371/journal.pone.0089762 |
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