Bayesian Integration of Information in Hippocampal Place Cells
Identifieur interne : 002D69 ( Ncbi/Checkpoint ); précédent : 002D68; suivant : 002D70Bayesian Integration of Information in Hippocampal Place Cells
Auteurs : Tamas Madl [Royaume-Uni, Autriche] ; Stan Franklin [États-Unis] ; Ke Chen [Royaume-Uni] ; Daniela Montaldi [Royaume-Uni] ; Robert Trappl [Autriche]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
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.
Url:
DOI: 10.1371/journal.pone.0089762
PubMed: 24603429
PubMed Central: 3945610
Affiliations:
- Autriche, Royaume-Uni, États-Unis
- Angleterre, Grand Manchester, Tennessee, Vienne (Autriche)
- Manchester, Vienne (Autriche)
- Université de Manchester
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PMC:3945610Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>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.</p>
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