Upload resources

Upload your works to SEDICI to increase its visibility and improve its impact

 

Show simple item record

dc.date.accessioned 2023-11-16T16:20:02Z
dc.date.available 2023-11-16T16:20:02Z
dc.date.issued 2010
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/160253
dc.description.abstract Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains. en
dc.format.extent 713-727 es
dc.language en es
dc.subject Mutual information es
dc.subject Sampling bias es
dc.subject Population coding es
dc.subject Somatosensory cortex es
dc.title Information-theoretic methods for studying population codes en
dc.type Articulo es
sedici.identifier.other https://doi.org/10.1016/j.neunet.2010.05.008 es
sedici.identifier.issn 0893-6080 es
sedici.creator.person Ince, Robin A.A. es
sedici.creator.person Senatore, Riccardo es
sedici.creator.person Arabzadeh, Ehsan es
sedici.creator.person Montani, Fernando Fabián es
sedici.creator.person Diamond, Mathew E. es
sedici.creator.person Panzeri, Stefano es
sedici.subject.materias Física es
sedici.description.fulltext true es
mods.originInfo.place Instituto de Física La Plata es
mods.originInfo.place Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Neural Networks es
sedici.relation.journalVolumeAndIssue vol. 23, no. 6 es


Download Files

This item appears in the following Collection(s)

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)