On information metrics for spatial coding
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/jspui/handle/123456789/25083 |
Resumo: | The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. |
id |
UFRN_0564bcd229acbd4d0efa9b7fab746aae |
---|---|
oai_identifier_str |
oai:https://repositorio.ufrn.br:123456789/25083 |
network_acronym_str |
UFRN |
network_name_str |
Repositório Institucional da UFRN |
repository_id_str |
|
spelling |
Souza, Bryan C.Pavão, RodrigoBelchior, HindiaelTort, Adriano Bretanha Lopes2018-04-26T12:57:06Z2018-04-26T12:57:06Z2018-04-01Souza, B. C. et al. On information metrics for spatial coding. [s.l.], Neuroscience, v. 375, p. 62-73, abr./2018.https://repositorio.ufrn.br/jspui/handle/123456789/2508310.1016/j.neuroscience.2018.01.066engPlace cellPlace fieldSpatial codingInformationSpike train analysisHippocampusOn information metrics for spatial codinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleThe hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALAdrianoTort_ICe_2018_On information metrics.pdfAdrianoTort_ICe_2018_On information metrics.pdfAdrianoTort_ICe_2018_On information metricsapplication/pdf2646898https://repositorio.ufrn.br/bitstream/123456789/25083/1/AdrianoTort_ICe_2018_On%20information%20metrics.pdf239b7559e284b8c8f1e88500c9e3fb8dMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/25083/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52TEXTAdrianoTort_ICe_2018_On information metrics.pdf.txtAdrianoTort_ICe_2018_On information metrics.pdf.txtExtracted texttext/plain47054https://repositorio.ufrn.br/bitstream/123456789/25083/3/AdrianoTort_ICe_2018_On%20information%20metrics.pdf.txt9bc2a9a7f13e9b22afdcae0227a3fb14MD53THUMBNAILAdrianoTort_ICe_2018_On information metrics.pdf.jpgAdrianoTort_ICe_2018_On information metrics.pdf.jpgIM Thumbnailimage/jpeg12538https://repositorio.ufrn.br/bitstream/123456789/25083/4/AdrianoTort_ICe_2018_On%20information%20metrics.pdf.jpg561c89d32fba6437cc14d7ea69d2765cMD54123456789/250832021-07-08 10:52:04.623oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-07-08T13:52:04Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
On information metrics for spatial coding |
title |
On information metrics for spatial coding |
spellingShingle |
On information metrics for spatial coding Souza, Bryan C. Place cell Place field Spatial coding Information Spike train analysis Hippocampus |
title_short |
On information metrics for spatial coding |
title_full |
On information metrics for spatial coding |
title_fullStr |
On information metrics for spatial coding |
title_full_unstemmed |
On information metrics for spatial coding |
title_sort |
On information metrics for spatial coding |
author |
Souza, Bryan C. |
author_facet |
Souza, Bryan C. Pavão, Rodrigo Belchior, Hindiael Tort, Adriano Bretanha Lopes |
author_role |
author |
author2 |
Pavão, Rodrigo Belchior, Hindiael Tort, Adriano Bretanha Lopes |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Souza, Bryan C. Pavão, Rodrigo Belchior, Hindiael Tort, Adriano Bretanha Lopes |
dc.subject.por.fl_str_mv |
Place cell Place field Spatial coding Information Spike train analysis Hippocampus |
topic |
Place cell Place field Spatial coding Information Spike train analysis Hippocampus |
description |
The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-04-26T12:57:06Z |
dc.date.available.fl_str_mv |
2018-04-26T12:57:06Z |
dc.date.issued.fl_str_mv |
2018-04-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Souza, B. C. et al. On information metrics for spatial coding. [s.l.], Neuroscience, v. 375, p. 62-73, abr./2018. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/jspui/handle/123456789/25083 |
dc.identifier.doi.none.fl_str_mv |
10.1016/j.neuroscience.2018.01.066 |
identifier_str_mv |
Souza, B. C. et al. On information metrics for spatial coding. [s.l.], Neuroscience, v. 375, p. 62-73, abr./2018. 10.1016/j.neuroscience.2018.01.066 |
url |
https://repositorio.ufrn.br/jspui/handle/123456789/25083 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
instname_str |
Universidade Federal do Rio Grande do Norte (UFRN) |
instacron_str |
UFRN |
institution |
UFRN |
reponame_str |
Repositório Institucional da UFRN |
collection |
Repositório Institucional da UFRN |
bitstream.url.fl_str_mv |
https://repositorio.ufrn.br/bitstream/123456789/25083/1/AdrianoTort_ICe_2018_On%20information%20metrics.pdf https://repositorio.ufrn.br/bitstream/123456789/25083/2/license.txt https://repositorio.ufrn.br/bitstream/123456789/25083/3/AdrianoTort_ICe_2018_On%20information%20metrics.pdf.txt https://repositorio.ufrn.br/bitstream/123456789/25083/4/AdrianoTort_ICe_2018_On%20information%20metrics.pdf.jpg |
bitstream.checksum.fl_str_mv |
239b7559e284b8c8f1e88500c9e3fb8d 8a4605be74aa9ea9d79846c1fba20a33 9bc2a9a7f13e9b22afdcae0227a3fb14 561c89d32fba6437cc14d7ea69d2765c |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN) |
repository.mail.fl_str_mv |
|
_version_ |
1802117785779699712 |