Evidence map›Paper›PMID 24603429›Full record

ArticlePloS one2014

Bayesian integration of information in hippocampal place cells.

Tamas Madl, Stan Franklin, Ke Chen, Daniela Montaldi, Robert Trappl

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Tamas MadlSchool of Computer Science, University of Manchester, Manchester, United Kingdom; Austrian Research Institute for Artificial Intelligence, Vienna, Austria.
Stan FranklinInstitute for Intelligent Systems, University of Memphis, Memphis, Tennessee, United States of America.
Ke ChenSchool of Computer Science, University of Manchester, Manchester, United Kingdom.
Daniela MontaldiSchool of Psychological Sciences, University of Manchester, Manchester, United Kingdom.
Robert TrapplAustrian Research Institute for Artificial Intelligence, Vienna, Austria.

Funding

Austrian Science Fund FWF P 25380
6 · The paper itself

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.

Indexed as

AlgorithmsBayes TheoremModels, NeurologicalAction PotentialsAnimalsCuesHippocampusHumansNeuronsOrientationSpace Perception

Identifiers

PMID24603429
PMCPMC3945610

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.